Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: Pilot study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li Ping; Napel, Sandy; Acar, Burak

2004-10-01

Computed tomography colonography (CTC) is a minimally invasive method that allows the evaluation of the colon wall from CT sections of the abdomen/pelvis. The primary goal of CTC is to detect colonic polyps, precursors to colorectal cancer. Because imperfect cleansing and distension can cause portions of the colon wall to be collapsed, covered with water, and/or covered with retained stool, patients are scanned in both prone and supine positions. We believe that both reading efficiency and computer aided detection (CAD) of CTC images can be improved by accurate registration of data from the supine and prone positions. We developed amore » two-stage approach that first registers the colonic central paths using a heuristic and automated algorithm and then matches polyps or polyp candidates (CAD hits) by a statistical approach. We evaluated the registration algorithm on 24 patient cases. After path registration, the mean misalignment distance between prone and supine identical anatomic landmarks was reduced from 47.08 to 12.66 mm, a 73% improvement. The polyp registration algorithm was specifically evaluated using eight patient cases for which radiologists identified polyps separately for both supine and prone data sets, and then manually registered corresponding pairs. The algorithm correctly matched 78% of these pairs without user input. The algorithm was also applied to the 30 highest-scoring CAD hits in the prone and supine scans and showed a success rate of 50% in automatically registering corresponding polyp pairs. Finally, we computed the average number of CAD hits that need to be manually compared in order to find the correct matches among the top 30 CAD hits. With polyp registration, the average number of comparisons was 1.78 per polyp, as opposed to 4.28 comparisons without polyp registration.« less

Role of Computer Aided Diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography

PubMed Central

Prakashini, K; Babu, Satish; Rajgopal, KV; Kokila, K Raja

2016-01-01

Aims and Objectives: To determine the overall performance of an existing CAD algorithm with thin-section computed tomography (CT) in the detection of pulmonary nodules and to evaluate detection sensitivity at a varying range of nodule density, size, and location. Materials and Methods: A cross-sectional prospective study was conducted on 20 patients with 322 suspected nodules who underwent diagnostic chest imaging using 64-row multi-detector CT. The examinations were evaluated on reconstructed images of 1.4 mm thickness and 0.7 mm interval. Detection of pulmonary nodules, initially by a radiologist of 2 years experience (RAD) and later by CAD lung nodule software was assessed. Then, CAD nodule candidates were accepted or rejected accordingly. Detected nodules were classified based on their size, density, and location. The performance of the RAD and CAD system was compared with the gold standard that is true nodules confirmed by consensus of senior RAD and CAD together. The overall sensitivity and false-positive (FP) rate of CAD software was calculated. Observations and Results: Of the 322 suspected nodules, 221 were classified as true nodules on the consensus of senior RAD and CAD together. Of the true nodules, the RAD detected 206 (93.2%) and 202 (91.4%) by the CAD. CAD and RAD together picked up more number of nodules than either CAD or RAD alone. Overall sensitivity for nodule detection with the CAD program was 91.4%, and FP detection per patient was 5.5%. The CAD showed comparatively higher sensitivity for nodules of size 4–10 mm (93.4%) and nodules in hilar (100%) and central (96.5%) location when compared to RAD's performance. Conclusion: CAD performance was high in detecting pulmonary nodules including the small size and low-density nodules. CAD even with relatively high FP rate, assists and improves RAD's performance as a second reader, especially for nodules located in the central and hilar region and for small nodules by saving RADs time. PMID:27578931

Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography.

PubMed

Coy, Heidi; Young, Jonathan R; Douek, Michael L; Brown, Matthew S; Sayre, James; Raman, Steven S

2017-07-01

To evaluate the performance of a novel, quantitative computer-aided diagnostic (CAD) algorithm on four-phase multidetector computed tomography (MDCT) to detect peak lesion attenuation to enable differentiation of clear cell renal cell carcinoma (ccRCC) from chromophobe RCC (chRCC), papillary RCC (pRCC), oncocytoma, and fat-poor angiomyolipoma (fp-AML). We queried our clinical databases to obtain a cohort of histologically proven renal masses with preoperative MDCT with four phases [unenhanced (U), corticomedullary (CM), nephrographic (NP), and excretory (E)]. A whole lesion 3D contour was obtained in all four phases. The CAD algorithm determined a region of interest (ROI) of peak lesion attenuation within the 3D lesion contour. For comparison, a manual ROI was separately placed in the most enhancing portion of the lesion by visual inspection for a reference standard, and in uninvolved renal cortex. Relative lesion attenuation for both CAD and manual methods was obtained by normalizing the CAD peak lesion attenuation ROI (and the reference standard manually placed ROI) to uninvolved renal cortex with the formula [(peak lesion attenuation ROI - cortex ROI)/cortex ROI] × 100%. ROC analysis and area under the curve (AUC) were used to assess diagnostic performance. Bland-Altman analysis was used to compare peak ROI between CAD and manual method. The study cohort comprised 200 patients with 200 unique renal masses: 106 (53%) ccRCC, 32 (16%) oncocytomas, 18 (9%) chRCCs, 34 (17%) pRCCs, and 10 (5%) fp-AMLs. In the CM phase, CAD-derived ROI enabled characterization of ccRCC from chRCC, pRCC, oncocytoma, and fp-AML with AUCs of 0.850 (95% CI 0.732-0.968), 0.959 (95% CI 0.930-0.989), 0.792 (95% CI 0.716-0.869), and 0.825 (95% CI 0.703-0.948), respectively. On Bland-Altman analysis, there was excellent agreement of CAD and manual methods with mean differences between 14 and 26 HU in each phase. A novel, quantitative CAD algorithm enabled robust peak HU lesion detection and discrimination of ccRCC from other renal lesions with similar performance compared to the manual method.

The design and integration of retinal CAD-SR to diabetes patient ePR system

NASA Astrophysics Data System (ADS)

Wu, Huiqun; Wei, Yufang; Liu, Brent J.; Shang, Yujuan; Shi, Lili; Jiang, Kui; Dong, Jiancheng

2017-03-01

Diabetic retinopathy (DR) is one of the serious complications of diabetes that could lead to blindness. Digital fundus camera is often used to detect retinal changes but the diagnosis relies too much on ophthalmologist's experience. Based on our previously developed algorithms for quantifying retinal vessels and lesions, we developed a computer aided detection-structured report (CAD-SR) template and implemented it into picture archiving and communication system (PACS). Furthermore, we mapped our CAD-SR into HL7 CDA to integrate CAD findings into diabetes patient electronic patient record (ePR) system. Such integration could provide more quantitative features from fundus image into ePR system, which is valuable for further data mining researches.

Computer-aided detection of pulmonary embolism at CT pulmonary angiography: can it improve performance of inexperienced readers?

PubMed

Blackmon, Kevin N; Florin, Charles; Bogoni, Luca; McCain, Joshua W; Koonce, James D; Lee, Heon; Bastarrika, Gorka; Thilo, Christian; Costello, Philip; Salganicoff, Marcos; Joseph Schoepf, U

2011-06-01

To evaluate the effect of a computer-aided detection (CAD) algorithm on the performance of novice readers for detection of pulmonary embolism (PE) at CT pulmonary angiography (CTPA). We included CTPA examinations of 79 patients (50 female, 52 ± 18 years). Studies were evaluated by two independent inexperienced readers who marked all vessels containing PE. After 3 months all studies were reevaluated by the same two readers, this time aided by CAD prototype. A consensus read by three expert radiologists served as the reference standard. Statistical analysis used χ(2) and McNemar testing. Expert consensus revealed 119 PEs in 32 studies. For PE detection, the sensitivity of CAD alone was 78%. Inexperienced readers' initial interpretations had an average per-PE sensitivity of 50%, which improved to 71% (p < 0.001) with CAD as a second reader. False positives increased from 0.18 to 0.25 per study (p = 0.03). Per-study, the readers initially detected 27/32 positive studies (84%); with CAD this number increased to 29.5 studies (92%; p = 0.125). Our results suggest that CAD significantly improves the sensitivity of PE detection for inexperienced readers with a small but appreciable increase in the rate of false positives.

Combining contour detection algorithms for the automatic extraction of the preparation line from a dental 3D measurement

NASA Astrophysics Data System (ADS)

Ahlers, Volker; Weigl, Paul; Schachtzabel, Hartmut

2005-04-01

Due to the increasing demand for high-quality ceramic crowns and bridges, the CAD/CAM-based production of dental restorations has been a subject of intensive research during the last fifteen years. A prerequisite for the efficient processing of the 3D measurement of prepared teeth with a minimal amount of user interaction is the automatic determination of the preparation line, which defines the sealing margin between the restoration and the prepared tooth. Current dental CAD/CAM systems mostly require the interactive definition of the preparation line by the user, at least by means of giving a number of start points. Previous approaches to the automatic extraction of the preparation line rely on single contour detection algorithms. In contrast, we use a combination of different contour detection algorithms to find several independent potential preparation lines from a height profile of the measured data. The different algorithms (gradient-based, contour-based, and region-based) show their strengths and weaknesses in different clinical situations. A classifier consisting of three stages (range check, decision tree, support vector machine), which is trained by human experts with real-world data, finally decides which is the correct preparation line. In a test with 101 clinical preparations, a success rate of 92.0% has been achieved. Thus the combination of different contour detection algorithms yields a reliable method for the automatic extraction of the preparation line, which enables the setup of a turn-key dental CAD/CAM process chain with a minimal amount of interactive screen work.

A survey on computer aided diagnosis for ocular diseases

PubMed Central

2014-01-01

Background Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients. Method We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed. Result We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively. Conclusion While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough. PMID:25175552

Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography.

PubMed

Nakao, Takahiro; Hanaoka, Shouhei; Nomura, Yukihiro; Sato, Issei; Nemoto, Mitsutaka; Miki, Soichiro; Maeda, Eriko; Yoshikawa, Takeharu; Hayashi, Naoto; Abe, Osamu

2018-04-01

The usefulness of computer-assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebral aneurysms. To develop a CAD system for intracranial aneurysms on unenhanced magnetic resonance angiography (MRA) images based on a deep convolutional neural network (CNN) and a maximum intensity projection (MIP) algorithm, and to demonstrate the usefulness of the system by training and evaluating it using a large dataset. Retrospective study. There were 450 cases with intracranial aneurysms. The diagnoses of brain aneurysms were made on the basis of MRA, which was performed as part of a brain screening program. Noncontrast-enhanced 3D time-of-flight (TOF) MRA on 3T MR scanners. In our CAD, we used a CNN classifier that predicts whether each voxel is inside or outside aneurysms by inputting MIP images generated from a volume of interest (VOI) around the voxel. The CNN was trained in advance using manually inputted labels. We evaluated our method using 450 cases with intracranial aneurysms, 300 of which were used for training, 50 for parameter tuning, and 100 for the final evaluation. Free-response receiver operating characteristic (FROC) analysis. Our CAD system detected 94.2% (98/104) of aneurysms with 2.9 false positives per case (FPs/case). At a sensitivity of 70%, the number of FPs/case was 0.26. We showed that the combination of a CNN and an MIP algorithm is useful for the detection of intracranial aneurysms. 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:948-953. © 2017 International Society for Magnetic Resonance in Medicine.

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study.

PubMed

Sunwoo, Leonard; Kim, Young Jae; Choi, Seung Hong; Kim, Kwang-Gi; Kang, Ji Hee; Kang, Yeonah; Bae, Yun Jung; Yoo, Roh-Eul; Kim, Jihang; Lee, Kyong Joon; Lee, Seung Hyun; Choi, Byung Se; Jung, Cheolkyu; Sohn, Chul-Ho; Kim, Jae Hyoung

2017-01-01

To assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists' diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard. The institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy. The sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time). CAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.

Diagnostic performance of an acoustic-based system for coronary artery disease risk stratification.

PubMed

Winther, Simon; Nissen, Louise; Schmidt, Samuel Emil; Westra, Jelmer Sybren; Rasmussen, Laust Dupont; Knudsen, Lars Lyhne; Madsen, Lene Helleskov; Kirk Johansen, Jane; Larsen, Bjarke Skogstad; Struijk, Johannes Jan; Frost, Lars; Holm, Niels Ramsing; Christiansen, Evald Høj; Botker, Hans Erik; Bøttcher, Morten

2018-06-01

Diagnosing coronary artery disease (CAD) continues to require substantial healthcare resources. Acoustic analysis of transcutaneous heart sounds of cardiac movement and intracoronary turbulence due to obstructive coronary disease could potentially change this. The aim of this study was thus to test the diagnostic accuracy of a new portable acoustic device for detection of CAD. We included 1675 patients consecutively with low to intermediate likelihood of CAD who had been referred for cardiac CT angiography. If significant obstruction was suspected in any coronary segment, patients were referred to invasive angiography and fractional flow reserve (FFR) assessment. Heart sound analysis was performed in all patients. A predefined acoustic CAD-score algorithm was evaluated; subsequently, we developed and validated an updated CAD-score algorithm that included both acoustic features and clinical risk factors. Low risk is indicated by a CAD-score value ≤20. Haemodynamically significant CAD assessed from FFR was present in 145 (10.0%) patients. In the entire cohort, the predefined CAD-score had a sensitivity of 63% and a specificity of 44%. In total, 50% had an updated CAD-score value ≤20. At this cut-off, sensitivity was 81% (95% CI 73% to 87%), specificity 53% (95% CI 50% to 56%), positive predictive value 16% (95% CI 13% to 18%) and negative predictive value 96% (95% CI 95% to 98%) for diagnosing haemodynamically significant CAD. Sound-based detection of CAD enables risk stratification superior to clinical risk scores. With a negative predictive value of 96%, this new acoustic rule-out system could potentially supplement clinical assessment to guide decisions on the need for further diagnostic investigation. ClinicalTrials.gov identifier NCT02264717; Results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Histopathological Image Analysis: A Review

PubMed Central

Gurcan, Metin N.; Boucheron, Laura; Can, Ali; Madabhushi, Anant; Rajpoot, Nasir; Yener, Bulent

2010-01-01

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement to the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe. PMID:20671804

A multistage approach to improve performance of computer-aided detection of pulmonary embolisms depicted on CT images: preliminary investigation.

PubMed

Park, Sang Cheol; Chapman, Brian E; Zheng, Bin

2011-06-01

This study developed a computer-aided detection (CAD) scheme for pulmonary embolism (PE) detection and investigated several approaches to improve CAD performance. In the study, 20 computed tomography examinations with various lung diseases were selected, which include 44 verified PE lesions. The proposed CAD scheme consists of five basic steps: 1) lung segmentation; 2) PE candidate extraction using an intensity mask and tobogganing region growing; 3) PE candidate feature extraction; 4) false-positive (FP) reduction using an artificial neural network (ANN); and 5) a multifeature-based k-nearest neighbor for positive/negative classification. In this study, we also investigated the following additional methods to improve CAD performance: 1) grouping 2-D detected features into a single 3-D object; 2) selecting features with a genetic algorithm (GA); and 3) limiting the number of allowed suspicious lesions to be cued in one examination. The results showed that 1) CAD scheme using tobogganing, an ANN, and grouping method achieved the maximum detection sensitivity of 79.2%; 2) the maximum scoring method achieved the superior performance over other scoring fusion methods; 3) GA was able to delete "redundant" features and further improve CAD performance; and 4) limiting the maximum number of cued lesions in an examination reduced FP rate by 5.3 times. Combining these approaches, CAD scheme achieved 63.2% detection sensitivity with 18.4 FP lesions per examination. The study suggested that performance of CAD schemes for PE detection depends on many factors that include 1) optimizing the 2-D region grouping and scoring methods; 2) selecting the optimal feature set; and 3) limiting the number of allowed cueing lesions per examination.

A CAD System for Hemorrhagic Stroke.

PubMed

Nowinski, Wieslaw L; Qian, Guoyu; Hanley, Daniel F

2014-09-01

Computer-aided detection/diagnosis (CAD) is a key component of routine clinical practice, increasingly used for detection, interpretation, quantification and decision support. Despite a critical need, there is no clinically accepted CAD system for stroke yet. Here we introduce a CAD system for hemorrhagic stroke. This CAD system segments, quantifies, and displays hematoma in 2D/3D, and supports evacuation of hemorrhage by thrombolytic treatment monitoring progression and quantifying clot removal. It supports seven-step workflow: select patient, add a new study, process patient's scans, show segmentation results, plot hematoma volumes, show 3D synchronized time series hematomas, and generate report. The system architecture contains four components: library, tools, application with user interface, and hematoma segmentation algorithm. The tools include a contour editor, 3D surface modeler, 3D volume measure, histogramming, hematoma volume plot, and 3D synchronized time-series hematoma display. The CAD system has been designed and implemented in C++. It has also been employed in the CLEAR and MISTIE phase-III, multicenter clinical trials. This stroke CAD system is potentially useful in research and clinical applications, particularly for clinical trials.

Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential

PubMed Central

Doi, Kunio

2007-01-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a “second opinion” and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists’ accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists. PMID:17349778

ProperCAD: A portable object-oriented parallel environment for VLSI CAD

NASA Technical Reports Server (NTRS)

Ramkumar, Balkrishna; Banerjee, Prithviraj

1993-01-01

Most parallel algorithms for VLSI CAD proposed to date have one important drawback: they work efficiently only on machines that they were designed for. As a result, algorithms designed to date are dependent on the architecture for which they are developed and do not port easily to other parallel architectures. A new project under way to address this problem is described. A Portable object-oriented parallel environment for CAD algorithms (ProperCAD) is being developed. The objectives of this research are (1) to develop new parallel algorithms that run in a portable object-oriented environment (CAD algorithms using a general purpose platform for portable parallel programming called CARM is being developed and a C++ environment that is truly object-oriented and specialized for CAD applications is also being developed); and (2) to design the parallel algorithms around a good sequential algorithm with a well-defined parallel-sequential interface (permitting the parallel algorithm to benefit from future developments in sequential algorithms). One CAD application that has been implemented as part of the ProperCAD project, flat VLSI circuit extraction, is described. The algorithm, its implementation, and its performance on a range of parallel machines are discussed in detail. It currently runs on an Encore Multimax, a Sequent Symmetry, Intel iPSC/2 and i860 hypercubes, a NCUBE 2 hypercube, and a network of Sun Sparc workstations. Performance data for other applications that were developed are provided: namely test pattern generation for sequential circuits, parallel logic synthesis, and standard cell placement.

Assessing operating characteristics of CAD algorithms in the absence of a gold standard

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roy Choudhury, Kingshuk; Paik, David S.; Yi, Chin A.

2010-04-15

Purpose: The authors examine potential bias when using a reference reader panel as ''gold standard'' for estimating operating characteristics of CAD algorithms for detecting lesions. As an alternative, the authors propose latent class analysis (LCA), which does not require an external gold standard to evaluate diagnostic accuracy. Methods: A binomial model for multiple reader detections using different diagnostic protocols was constructed, assuming conditional independence of readings given true lesion status. Operating characteristics of all protocols were estimated by maximum likelihood LCA. Reader panel and LCA based estimates were compared using data simulated from the binomial model for a range ofmore » operating characteristics. LCA was applied to 36 thin section thoracic computed tomography data sets from the Lung Image Database Consortium (LIDC): Free search markings of four radiologists were compared to markings from four different CAD assisted radiologists. For real data, bootstrap-based resampling methods, which accommodate dependence in reader detections, are proposed to test of hypotheses of differences between detection protocols. Results: In simulation studies, reader panel based sensitivity estimates had an average relative bias (ARB) of -23% to -27%, significantly higher (p-value <0.0001) than LCA (ARB -2% to -6%). Specificity was well estimated by both reader panel (ARB -0.6% to -0.5%) and LCA (ARB 1.4%-0.5%). Among 1145 lesion candidates LIDC considered, LCA estimated sensitivity of reference readers (55%) was significantly lower (p-value 0.006) than CAD assisted readers' (68%). Average false positives per patient for reference readers (0.95) was not significantly lower (p-value 0.28) than CAD assisted readers' (1.27). Conclusions: Whereas a gold standard based on a consensus of readers may substantially bias sensitivity estimates, LCA may be a significantly more accurate and consistent means for evaluating diagnostic accuracy.« less

Performance evaluation for volumetric segmentation of multiple sclerosis lesions using MATLAB and computing engine in the graphical processing unit (GPU)

NASA Astrophysics Data System (ADS)

Le, Anh H.; Park, Young W.; Ma, Kevin; Jacobs, Colin; Liu, Brent J.

2010-03-01

Multiple Sclerosis (MS) is a progressive neurological disease affecting myelin pathways in the brain. Multiple lesions in the white matter can cause paralysis and severe motor disabilities of the affected patient. To solve the issue of inconsistency and user-dependency in manual lesion measurement of MRI, we have proposed a 3-D automated lesion quantification algorithm to enable objective and efficient lesion volume tracking. The computer-aided detection (CAD) of MS, written in MATLAB, utilizes K-Nearest Neighbors (KNN) method to compute the probability of lesions on a per-voxel basis. Despite the highly optimized algorithm of imaging processing that is used in CAD development, MS CAD integration and evaluation in clinical workflow is technically challenging due to the requirement of high computation rates and memory bandwidth in the recursive nature of the algorithm. In this paper, we present the development and evaluation of using a computing engine in the graphical processing unit (GPU) with MATLAB for segmentation of MS lesions. The paper investigates the utilization of a high-end GPU for parallel computing of KNN in the MATLAB environment to improve algorithm performance. The integration is accomplished using NVIDIA's CUDA developmental toolkit for MATLAB. The results of this study will validate the practicality and effectiveness of the prototype MS CAD in a clinical setting. The GPU method may allow MS CAD to rapidly integrate in an electronic patient record or any disease-centric health care system.

Crowdsourcing lung nodules detection and annotation

NASA Astrophysics Data System (ADS)

Boorboor, Saeed; Nadeem, Saad; Park, Ji Hwan; Baker, Kevin; Kaufman, Arie

2018-03-01

We present crowdsourcing as an additional modality to aid radiologists in the diagnosis of lung cancer from clinical chest computed tomography (CT) scans. More specifically, a complete work flow is introduced which can help maximize the sensitivity of lung nodule detection by utilizing the collective intelligence of the crowd. We combine the concept of overlapping thin-slab maximum intensity projections (TS-MIPs) and cine viewing to render short videos that can be outsourced as an annotation task to the crowd. These videos are generated by linearly interpolating overlapping TS-MIPs of CT slices through the depth of each quadrant of a patient's lung. The resultant videos are outsourced to an online community of non-expert users who, after a brief tutorial, annotate suspected nodules in these video segments. Using our crowdsourcing work flow, we achieved a lung nodule detection sensitivity of over 90% for 20 patient CT datasets (containing 178 lung nodules with sizes between 1-30mm), and only 47 false positives from a total of 1021 annotations on nodules of all sizes (96% sensitivity for nodules>4mm). These results show that crowdsourcing can be a robust and scalable modality to aid radiologists in screening for lung cancer, directly or in combination with computer-aided detection (CAD) algorithms. For CAD algorithms, the presented work flow can provide highly accurate training data to overcome the high false-positive rate (per scan) problem. We also provide, for the first time, analysis on nodule size and position which can help improve CAD algorithms.

Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts.

PubMed

Liao, Katherine P; Ananthakrishnan, Ashwin N; Kumar, Vishesh; Xia, Zongqi; Cagan, Andrew; Gainer, Vivian S; Goryachev, Sergey; Chen, Pei; Savova, Guergana K; Agniel, Denis; Churchill, Susanne; Lee, Jaeyoung; Murphy, Shawn N; Plenge, Robert M; Szolovits, Peter; Kohane, Isaac; Shaw, Stanley Y; Karlson, Elizabeth W; Cai, Tianxi

2015-01-01

Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study. We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p<0.0001) after adjusting for traditional cardiovascular risk factors. We developed and validated a CAD algorithm that performed well across diverse patient populations. The addition of NLP into the CAD algorithm improved the sensitivity of the algorithm, particularly in cohorts where the prevalence of CAD was low. Preliminary data suggest that CAD risk was significantly lower in RA and IBD compared to DM.

Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts

PubMed Central

Liao, Katherine P.; Ananthakrishnan, Ashwin N.; Kumar, Vishesh; Xia, Zongqi; Cagan, Andrew; Gainer, Vivian S.; Goryachev, Sergey; Chen, Pei; Savova, Guergana K.; Agniel, Denis; Churchill, Susanne; Lee, Jaeyoung; Murphy, Shawn N.; Plenge, Robert M.; Szolovits, Peter; Kohane, Isaac; Shaw, Stanley Y.; Karlson, Elizabeth W.; Cai, Tianxi

2015-01-01

Background Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study. Methods and Results We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p<0.0001) after adjusting for traditional cardiovascular risk factors. Conclusions We developed and validated a CAD algorithm that performed well across diverse patient populations. The addition of NLP into the CAD algorithm improved the sensitivity of the algorithm, particularly in cohorts where the prevalence of CAD was low. Preliminary data suggest that CAD risk was significantly lower in RA and IBD compared to DM. PMID:26301417

The MAGIC-5 CAD for nodule detection in low dose and thin slice lung CTs

NASA Astrophysics Data System (ADS)

Cerello, Piergiorgio; MAGIC-5 Collaboration

2010-11-01

Lung cancer is the leading cause of cancer-related mortality in developed countries. Only 10-15% of all men and women diagnosed with lung cancer live 5 years after the diagnosis. However, the 5-year survival rate for patients diagnosed in the early asymptomatic stage of the disease can reach 70%. Early-stage lung cancers can be diagnosed by detecting non-calcified small pulmonary nodules with computed tomography (CT). Computer-aided detection (CAD) could support radiologists in the analysis of the large amount of noisy images generated in screening programs, where low-dose and thin-slice settings are used. The MAGIC-5 project, funded by the Istituto Nazionale di Fisica Nucleare (INFN, Italy) and Ministero dell'Università e della Ricerca (MUR, Italy), developed a multi-method approach based on three CAD algorithms to be used in parallel with a merging of their results: the Channeler Ant Model (CAM), based on Virtual Ant Colonies, the Dot-Enhancement/Pleura Surface Normals/VBNA (DE-PSN-VBNA), and the Region Growing Volume Plateau (RGVP). Preliminary results show quite good performances, to be improved with the refining of the single algorithm and the added value of the results merging.

Reconstruction software of the silicon tracker of DAMPE mission

NASA Astrophysics Data System (ADS)

Tykhonov, A.; Gallo, V.; Wu, X.; Zimmer, S.

2017-10-01

DAMPE is a satellite-borne experiment aimed to probe astroparticle physics in the GeV-TeV energy range. The Silicon tracker (STK) is one of the key components of DAMPE, which allows the reconstruction of trajectories (tracks) of detected particles. The non-negligible amount of material in the tracker poses a challenge to its reconstruction and alignment. In this paper we describe methods to address this challenge. We present the track reconstruction algorithm and give insight into the alignment algorithm. We also present our CAD-to-GDML converter, an in-house tool for implementing detector geometry in the software from the CAD drawings of the detector.

High-performance computer aided detection system for polyp detection in CT colonography with fluid and fecal tagging

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Wang, Shijun; Kabadi, Suraj; Summers, Ronald M.

2009-02-01

CT colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. Computer-aided detection (CAD) of polyps has improved consistency and sensitivity of virtual colonoscopy interpretation and reduced interpretation burden. A CAD system typically consists of four stages: (1) image preprocessing including colon segmentation; (2) initial detection generation; (3) feature selection; and (4) detection classification. In our experience, three existing problems limit the performance of our current CAD system. First, highdensity orally administered contrast agents in fecal-tagging CTC have scatter effects on neighboring tissues. The scattering manifests itself as an artificial elevation in the observed CT attenuation values of the neighboring tissues. This pseudo-enhancement phenomenon presents a problem for the application of computer-aided polyp detection, especially when polyps are submerged in the contrast agents. Second, general kernel approach for surface curvature computation in the second stage of our CAD system could yield erroneous results for thin structures such as small (6-9 mm) polyps and for touching structures such as polyps that lie on haustral folds. Those erroneous curvatures will reduce the sensitivity of polyp detection. The third problem is that more than 150 features are selected from each polyp candidate in the third stage of our CAD system. These high dimensional features make it difficult to learn a good decision boundary for detection classification and reduce the accuracy of predictions. Therefore, an improved CAD system for polyp detection in CTC data is proposed by introducing three new techniques. First, a scale-based scatter correction algorithm is applied to reduce pseudo-enhancement effects in the image pre-processing stage. Second, a cubic spline interpolation method is utilized to accurately estimate curvatures for initial detection generation. Third, a new dimensionality reduction classifier, diffusion map and local linear embedding (DMLLE), is developed for classification and false positives (FP) reduction. Performance of the improved CAD system is evaluated and compared with our existing CAD system (without applying those techniques) using CT scans of 1186 patients. These scans are divided into a training set and a test set. The sensitivity of the improved CAD system increased 18% on training data at a rate of 5 FPs per patient and 15% on test data at a rate of 5 FPs per patient. Our results indicated that the improved CAD system achieved significantly better performance on medium-sized colonic adenomas with higher sensitivity and lower FP rate in CTC.

WE-E-217A-02: Methodologies for Evaluation of Standalone CAD System Performance.

PubMed

Sahiner, B

2012-06-01

Standalone performance evaluation of a CAD system provides information about the abnormality detection or classification performance of the computerized system alone. Although the performance of the reader with CAD is the final step in CAD system assessment, standalone performance evaluation is an important component for several reasons: First, standalone evaluation informs the reader about the performance level of the CAD system and may have an impact on how the reader uses the system. Second, it provides essential information to the system designer for algorithm optimization during system development. Third, standalone evaluation can provide a detailed description of algorithm performance (e.g., on subgroups of the population) because a larger data set with more samples from different subgroups can be included in standalone studies compared to reader studies. Proper standalone evaluation of a CAD system involves a number of key components, some of which are shared with the assessment of reader performance with CAD. These include (1) selection of a test data set that allows performance assessment with little or no bias and acceptable uncertainty; (2) a reference standard that indicates disease status as well as the location and extent of disease; (3) a clearly defined method for labeling each CAD mark as a true-positive or false-positive; and (4) a properly selected set of metrics to summarize the accuracy of the computer marks and their corresponding scores. In this lecture, we will discuss various approaches for the key components of standalone CAD performance evaluation listed above, and present some of the recommendations and opinions from the AAPM CAD subcommittee on these issues. Learning Objectives 1. Identify basic components and metrics in the assessment of standalone CAD systems 2. Understand how each component may affect the assessed performance 3. Learn about AAPM CAD subcommittee's opinions and recommendations on factors and metrics related to the evaluation of standalone CAD system performance. © 2012 American Association of Physicists in Medicine.

Automated target classification in high resolution dual frequency sonar imagery

NASA Astrophysics Data System (ADS)

Aridgides, Tom; Fernández, Manuel

2007-04-01

An improved computer-aided-detection / computer-aided-classification (CAD/CAC) processing string has been developed. The classified objects of 2 distinct strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new high-resolution dual frequency sonar imagery. Three significant fusion algorithm improvements were made. First, a nonlinear 2nd order (Volterra) feature LLRT fusion algorithm was developed. Second, a Box-Cox nonlinear feature LLRT fusion algorithm was developed. The Box-Cox transformation consists of raising the features to a to-be-determined power. Third, a repeated application of a subset feature selection / feature orthogonalization / Volterra feature LLRT fusion block was utilized. It was shown that cascaded Volterra feature LLRT fusion of the CAD/CAC processing strings outperforms summing, baseline single-stage Volterra and Box-Cox feature LLRT algorithms, yielding significant improvements over the best single CAD/CAC processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate. Additionally, the robustness of cascaded Volterra feature fusion was demonstrated, by showing that the algorithm yields similar performance with the training and test sets.

Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches

NASA Astrophysics Data System (ADS)

Fotin, Sergei V.; Yin, Yin; Haldankar, Hrishikesh; Hoffmeister, Jeffrey W.; Periaswamy, Senthil

2016-03-01

Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 +/- 0:040 to 0:893 +/- 0:033 for suspicious ROIs; and from 0:852 +/- 0:065 to 0:930 +/- 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.

Hole Feature on Conical Face Recognition for Turning Part Model

NASA Astrophysics Data System (ADS)

Zubair, A. F.; Abu Mansor, M. S.

2018-03-01

Computer Aided Process Planning (CAPP) is the bridge between CAD and CAM and pre-processing of the CAD data in the CAPP system is essential. For CNC turning part, conical faces of part model is inevitable to be recognised beside cylindrical and planar faces. As the sinus cosines of the cone radius structure differ according to different models, face identification in automatic feature recognition of the part model need special intention. This paper intends to focus hole on feature on conical faces that can be detected by CAD solid modeller ACIS via. SAT file. Detection algorithm of face topology were generated and compared. The study shows different faces setup for similar conical part models with different hole type features. Three types of holes were compared and different between merge faces and unmerge faces were studied.

Optimizing computer-aided colonic polyp detection for CT colonography by evolving the Pareto front1

PubMed Central

Li, Jiang; Huang, Adam; Yao, Jack; Liu, Jiamin; Van Uitert, Robert L.; Petrick, Nicholas; Summers, Ronald M.

2009-01-01

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6–9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p=7.78×10−5) for the size category of 6–9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p=7.95×10−5) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95%CI [0.75%, 16%], p=0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists. PMID:19235388

A ℓ2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Zhang, Jian; Li, Wei; Zhao, Dazhe; Huang, Min; Zaiane, Osmar

2017-03-01

The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD). In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a ℓ 2, 1 norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth ℓ 2, 1 regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the ℓ 2, 1 norm of kernel weights, and use an accelerated method based on FISTA; the second one employs an iterative scheme based on an approximate gradient descent method. The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the ℓ 2, 1 norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods. The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed ℓ 2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Anniversary Paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM

PubMed Central

Giger, Maryellen L.; Chan, Heang-Ping; Boone, John

2008-01-01

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists’ goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists—as opposed to a completely automatic computer interpretation—focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous—from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects—collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more—from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis. PMID:19175137

Anniversary Paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giger, Maryellen L.; Chan, Heang-Ping; Boone, John

2008-12-15

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities thatmore » are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists--as opposed to a completely automatic computer interpretation--focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous--from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects--collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more--from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.« less

A hybrid lung and vessel segmentation algorithm for computer aided detection of pulmonary embolism

NASA Astrophysics Data System (ADS)

Raghupathi, Laks; Lakare, Sarang

2009-02-01

Advances in multi-detector technology have made CT pulmonary angiography (CTPA) a popular radiological tool for pulmonary emboli (PE) detection. CTPA provide rich detail of lung anatomy and is a useful diagnostic aid in highlighting even very small PE. However analyzing hundreds of slices is laborious and time-consuming for the practicing radiologist which may also cause misdiagnosis due to the presence of various PE look-alike. Computer-aided diagnosis (CAD) can be a potential second reader in providing key diagnostic information. Since PE occurs only in vessel arteries, it is important to mark this region of interest (ROI) during CAD preprocessing. In this paper, we present a new lung and vessel segmentation algorithm for extracting contrast-enhanced vessel ROI in CTPA. Existing approaches to segmentation either provide only the larger lung area without highlighting the vessels or is computationally prohibitive. In this paper, we propose a hybrid lung and vessel segmentation which uses an initial lung ROI and determines the vessels through a series of refinement steps. We first identify a coarse vessel ROI by finding the "holes" from the lung ROI. We then use the initial ROI as seed-points for a region-growing process while carefully excluding regions which are not relevant. The vessel segmentation mask covers 99% of the 259 PE from a real-world set of 107 CTPA. Further, our algorithm increases the net sensitivity of a prototype CAD system by 5-9% across all PE categories in the training and validation data sets. The average run-time of algorithm was only 100 seconds on a standard workstation.

Initial experience with computer aided detection for microcalcification in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Harkness, E. F.; Lim, Y. Y.; Wilson, M. W.; Haq, R.; Zhou, J.; Tate, C.; Maxwell, A. J.; Astley, S. M.; Gilbert, F. J.

2015-03-01

Digital breast tomosynthesis (DBT) addresses limitations of 2-D projection imaging for detection of masses. Microcalcification clusters may be more difficult to appreciate in DBT as individual calcifications within clusters may appear on different slices. This research aims to evaluate the performance of ImageChecker 3D Calc CAD v1.0. Women were recruited as part of the TOMMY trial. From the trial, 169 were included in this study. The DBT images were processed with the computer aided detection (CAD) algorithm. Three consultant radiologists reviewed the images and recorded whether CAD prompts were on or off target. 79/80 (98.8%) malignant cases had a prompt on the area of microcalcification. In these cases, there were 1-15 marks (median 5) with the majority of false prompts (n=326/431) due to benign (68%) and vascular (24%) calcifications. Of 89 normal/benign cases, there were 1-13 prompts (median 3), 27 (30%) had no prompts and the majority of false prompts (n=238) were benign (77%) calcifications. CAD is effective in prompting malignant microcalcification clusters and may overcome the difficulty of detecting clusters in slice images. Although there was a high rate of false prompts, further advances in the software may improve specificity.

Neural networks: Application to medical imaging

NASA Technical Reports Server (NTRS)

Clarke, Laurence P.

1994-01-01

The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

Automatic detection of multi-level acetowhite regions in RGB color images of the uterine cervix

NASA Astrophysics Data System (ADS)

Lange, Holger

2005-04-01

Uterine cervical cancer is the second most common cancer among women worldwide. Colposcopy is a diagnostic method used to detect cancer precursors and cancer of the uterine cervix, whereby a physician (colposcopist) visually inspects the metaplastic epithelium on the cervix for certain distinctly abnormal morphologic features. A contrast agent, a 3-5% acetic acid solution, is used, causing abnormal and metaplastic epithelia to turn white. The colposcopist considers diagnostic features such as the acetowhite, blood vessel structure, and lesion margin to derive a clinical diagnosis. STI Medical Systems is developing a Computer-Aided-Diagnosis (CAD) system for colposcopy -- ColpoCAD, a complex image analysis system that at its core assesses the same visual features as used by colposcopists. The acetowhite feature has been identified as one of the most important individual predictors of lesion severity. Here, we present the details and preliminary results of a multi-level acetowhite region detection algorithm for RGB color images of the cervix, including the detection of the anatomic features: cervix, os and columnar region, which are used for the acetowhite region detection. The RGB images are assumed to be glare free, either obtained by cross-polarized image acquisition or glare removal pre-processing. The basic approach of the algorithm is to extract a feature image from the RGB image that provides a good acetowhite to cervix background ratio, to segment the feature image using novel pixel grouping and multi-stage region-growing algorithms that provide region segmentations with different levels of detail, to extract the acetowhite regions from the region segmentations using a novel region selection algorithm, and then finally to extract the multi-levels from the acetowhite regions using multiple thresholds. The performance of the algorithm is demonstrated using human subject data.

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

PubMed Central

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

2017-01-01

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography (FFDM) images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a “scoring fusion” artificial neural network (ANN) classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793±0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions. PMID:27997380

Diagnostic Performance of a Novel Coronary CT Angiography Algorithm: Prospective Multicenter Validation of an Intracycle CT Motion Correction Algorithm for Diagnostic Accuracy.

PubMed

Andreini, Daniele; Lin, Fay Y; Rizvi, Asim; Cho, Iksung; Heo, Ran; Pontone, Gianluca; Bartorelli, Antonio L; Mushtaq, Saima; Villines, Todd C; Carrascosa, Patricia; Choi, Byoung Wook; Bloom, Stephen; Wei, Han; Xing, Yan; Gebow, Dan; Gransar, Heidi; Chang, Hyuk-Jae; Leipsic, Jonathon; Min, James K

2018-06-01

Motion artifact can reduce the diagnostic accuracy of coronary CT angiography (CCTA) for coronary artery disease (CAD). The purpose of this study was to compare the diagnostic performance of an algorithm dedicated to correcting coronary motion artifact with the performance of standard reconstruction methods in a prospective international multicenter study. Patients referred for clinically indicated invasive coronary angiography (ICA) for suspected CAD prospectively underwent an investigational CCTA examination free from heart rate-lowering medications before they underwent ICA. Blinded core laboratory interpretations of motion-corrected and standard reconstructions for obstructive CAD (≥ 50% stenosis) were compared with ICA findings. Segments unevaluable owing to artifact were considered obstructive. The primary endpoint was per-subject diagnostic accuracy of the intracycle motion correction algorithm for obstructive CAD found at ICA. Among 230 patients who underwent CCTA with the motion correction algorithm and standard reconstruction, 92 (40.0%) had obstructive CAD on the basis of ICA findings. At a mean heart rate of 68.0 ± 11.7 beats/min, the motion correction algorithm reduced the number of nondiagnostic scans compared with standard reconstruction (20.4% vs 34.8%; p < 0.001). Diagnostic accuracy for obstructive CAD with the motion correction algorithm (62%; 95% CI, 56-68%) was not significantly different from that of standard reconstruction on a per-subject basis (59%; 95% CI, 53-66%; p = 0.28) but was superior on a per-vessel basis: 77% (95% CI, 74-80%) versus 72% (95% CI, 69-75%) (p = 0.02). The motion correction algorithm was superior in subgroups of patients with severely obstructive (≥ 70%) stenosis, heart rate ≥ 70 beats/min, and vessels in the atrioventricular groove. The motion correction algorithm studied reduces artifacts and improves diagnostic performance for obstructive CAD on a per-vessel basis and in selected subgroups on a per-subject basis.

Hybrid detection of lung nodules on CT scan images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Lin; Tan, Yongqiang; Schwartz, Lawrence H.

Purpose: The diversity of lung nodules poses difficulty for the current computer-aided diagnostic (CAD) schemes for lung nodule detection on computed tomography (CT) scan images, especially in large-scale CT screening studies. We proposed a novel CAD scheme based on a hybrid method to address the challenges of detection in diverse lung nodules. Methods: The hybrid method proposed in this paper integrates several existing and widely used algorithms in the field of nodule detection, including morphological operation, dot-enhancement based on Hessian matrix, fuzzy connectedness segmentation, local density maximum algorithm, geodesic distance map, and regression tree classification. All of the adopted algorithmsmore » were organized into tree structures with multi-nodes. Each node in the tree structure aimed to deal with one type of lung nodule. Results: The method has been evaluated on 294 CT scans from the Lung Image Database Consortium (LIDC) dataset. The CT scans were randomly divided into two independent subsets: a training set (196 scans) and a test set (98 scans). In total, the 294 CT scans contained 631 lung nodules, which were annotated by at least two radiologists participating in the LIDC project. The sensitivity and false positive per scan for the training set were 87% and 2.61%. The sensitivity and false positive per scan for the testing set were 85.2% and 3.13%. Conclusions: The proposed hybrid method yielded high performance on the evaluation dataset and exhibits advantages over existing CAD schemes. We believe that the present method would be useful for a wide variety of CT imaging protocols used in both routine diagnosis and screening studies.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Setio, Arnaud A. A., E-mail: arnaud.arindraadiyoso@radboudumc.nl; Jacobs, Colin; Gelderblom, Jaap

Purpose: Current computer-aided detection (CAD) systems for pulmonary nodules in computed tomography (CT) scans have a good performance for relatively small nodules, but often fail to detect the much rarer larger nodules, which are more likely to be cancerous. We present a novel CAD system specifically designed to detect solid nodules larger than 10 mm. Methods: The proposed detection pipeline is initiated by a three-dimensional lung segmentation algorithm optimized to include large nodules attached to the pleural wall via morphological processing. An additional preprocessing is used to mask out structures outside the pleural space to ensure that pleural and parenchymalmore » nodules have a similar appearance. Next, nodule candidates are obtained via a multistage process of thresholding and morphological operations, to detect both larger and smaller candidates. After segmenting each candidate, a set of 24 features based on intensity, shape, blobness, and spatial context are computed. A radial basis support vector machine (SVM) classifier was used to classify nodule candidates, and performance was evaluated using ten-fold cross-validation on the full publicly available lung image database consortium database. Results: The proposed CAD system reaches a sensitivity of 98.3% (234/238) and 94.1% (224/238) large nodules at an average of 4.0 and 1.0 false positives/scan, respectively. Conclusions: The authors conclude that the proposed dedicated CAD system for large pulmonary nodules can identify the vast majority of highly suspicious lesions in thoracic CT scans with a small number of false positives.« less

GPU-accelerated depth map generation for X-ray simulations of complex CAD geometries

NASA Astrophysics Data System (ADS)

Grandin, Robert J.; Young, Gavin; Holland, Stephen D.; Krishnamurthy, Adarsh

2018-04-01

Interactive x-ray simulations of complex computer-aided design (CAD) models can provide valuable insights for better interpretation of the defect signatures such as porosity from x-ray CT images. Generating the depth map along a particular direction for the given CAD geometry is the most compute-intensive step in x-ray simulations. We have developed a GPU-accelerated method for real-time generation of depth maps of complex CAD geometries. We preprocess complex components designed using commercial CAD systems using a custom CAD module and convert them into a fine user-defined surface tessellation. Our CAD module can be used by different simulators as well as handle complex geometries, including those that arise from complex castings and composite structures. We then make use of a parallel algorithm that runs on a graphics processing unit (GPU) to convert the finely-tessellated CAD model to a voxelized representation. The voxelized representation can enable heterogeneous modeling of the volume enclosed by the CAD model by assigning heterogeneous material properties in specific regions. The depth maps are generated from this voxelized representation with the help of a GPU-accelerated ray-casting algorithm. The GPU-accelerated ray-casting method enables interactive (> 60 frames-per-second) generation of the depth maps of complex CAD geometries. This enables arbitrarily rotation and slicing of the CAD model, leading to better interpretation of the x-ray images by the user. In addition, the depth maps can be used to aid directly in CT reconstruction algorithms.

A feature-preserving hair removal algorithm for dermoscopy images.

PubMed

Abbas, Qaisar; Garcia, Irene Fondón; Emre Celebi, M; Ahmad, Waqar

2013-02-01

Accurate segmentation and repair of hair-occluded information from dermoscopy images are challenging tasks for computer-aided detection (CAD) of melanoma. Currently, many hair-restoration algorithms have been developed, but most of these fail to identify hairs accurately and their removal technique is slow and disturbs the lesion's pattern. In this article, a novel hair-restoration algorithm is presented, which has a capability to preserve the skin lesion features such as color and texture and able to segment both dark and light hairs. Our algorithm is based on three major steps: the rough hairs are segmented using a matched filtering with first derivative of gaussian (MF-FDOG) with thresholding that generate strong responses for both dark and light hairs, refinement of hairs by morphological edge-based techniques, which are repaired through a fast marching inpainting method. Diagnostic accuracy (DA) and texture-quality measure (TQM) metrics are utilized based on dermatologist-drawn manual hair masks that were used as a ground truth to evaluate the performance of the system. The hair-restoration algorithm is tested on 100 dermoscopy images. The comparisons have been done among (i) linear interpolation, inpainting by (ii) non-linear partial differential equation (PDE), and (iii) exemplar-based repairing techniques. Among different hair detection and removal techniques, our proposed algorithm obtained the highest value of DA: 93.3% and TQM: 90%. The experimental results indicate that the proposed algorithm is highly accurate, robust and able to restore hair pixels without damaging the lesion texture. This method is fully automatic and can be easily integrated into a CAD system. © 2011 John Wiley & Sons A/S.

Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants

PubMed Central

Castro, Alfonso; Boveda, Carmen; Arcay, Bernardino; Sanjurjo, Pedro

2016-01-01

The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium). PMID:27517049

Computation-aware algorithm selection approach for interlaced-to-progressive conversion

NASA Astrophysics Data System (ADS)

Park, Sang-Jun; Jeon, Gwanggil; Jeong, Jechang

2010-05-01

We discuss deinterlacing results in a computationally constrained and varied environment. The proposed computation-aware algorithm selection approach (CASA) for fast interlaced to progressive conversion algorithm consists of three methods: the line-averaging (LA) method for plain regions, the modified edge-based line-averaging (MELA) method for medium regions, and the proposed covariance-based adaptive deinterlacing (CAD) method for complex regions. The proposed CASA uses two criteria, mean-squared error (MSE) and CPU time, for assigning the method. We proposed a CAD method. The principle idea of CAD is based on the correspondence between the high and low-resolution covariances. We estimated the local covariance coefficients from an interlaced image using Wiener filtering theory and then used these optimal minimum MSE interpolation coefficients to obtain a deinterlaced image. The CAD method, though more robust than most known methods, was not found to be very fast compared to the others. To alleviate this issue, we proposed an adaptive selection approach using a fast deinterlacing algorithm rather than using only one CAD algorithm. The proposed hybrid approach of switching between the conventional schemes (LA and MELA) and our CAD was proposed to reduce the overall computational load. A reliable condition to be used for switching the schemes was presented after a wide set of initial training processes. The results of computer simulations showed that the proposed methods outperformed a number of methods presented in the literature.

A new approach to develop computer-aided detection schemes of digital mammograms

NASA Astrophysics Data System (ADS)

Tan, Maxine; Qian, Wei; Pu, Jiantao; Liu, Hong; Zheng, Bin

2015-06-01

The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman’s age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a sequential forward floating selection feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC = 0.779   ±   0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.

Pulmonary nodule detection using a cascaded SVM classifier

NASA Astrophysics Data System (ADS)

Bergtholdt, Martin; Wiemker, Rafael; Klinder, Tobias

2016-03-01

Automatic detection of lung nodules from chest CT has been researched intensively over the last decades resulting also in several commercial products. However, solutions are adopted only slowly into daily clinical routine as many current CAD systems still potentially miss true nodules while at the same time generating too many false positives (FP). While many earlier approaches had to rely on rather few cases for development, larger databases become now available and can be used for algorithmic development. In this paper, we address the problem of lung nodule detection via a cascaded SVM classifier. The idea is to sequentially perform two classification tasks in order to select from an extremely large pool of potential candidates the few most likely ones. As the initial pool is allowed to contain thousands of candidates, very loose criteria could be applied during this pre-selection. In this way, the chances that a true nodule is falsely rejected as a candidate are reduced significantly. The final algorithm is trained and tested on the full LIDC/IDRI database. Comparison is done against two previously published CAD systems. Overall, the algorithm achieved sensitivity of 0.859 at 2.5 FP/volume where the other two achieved sensitivity values of 0.321 and 0.625, respectively. On low dose data sets, only slight increase in the number of FP/volume was observed, while the sensitivity was not affected.

Integrating CAD modules in a PACS environment using a wide computing infrastructure.

PubMed

Suárez-Cuenca, Jorge J; Tilve, Amara; López, Ricardo; Ferro, Gonzalo; Quiles, Javier; Souto, Miguel

2017-04-01

The aim of this paper is to describe a project designed to achieve a total integration of different CAD algorithms into the PACS environment by using a wide computing infrastructure. The aim is to build a system for the entire region of Galicia, Spain, to make CAD accessible to multiple hospitals by employing different PACSs and clinical workstations. The new CAD model seeks to connect different devices (CAD systems, acquisition modalities, workstations and PACS) by means of networking based on a platform that will offer different CAD services. This paper describes some aspects related to the health services of the region where the project was developed, CAD algorithms that were either employed or selected for inclusion in the project, and several technical aspects and results. We have built a standard-based platform with which users can request a CAD service and receive the results in their local PACS. The process runs through a web interface that allows sending data to the different CAD services. A DICOM SR object is received with the results of the algorithms stored inside the original study in the proper folder with the original images. As a result, a homogeneous service to the different hospitals of the region will be offered. End users will benefit from a homogeneous workflow and a standardised integration model to request and obtain results from CAD systems in any modality, not dependant on commercial integration models. This new solution will foster the deployment of these technologies in the entire region of Galicia.

Computer-assisted detection (CAD) methodology for early detection of response to pharmaceutical therapy in tuberculosis patients

NASA Astrophysics Data System (ADS)

Lieberman, Robert; Kwong, Heston; Liu, Brent; Huang, H. K.

2009-02-01

The chest x-ray radiological features of tuberculosis patients are well documented, and the radiological features that change in response to successful pharmaceutical therapy can be followed with longitudinal studies over time. The patients can also be classified as either responsive or resistant to pharmaceutical therapy based on clinical improvement. We have retrospectively collected time series chest x-ray images of 200 patients diagnosed with tuberculosis receiving the standard pharmaceutical treatment. Computer algorithms can be created to utilize image texture features to assess the temporal changes in the chest x-rays of the tuberculosis patients. This methodology provides a framework for a computer-assisted detection (CAD) system that may provide physicians with the ability to detect poor treatment response earlier in pharmaceutical therapy. Early detection allows physicians to respond with more timely treatment alternatives and improved outcomes. Such a system has the potential to increase treatment efficacy for millions of patients each year.

Development of a web-based DICOM-SR viewer for CAD data of multiple sclerosis lesions in an imaging informatics-based efolder

NASA Astrophysics Data System (ADS)

Ma, Kevin; Wong, Jonathan; Zhong, Mark; Zhang, Jeff; Liu, Brent

2014-03-01

In the past, we have presented an imaging-informatics based eFolder system for managing and analyzing imaging and lesion data of multiple sclerosis (MS) patients, which allows for data storage, data analysis, and data mining in clinical and research settings. The system integrates the patient's clinical data with imaging studies and a computer-aided detection (CAD) algorithm for quantifying MS lesion volume, lesion contour, locations, and sizes in brain MRI studies. For compliance with IHE integration protocols, long-term storage in PACS, and data query and display in a DICOM compliant clinical setting, CAD results need to be converted into DICOM-Structured Report (SR) format. Open-source dcmtk and customized XML templates are used to convert quantitative MS CAD results from MATLAB to DICOM-SR format. A web-based GUI based on our existing web-accessible DICOM object (WADO) image viewer has been designed to display the CAD results from generated SR files. The GUI is able to parse DICOM-SR files and extract SR document data, then display lesion volume, location, and brain matter volume along with the referenced DICOM imaging study. In addition, the GUI supports lesion contour overlay, which matches a detected MS lesion with its corresponding DICOM-SR data when a user selects either the lesion or the data. The methodology of converting CAD data in native MATLAB format to DICOM-SR and displaying the tabulated DICOM-SR along with the patient's clinical information, and relevant study images in the GUI will be demonstrated. The developed SR conversion model and GUI support aim to further demonstrate how to incorporate CAD post-processing components in a PACS and imaging informatics-based environment.

Construction and comparative evaluation of different activity detection methods in brain FDG-PET.

PubMed

Buchholz, Hans-Georg; Wenzel, Fabian; Gartenschläger, Martin; Thiele, Frank; Young, Stewart; Reuss, Stefan; Schreckenberger, Mathias

2015-08-18

We constructed and evaluated reference brain FDG-PET databases for usage by three software programs (Computer-aided diagnosis for dementia (CAD4D), Statistical Parametric Mapping (SPM) and NEUROSTAT), which allow a user-independent detection of dementia-related hypometabolism in patients' brain FDG-PET. Thirty-seven healthy volunteers were scanned in order to construct brain FDG reference databases, which reflect the normal, age-dependent glucose consumption in human brain, using either software. Databases were compared to each other to assess the impact of different stereotactic normalization algorithms used by either software package. In addition, performance of the new reference databases in the detection of altered glucose consumption in the brains of patients was evaluated by calculating statistical maps of regional hypometabolism in FDG-PET of 20 patients with confirmed Alzheimer's dementia (AD) and of 10 non-AD patients. Extent (hypometabolic volume referred to as cluster size) and magnitude (peak z-score) of detected hypometabolism was statistically analyzed. Differences between the reference databases built by CAD4D, SPM or NEUROSTAT were observed. Due to the different normalization methods, altered spatial FDG patterns were found. When analyzing patient data with the reference databases created using CAD4D, SPM or NEUROSTAT, similar characteristic clusters of hypometabolism in the same brain regions were found in the AD group with either software. However, larger z-scores were observed with CAD4D and NEUROSTAT than those reported by SPM. Better concordance with CAD4D and NEUROSTAT was achieved using the spatially normalized images of SPM and an independent z-score calculation. The three software packages identified the peak z-scores in the same brain region in 11 of 20 AD cases, and there was concordance between CAD4D and SPM in 16 AD subjects. The clinical evaluation of brain FDG-PET of 20 AD patients with either CAD4D-, SPM- or NEUROSTAT-generated databases from an identical reference dataset showed similar patterns of hypometabolism in the brain regions known to be involved in AD. The extent of hypometabolism and peak z-score appeared to be influenced by the calculation method used in each software package rather than by different spatial normalization parameters.

Mixture of learners for cancer stem cell detection using CD13 and H and E stained images

NASA Astrophysics Data System (ADS)

Oǧuz, Oǧuzhan; Akbaş, Cem Emre; Mallah, Maen; Taşdemir, Kasım.; Akhan Güzelcan, Ece; Muenzenmayer, Christian; Wittenberg, Thomas; Üner, Ayşegül; Cetin, A. E.; ćetin Atalay, Rengül

2016-03-01

In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H and E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and Eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H and E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using H and E stained microscopic tissue images.

Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

PubMed

Hatipoglu, Nuh; Bilgin, Gokhan

2017-10-01

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

Head-to-head comparison of adaptive statistical and model-based iterative reconstruction algorithms for submillisievert coronary CT angiography.

PubMed

Benz, Dominik C; Fuchs, Tobias A; Gräni, Christoph; Studer Bruengger, Annina A; Clerc, Olivier F; Mikulicic, Fran; Messerli, Michael; Stehli, Julia; Possner, Mathias; Pazhenkottil, Aju P; Gaemperli, Oliver; Kaufmann, Philipp A; Buechel, Ronny R

2018-02-01

Iterative reconstruction (IR) algorithms allow for a significant reduction in radiation dose of coronary computed tomography angiography (CCTA). We performed a head-to-head comparison of adaptive statistical IR (ASiR) and model-based IR (MBIR) algorithms to assess their impact on quantitative image parameters and diagnostic accuracy for submillisievert CCTA. CCTA datasets of 91 patients were reconstructed using filtered back projection (FBP), increasing contributions of ASiR (20, 40, 60, 80, and 100%), and MBIR. Signal and noise were measured in the aortic root to calculate signal-to-noise ratio (SNR). In a subgroup of 36 patients, diagnostic accuracy of ASiR 40%, ASiR 100%, and MBIR for diagnosis of coronary artery disease (CAD) was compared with invasive coronary angiography. Median radiation dose was 0.21 mSv for CCTA. While increasing levels of ASiR gradually reduced image noise compared with FBP (up to - 48%, P < 0.001), MBIR provided largest noise reduction (-79% compared with FBP) outperforming ASiR (-59% compared with ASiR 100%; P < 0.001). Increased noise and lower SNR with ASiR 40% and ASiR 100% resulted in substantially lower diagnostic accuracy to detect CAD as diagnosed by invasive coronary angiography compared with MBIR: sensitivity and specificity were 100 and 37%, 100 and 57%, and 100 and 74% for ASiR 40%, ASiR 100%, and MBIR, respectively. MBIR offers substantial noise reduction with increased SNR, paving the way for implementation of submillisievert CCTA protocols in clinical routine. In contrast, inferior noise reduction by ASiR negatively affects diagnostic accuracy of submillisievert CCTA for CAD detection. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.

Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm.

PubMed

Gaber, Tarek; Ismail, Gehad; Anter, Ahmed; Soliman, Mona; Ali, Mona; Semary, Noura; Hassanien, Aboul Ella; Snasel, Vaclav

2015-08-01

The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation and classification. For the former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%.

Detection of Obstructive Coronary Artery Disease Using Peak Systolic Global Longitudinal Strain Derived by Two-Dimensional Speckle-Tracking: A Systematic Review and Meta-Analysis.

PubMed

Liou, Kevin; Negishi, Kazuaki; Ho, Suyen; Russell, Elizabeth A; Cranney, Greg; Ooi, Sze-Yuan

2016-08-01

Global longitudinal strain (GLS) is well validated and has important applications in contemporary clinical practice. The aim of this analysis was to evaluate the accuracy of resting peak GLS in the diagnosis of obstructive coronary artery disease (CAD). A systematic literature search was performed through July 2015 using four databases. Data were extracted independently by two authors and correlated before analyses. Using a random-effect model, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and summary area under the curve for GLS were estimated with their respective 95% CIs. Screening of 1,669 articles yielded 10 studies with 1,385 patients appropriate for inclusion in the analysis. The mean age and left ventricular ejection fraction were 59.9 years and 61.1%. On the whole, 54.9% and 20.9% of the patients had hypertension and diabetes, respectively. Overall, abnormal GLS detected moderate to severe CAD with a pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of 74.4%, 72.1%, 2.9, and 0.35 respectively. The area under the curve and diagnostic odds ratio were 0.81 and 8.5. The mean values of GLS for those with and without CAD were -16.5% (95% CI, -15.8% to -17.3%) and -19.7% (95% CI, -18.8% to -20.7%), respectively. Subgroup analyses for patients with severe CAD and normal left ventricular ejection fractions yielded similar results. Current evidence supports the use of GLS in the detection of moderate to severe obstructive CAD in symptomatic patients. GLS may complement existing diagnostic algorithms and act as an early adjunctive marker of cardiac ischemia. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Ross, Stephen R.; Wang, Yunzhi; Wu, Dee H.; Cornwell, Benjamin O.; Ray, Bappaditya; Zheng, Bin

2017-03-01

Aneurysmal subarachnoid hemorrhage (aSAH) is a form of hemorrhagic stroke that affects middle-aged individuals and associated with significant morbidity and/or mortality especially those presenting with higher clinical and radiologic grades at the time of admission. Previous studies suggested that blood extravasated after aneurysmal rupture was a potentially clinical prognosis factor. But all such studies used qualitative scales to predict prognosis. The purpose of this study is to develop and test a new interactive computer-aided detection (CAD) tool to detect, segment and quantify brain hemorrhage and ventricular cerebrospinal fluid on non-contrasted brain CT images. First, CAD segments brain skull using a multilayer region growing algorithm with adaptively adjusted thresholds. Second, CAD assigns pixels inside the segmented brain region into one of three classes namely, normal brain tissue, blood and fluid. Third, to avoid "black-box" approach and increase accuracy in quantification of these two image markers using CT images with large noise variation in different cases, a graphic User Interface (GUI) was implemented and allows users to visually examine segmentation results. If a user likes to correct any errors (i.e., deleting clinically irrelevant blood or fluid regions, or fill in the holes inside the relevant blood or fluid regions), he/she can manually define the region and select a corresponding correction function. CAD will automatically perform correction and update the computed data. The new CAD tool is now being used in clinical and research settings to estimate various quantitatively radiological parameters/markers to determine radiological severity of aSAH at presentation and correlate the estimations with various homeostatic/metabolic derangements and predict clinical outcome.

Noise detection in heart sound recordings.

PubMed

Zia, Mohammad K; Griffel, Benjamin; Fridman, Vladimir; Saponieri, Cesare; Semmlow, John L

2011-01-01

Coronary artery disease (CAD) is the leading cause of death in the United States. Although progression of CAD can be controlled using drugs and diet, it is usually detected in advanced stages when invasive treatment is required. Current methods to detect CAD are invasive and/or costly, hence not suitable as a regular screening tool to detect CAD in early stages. Currently, we are developing a noninvasive and cost-effective system to detect CAD using the acoustic approach. This method identifies sounds generated by turbulent flow through partially narrowed coronary arteries to detect CAD. The limiting factor of this method is sensitivity to noises commonly encountered in the clinical setting. Because the CAD sounds are faint, these noises can easily obscure the CAD sounds and make detection impossible. In this paper, we propose a method to detect and eliminate noise encountered in the clinical setting using a reference channel. We show that our method is effective in detecting noise, which is essential to the success of the acoustic approach.

Digital mammography, cancer screening: Factors important for image compression

NASA Technical Reports Server (NTRS)

Clarke, Laurence P.; Blaine, G. James; Doi, Kunio; Yaffe, Martin J.; Shtern, Faina; Brown, G. Stephen; Winfield, Daniel L.; Kallergi, Maria

1993-01-01

The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers.

Symmetry-based detection and diagnosis of DCIS in breast MRI

NASA Astrophysics Data System (ADS)

Srikantha, Abhilash; Harz, Markus T.; Newstead, Gillian; Wang, Lei; Platel, Bram; Hegenscheid, Katrin; Mann, Ritse M.; Hahn, Horst K.; Peitgen, Heinz-Otto

2013-02-01

The delineation and diagnosis of non-mass-like lesions, most notably DCIS (ductal carcinoma in situ), is among the most challenging tasks in breast MRI reading. Even for human observers, DCIS is not always easy to diferentiate from patterns of active parenchymal enhancement or from benign alterations of breast tissue. In this light, it is no surprise that CADe/CADx approaches often completely fail to classify DCIS. Of the several approaches that have tried to devise such computer aid, none achieve performances similar to mass detection and classification in terms of sensitivity and specificity. In our contribution, we show a novel approach to combine a newly proposed metric of anatomical breast symmetry calculated on subtraction images of dynamic contrast-enhanced (DCE) breast MRI, descriptive kinetic parameters, and lesion candidate morphology to achieve performances comparable to computer-aided methods used for masses. We have based the development of the method on DCE MRI data of 18 DCIS cases with hand-annotated lesions, complemented by DCE-MRI data of nine normal cases. We propose a novel metric to quantify the symmetry of contralateral breasts and derive a strong indicator for potentially malignant changes from this metric. Also, we propose a novel metric for the orientation of a finding towards a fix point (the nipple). Our combined scheme then achieves a sensitivity of 89% with a specificity of 78%, matching CAD results for breast MRI on masses. The processing pipeline is intended to run on a CAD server, hence we designed all processing to be automated and free of per-case parameters. We expect that the detection results of our proposed non-mass aimed algorithm will complement other CAD algorithms, or ideally be joined with them in a voting scheme.

The 2013 ACC/AHA cardiovascular prevention guidelines improve alignment of statin therapy with coronary atherosclerosis as detected by coronary computed tomography angiography.

PubMed

Pursnani, Amit; Mayrhofer, Thomas; Ferencik, Maros; Hoffmann, Udo

2014-11-01

The recently released 2013 ACC/AHA guidelines for management of blood cholesterol have substantially increased the number of adults who are eligible for preventive statin therapy. We sought to determine whether eligibility for statin therapy as determined by the 2013 ACC/AHA guideline recommendation is better aligned with the actual presence of coronary artery disease (CAD) as detected by coronary CT angiography (CCTA) when compared to prior guidelines including the 2004 NCEP ATP III and 2011 ESC/EAS guidelines. In this secondary analysis of the prospective observational ROMICAT I (Rule Out Myocardial Infarction with Computer Assisted Tomography) cohort study, we included all men and women aged 40-79 years presenting with acute chest pain but not diagnosed with acute coronary syndrome nor on admission statin. Based on risk factor assessment and lipid data, we determined guideline-based eligibility for statin therapy by the 2013 ACC/AHA, the 2004 NCEP ATP III, and the 2011 ESC/EAS guidelines. We determined the presence and severity of CAD as detected by CCTA. The 2013 ACC/AHA algorithm identified nearly twice as many individuals as eligible for statins (n = 77/189; 41%) as compared to the 2004 ATP III criteria: (n = 41/189; 22%), (p < .0001) In addition, the 2013 ACC/AHA guidelines were more sensitive for treatment of CCTA-detected CAD than the 2004 ATP III guidelines [53.4% (42.5-64.1) vs 27.3% (18.3-37.8), p < .001] and the 2011 ESC/EAE guidelines [53.4% (42.5-64.1) vs 34.1% (24.3-45.0), p < .001]. However, the specificity of these guidelines was modestly reduced compared to the 2004 ATP III guidelines [70.3 (60.4-79.0) vs 83.2 (74.4-89.9), p < .001] and the 2011 ESC/EAE guidelines [70.3 (60.4-79.0) vs 86.1 (77.8-92.2), p < .001], suggesting increased treatment of subjects without CCTA-detected CAD. Overall, the 2013 ACC/AHA guidelines are more sensitive to identify patients who have CAD detected by CCTA eligible for statin therapy as compared with prior guidelines, with an acceptable trade-off in specificity for recommending statin therapy in those without CAD. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The 2013 ACC/AHA Cardiovascular Prevention Guidelines Improve Alignment of Statin Therapy with Coronary Atherosclerosis As Detected by Coronary Computed Tomography Angiography

PubMed Central

Pursnani, Amit; Mayrhofer, Thomas; Ferencik, Maros; Hoffmann, Udo

2018-01-01

The recently released 2013 ACC/AHA guidelines for management of blood cholesterol have substantially increased the number of adults who are eligible for preventive statin therapy. We sought to determine whether eligibility for statin therapy as determined by the 2013 ACC/AHA guideline recommendation is better aligned with the actual presence of coronary artery disease (CAD) as detected by coronary CT angiography (CCTA) when compared to prior guidelines including the 2004 NCEP ATP III and 2011 ESC/EAS guidelines. In this secondary analysis of the prospective observational ROMICAT I (Rule Out Myocardial Infarction with Computer Assisted Tomography) cohort study, we included all men and women aged 40–79 years presenting with acute chest pain but not diagnosed with acute coronary syndrome nor on admission statin. Based on risk factor assessment and lipid data, we determined guideline-based eligibility for statin therapy by the 2013 ACC/AHA, the 2004 NCEP ATP II, and the 2011 ESC/EAS guidelines. We determined the presence and severity of CAD as detected by CCTA. The 2013 ACC/AHA algorithm identified nearly twice as many individuals as eligible for statins (n=77/189; 41%) as compared to the 2004 ATPIII criteria: (n=41/189; 22%), (P<.0001) In addition, the 2013 ACC/AHA guidelines were more sensitive for treatment of CCTA-detected CAD than the 2004 ATP III guidelines [53.4% (42.5–64.1) vs 27.3% (18.3–37.8), p<.001] and the 2011 ESC/EAE guidelines [53.4% (42.5–64.1) vs 34.1% (24.3–45.0), p<.001]. However, the specificity of these guidelines was modestly reduced compared to the 2004 ATP III guidelines [70.3 (60.4–79.0) vs 83.2 (74.4–89.9), p<.001] and the 2011 ESC/EAE guidelines [70.3 (60.4–79.0) vs 86.1 (77.8–92.2), p<.001], suggesting increased treatment of subjects without CCTA-detected CAD. Overall, the 2013 ACC/AHA guidelines are more sensitive to identify patients who have CAD detected by CCTA eligible for statin therapy as compared with prior guidelines, with an acceptable trade-off in specificity for recommending statin therapy in those without CAD. PMID:25299966

TH-AB-207A-12: CT Lung Cancer Screening and the Effects of Further Dose Reduction On CAD Performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, S; Lo, P; Hoffman, J

Purpose: CT lung screening is already performed at low doses. In this study, we investigated the effects of further dose reduction on a lung-nodule CAD detection algorithm. Methods: The original raw CT data and images from 348 patients were obtained from our local database of National Lung Screening Trial (NLST) cases. 61 patients (17.5%) had at least one nodule reported on the NLST reader forms. All scans were acquired with fixed mAs (25 for standard-sized patients, 40 for large patients) on a 64-slice scanner (Sensation 64, Siemens Healthcare). All images were reconstructed with 1-mm slice thickness, B50 kernel. Based onmore » a previously-published technique, we added noise to the raw data to simulate reduced-dose versions of each case at 50% and 25% of the original NLST dose (i.e. approximately 1.0 and 0.5 mGy CTDIvol). For each case at each dose level, a CAD detection algorithm was run and nodules greater than 4 mm in diameter were reported. These CAD results were compared to “truth”, defined as the approximate nodule centroids from the NLST forms. Sensitivities and false-positive rates (FPR) were calculated for each dose level, with a sub-analysis by nodule LungRADS category. Results: For larger category 4 nodules, median sensitivities were 100% at all three dose levels, and mean sensitivity decreased with dose. For the more challenging category 2 and 3 nodules, the dose dependence was less obvious. Overall, mean subject-level sensitivity varied from 38.5% at 100% dose to 40.4% at 50% dose, a difference of only 1.9%. However, median FPR quadrupled from 1 per case at 100% dose to 4 per case at 25% dose. Conclusions: Dose reduction affected nodule detectability differently depending on the LungRADS category, and FPR was very sensitive at sub-screening levels. Care should be taken to adapt CAD for the very challenging noise characteristics of screening. Funding support: NIH U01 CA181156; Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.« less

Research on remote sensing image pixel attribute data acquisition method in AutoCAD

NASA Astrophysics Data System (ADS)

Liu, Xiaoyang; Sun, Guangtong; Liu, Jun; Liu, Hui

2013-07-01

The remote sensing image has been widely used in AutoCAD, but AutoCAD lack of the function of remote sensing image processing. In the paper, ObjectARX was used for the secondary development tool, combined with the Image Engine SDK to realize remote sensing image pixel attribute data acquisition in AutoCAD, which provides critical technical support for AutoCAD environment remote sensing image processing algorithms.

Colonic polyps: application value of computer-aided detection in computed tomographic colonography.

PubMed

Zhang, Hui-Mao; Guo, Wei; Liu, Gui-Feng; An, Dong-Hong; Gao, Shuo-Hui; Sun, Li-Bo; Yang, Hai-Shan

2011-02-01

Colonic polyps are frequently encountered in clinics. Computed tomographic colonography (CTC), as a painless and quick detection, has high values in clinics. In this study, we evaluated the application value of computer-aided detection (CAD) in CTC detection of colonic polyps in the Chinese population. CTC was performed with a GE 64-row multidetector computed tomography (MDCT) scanner. Data of 50 CTC patients (39 patients positive for at least one polyp of ≥ 0.5 cm in size and the other 11 patients negative by endoscopic detection) were retrospectively reviewed first without computer-aided detection (CAD) and then with CAD by four radiologists (two were experienced and another two inexperienced) blinded to colonoscopy findings. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of detected colonic polyps, as well as the areas under the ROC curves (Az value) with and without CAD were calculated. CAD increased the overall sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the colonic polyps detected by experienced and inexperienced readers. The sensitivity in detecting small polyps (5 - 9 mm) with CAD in experienced and inexperienced readers increased from 82% and 44% to 93% and 82%, respectively (P > 0.05 and P < 0.001). With the use of CAD, the overall false positive rate and false negative rate for the detection of polyps by experienced and inexperienced readers decreased in different degrees. Among 13 sessile polyps not detected by CAD, two were ≥ 1.0 cm, eleven were 5 - 9 mm in diameter, and nine were flat-shaped lesions. The application of CAD in combination with CTC can increase the ability to detect colonic polyps, particularly for inexperienced readers. However, CAD is of limited value for the detection of flat polyps.

A cloud platform for remote diagnosis of breast cancer in mammography by fusion of machine and human intelligence

NASA Astrophysics Data System (ADS)

Jiang, Guodong; Fan, Ming; Li, Lihua

2016-03-01

Mammography is the gold standard for breast cancer screening, reducing mortality by about 30%. The application of a computer-aided detection (CAD) system to assist a single radiologist is important to further improve mammographic sensitivity for breast cancer detection. In this study, a design and realization of the prototype for remote diagnosis system in mammography based on cloud platform were proposed. To build this system, technologies were utilized including medical image information construction, cloud infrastructure and human-machine diagnosis model. Specifically, on one hand, web platform for remote diagnosis was established by J2EE web technology. Moreover, background design was realized through Hadoop open-source framework. On the other hand, storage system was built up with Hadoop distributed file system (HDFS) technology which enables users to easily develop and run on massive data application, and give full play to the advantages of cloud computing which is characterized by high efficiency, scalability and low cost. In addition, the CAD system was realized through MapReduce frame. The diagnosis module in this system implemented the algorithms of fusion of machine and human intelligence. Specifically, we combined results of diagnoses from doctors' experience and traditional CAD by using the man-machine intelligent fusion model based on Alpha-Integration and multi-agent algorithm. Finally, the applications on different levels of this system in the platform were also discussed. This diagnosis system will have great importance for the balanced health resource, lower medical expense and improvement of accuracy of diagnosis in basic medical institutes.

The effect of radiation dose reduction on computer-aided detection (CAD) performance in a low-dose lung cancer screening population.

PubMed

Young, Stefano; Lo, Pechin; Kim, Grace; Brown, Matthew; Hoffman, John; Hsu, William; Wahi-Anwar, Wasil; Flores, Carlos; Lee, Grace; Noo, Frederic; Goldin, Jonathan; McNitt-Gray, Michael

2017-04-01

Lung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-aided detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung nodule detection performance in a screening population. The raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multidetector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher dose scans (75-300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub-analyses were performed. From the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. Twenty-seven nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose, respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and nonsolid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case. CAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high-noise screening exams. © 2017 American Association of Physicists in Medicine.

A comparison study of image features between FFDM and film mammogram images

PubMed Central

Jing, Hao; Yang, Yongyi; Wernick, Miles N.; Yarusso, Laura M.; Nishikawa, Robert M.

2012-01-01

Purpose: This work is to provide a direct, quantitative comparison of image features measured by film and full-field digital mammography (FFDM). The purpose is to investigate whether there is any systematic difference between film and FFDM in terms of quantitative image features and their influence on the performance of a computer-aided diagnosis (CAD) system. Methods: The authors make use of a set of matched film-FFDM image pairs acquired from cadaver breast specimens with simulated microcalcifications consisting of bone and teeth fragments using both a GE digital mammography system and a screen-film system. To quantify the image features, the authors consider a set of 12 textural features of lesion regions and six image features of individual microcalcifications (MCs). The authors first conduct a direct comparison on these quantitative features extracted from film and FFDM images. The authors then study the performance of a CAD classifier for discriminating between MCs and false positives (FPs) when the classifier is trained on images of different types (film, FFDM, or both). Results: For all the features considered, the quantitative results show a high degree of correlation between features extracted from film and FFDM, with the correlation coefficients ranging from 0.7326 to 0.9602 for the different features. Based on a Fisher sign rank test, there was no significant difference observed between the features extracted from film and those from FFDM. For both MC detection and discrimination of FPs from MCs, FFDM had a slight but statistically significant advantage in performance; however, when the classifiers were trained on different types of images (acquired with FFDM or SFM) for discriminating MCs from FPs, there was little difference. Conclusions: The results indicate good agreement between film and FFDM in quantitative image features. While FFDM images provide better detection performance in MCs, FFDM and film images may be interchangeable for the purposes of training CAD algorithms, and a single CAD algorithm may be applied to either type of images. PMID:22830771

Improving efficacy of metastatic tumor segmentation to facilitate early prediction of ovarian cancer patients' response to chemotherapy

NASA Astrophysics Data System (ADS)

Danala, Gopichandh; Wang, Yunzhi; Thai, Theresa; Gunderson, Camille C.; Moxley, Katherine M.; Moore, Kathleen; Mannel, Robert S.; Cheng, Samuel; Liu, Hong; Zheng, Bin; Qiu, Yuchen

2017-02-01

Accurate tumor segmentation is a critical step in the development of the computer-aided detection (CAD) based quantitative image analysis scheme for early stage prognostic evaluation of ovarian cancer patients. The purpose of this investigation is to assess the efficacy of several different methods to segment the metastatic tumors occurred in different organs of ovarian cancer patients. In this study, we developed a segmentation scheme consisting of eight different algorithms, which can be divided into three groups: 1) Region growth based methods; 2) Canny operator based methods; and 3) Partial differential equation (PDE) based methods. A number of 138 tumors acquired from 30 ovarian cancer patients were used to test the performance of these eight segmentation algorithms. The results demonstrate each of the tested tumors can be successfully segmented by at least one of the eight algorithms without the manual boundary correction. Furthermore, modified region growth, classical Canny detector, and fast marching, and threshold level set algorithms are suggested in the future development of the ovarian cancer related CAD schemes. This study may provide meaningful reference for developing novel quantitative image feature analysis scheme to more accurately predict the response of ovarian cancer patients to the chemotherapy at early stage.

Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

PubMed

Wang, Shijun; McKenna, Matthew T; Nguyen, Tan B; Burns, Joseph E; Petrick, Nicholas; Sahiner, Berkman; Summers, Ronald M

2012-05-01

In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

PubMed

Yassin, Nisreen I R; Omran, Shaimaa; El Houby, Enas M F; Allam, Hemat

2018-03-01

The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows.

PubMed

Depeursinge, Adrien; Vargas, Alejandro; Gaillard, Frédéric; Platon, Alexandra; Geissbuhler, Antoine; Poletti, Pierre-Alexandre; Müller, Henning

2012-01-01

Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the system.

Seeing is Believing: Video Classification for Computed Tomographic Colonography Using Multiple-Instance Learning

PubMed Central

Wang, Shijun; McKenna, Matthew T.; Nguyen, Tan B.; Burns, Joseph E.; Petrick, Nicholas; Sahiner, Berkman

2012-01-01

In this paper we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods. PMID:22552333

The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review.

PubMed

Henriksen, Emilie L; Carlsen, Jonathan F; Vejborg, Ilse Mm; Nielsen, Michael B; Lauridsen, Carsten A

2018-01-01

Background Early detection of breast cancer (BC) is crucial in lowering the mortality. Purpose To present an overview of studies concerning computer-aided detection (CAD) in screening mammography for early detection of BC and compare diagnostic accuracy and recall rates (RR) of single reading (SR) with SR + CAD and double reading (DR) with SR + CAD. Material and Methods PRISMA guidelines were used as a review protocol. Articles on clinical trials concerning CAD for detection of BC in a screening population were included. The literature search resulted in 1522 records. A total of 1491 records were excluded by abstract and 18 were excluded by full text reading. A total of 13 articles were included. Results All but two studies from the SR vs. SR + CAD group showed an increased sensitivity and/or cancer detection rate (CDR) when adding CAD. The DR vs. SR + CAD group showed no significant differences in sensitivity and CDR. Adding CAD to SR increased the RR and decreased the specificity in all but one study. For the DR vs. SR + CAD group only one study reported a significant difference in RR. Conclusion All but two studies showed an increase in RR, sensitivity and CDR when adding CAD to SR. Compared to DR no statistically significant differences in sensitivity or CDR were reported. Additional studies based on organized population-based screening programs, with longer follow-up time, high-volume readers, and digital mammography are needed to evaluate the efficacy of CAD.

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

NASA Astrophysics Data System (ADS)

Traverso, A.; Lopez Torres, E.; Fantacci, M. E.; Cerello, P.

2017-05-01

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists.

Common genetic risk factors for coronary artery disease: new opportunities for prevention?

PubMed

Hamrefors, Viktor

2017-05-01

Atherosclerotic cardiovascular disease (CVD) is a leading cause of mortality and morbidity worldwide, with coronary artery disease (CAD) being the single leading cause of death. Better control of risk factors, enhanced diagnostic techniques and improved medical therapies have all substantially decreased the mortality of CAD in developed countries. However, CAD and other forms of atherosclerotic CVD are projected to remain the leading cause of death by 2030 and we face a number of challenges if the outcomes of CAD are to be further improved. The fact that a substantial fraction of high-risk subjects do not reach treatment goals for important risk factors is one of these challenges. At the same time, there is also a non-negotiable fraction of 'concealed' high-risk subjects who are not detected by current risk algorithms and diagnostic modalities. In recent years, we have started to rapidly increase our knowledge of the framework of common genetics underlying CAD and atherosclerotic CVD in the population. In conjunction with modern diagnostic and therapeutic options, this new genetic knowledge may provide a valuable tool for further improvements in prevention. This review summarizes the recent findings from the search for common genetic risk factors for CAD. Furthermore, the author discusses how such recent findings could potentially be used in a number of clinical applications within CAD prevention, including in clinical risk stratification, in prediction of drug treatment response and in the search for targets for novel preventive therapies. © 2015 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Detection of breast cancer with full-field digital mammography and computer-aided detection.

PubMed

The, Juliette S; Schilling, Kathy J; Hoffmeister, Jeffrey W; Friedmann, Euvondia; McGinnis, Ryan; Holcomb, Richard G

2009-02-01

The purpose of this study was to evaluate computer-aided detection (CAD) performance with full-field digital mammography (FFDM). CAD (Second Look, version 7.2) was used to evaluate 123 cases of breast cancer detected with FFDM (Senographe DS). Retrospectively, CAD sensitivity was assessed using breast density, mammographic presentation, histopathology results, and lesion size. To determine the case-based false-positive rate, patients with four standard views per case were included in the study group. Eighteen unilateral mammography examinations with nonstandard views were excluded, resulting in a sample of 105 bilateral cases. CAD detected 115 (94%) of 123 cancer cases: six of six (100%) in fatty breasts, 63 of 66 (95%) in breasts containing scattered fibroglandular densities, 43 of 46 (93%) in heterogeneously dense breasts, and three of five (60%) in extremely dense breasts. CAD detected 93% (41/44) of cancers manifesting as calcifications, 92% (57/62) as masses, and 100% (17/17) as mixed masses and calcifications. CAD detected 94% of the invasive ductal carcinomas (n = 63), 100% of the invasive lobular carcinomas (n = 7), 91% of the other invasive carcinomas (n = 11), and 93% of the ductal carcinomas in situ (n = 42). CAD sensitivity for cancers 1-10 mm (n = 55) was 89%; 11-20 mm (n = 37), 97%; 21-30 mm (n = 16), 100%; and larger than 30 mm (n = 15), 93%. The CAD false-positive rate was 2.3 marks per four-image case. CAD with FFDM showed a high sensitivity in identifying cancers manifesting as calcifications and masses. Sensitivity was maintained in cancers with lower mammographic sensitivity, including invasive lobular carcinomas and small neoplasms (1-20 mm). CAD with FFDM should be effective in assisting radiologists with earlier detection of breast cancer. Future studies are needed to assess CAD accuracy in larger populations.

Automatic updating and 3D modeling of airport information from high resolution images using GIS and LIDAR data

NASA Astrophysics Data System (ADS)

Lv, Zheng; Sui, Haigang; Zhang, Xilin; Huang, Xianfeng

2007-11-01

As one of the most important geo-spatial objects and military establishment, airport is always a key target in fields of transportation and military affairs. Therefore, automatic recognition and extraction of airport from remote sensing images is very important and urgent for updating of civil aviation and military application. In this paper, a new multi-source data fusion approach on automatic airport information extraction, updating and 3D modeling is addressed. Corresponding key technologies including feature extraction of airport information based on a modified Ostu algorithm, automatic change detection based on new parallel lines-based buffer detection algorithm, 3D modeling based on gradual elimination of non-building points algorithm, 3D change detecting between old airport model and LIDAR data, typical CAD models imported and so on are discussed in detail. At last, based on these technologies, we develop a prototype system and the results show our method can achieve good effects.

Matching Real and Synthetic Panoramic Images Using a Variant of Geometric Hashing

NASA Astrophysics Data System (ADS)

Li-Chee-Ming, J.; Armenakis, C.

2017-05-01

This work demonstrates an approach to automatically initialize a visual model-based tracker, and recover from lost tracking, without prior camera pose information. These approaches are commonly referred to as tracking-by-detection. Previous tracking-by-detection techniques used either fiducials (i.e. landmarks or markers) or the object's texture. The main contribution of this work is the development of a tracking-by-detection algorithm that is based solely on natural geometric features. A variant of geometric hashing, a model-to-image registration algorithm, is proposed that searches for a matching panoramic image from a database of synthetic panoramic images captured in a 3D virtual environment. The approach identifies corresponding features between the matched panoramic images. The corresponding features are to be used in a photogrammetric space resection to estimate the camera pose. The experiments apply this algorithm to initialize a model-based tracker in an indoor environment using the 3D CAD model of the building.

CAD system for footwear design based on whole real 3D data of last surface

NASA Astrophysics Data System (ADS)

Song, Wanzhong; Su, Xianyu

2000-10-01

Two major parts of application of CAD in footwear design are studied: the development of last surface; computer-aided design of planar shoe-template. A new quasi-experiential development algorithm of last surface based on triangulation approximation is presented. This development algorithm consumes less time and does not need any interactive operation for precisely development compared with other development algorithm of last surface. Based on this algorithm, a software, SHOEMAKERTM, which contains computer aided automatic measurement, automatic development of last surface and computer aide design of shoe-template has been developed.

Computer-aided diagnosis (CAD) for colonoscopy

NASA Astrophysics Data System (ADS)

Gu, Jia; Poirson, Allen

2007-03-01

Colorectal cancer is the second leading cause of cancer deaths, and ranks third for new cancer cases and cancer mortality for both men and women. However, its death rate can be dramatically reduced by appropriate treatment when early detection is available. The purpose of colonoscopy is to identify and assess the severity of lesions, which may be flat or protruding. Due to the subjective nature of the examination, colonoscopic proficiency is highly variable and dependent upon the colonoscopist's knowledge and experience. An automated image processing system providing an objective, rapid, and inexpensive analysis of video from a standard colonoscope could provide a valuable tool for screening and diagnosis. In this paper, we present the design, functionality and preliminary results of its Computer-Aided-Diagnosis (CAD) system for colonoscopy - ColonoCAD TM. ColonoCAD is a complex multi-sensor, multi-data and multi-algorithm image processing system, incorporating data management and visualization, video quality assessment and enhancement, calibration, multiple view based reconstruction, feature extraction and classification. As this is a new field in medical image processing, our hope is that this paper will provide the framework to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.

Analog Computer-Aided Detection (CAD) information can be more effective than binary marks.

PubMed

Cunningham, Corbin A; Drew, Trafton; Wolfe, Jeremy M

2017-02-01

In socially important visual search tasks, such as baggage screening and diagnostic radiology, experts miss more targets than is desirable. Computer-aided detection (CAD) programs have been developed specifically to improve performance in these professional search tasks. For example, in breast cancer screening, many CAD systems are capable of detecting approximately 90% of breast cancer, with approximately 0.5 false-positive detections per image. Nevertheless, benefits of CAD in clinical settings tend to be small (Birdwell, 2009) or even absent (Meziane et al., 2011; Philpotts, 2009). The marks made by a CAD system can be "binary," giving the same signal to any location where the signal is above some threshold. Alternatively, a CAD system presents an analog signal that reflects strength of the signal at a location. In the experiments reported, we compare analog and binary CAD presentations using nonexpert observers and artificial stimuli defined by two noisy signals: a visible color signal and an "invisible" signal that informed our simulated CAD system. We found that analog CAD generally yielded better overall performance than binary CAD. The analog benefit is similar at high and low target prevalence. Our data suggest that the form of the CAD signal can directly influence performance. Analog CAD may allow the computer to be more helpful to the searcher.

Electrocardiologic and related methods of non-invasive detection and risk stratification in myocardial ischemia: state of the art and perspectives

PubMed Central

Huebner, Thomas; Goernig, Matthias; Schuepbach, Michael; Sanz, Ernst; Pilgram, Roland; Seeck, Andrea; Voss, Andreas

2010-01-01

Background: Electrocardiographic methods still provide the bulk of cardiovascular diagnostics. Cardiac ischemia is associated with typical alterations in cardiac biosignals that have to be measured, analyzed by mathematical algorithms and allegorized for further clinical diagnostics. The fast growing fields of biomedical engineering and applied sciences are intensely focused on generating new approaches to cardiac biosignal analysis for diagnosis and risk stratification in myocardial ischemia. Objectives: To present and review the state of the art in and new approaches to electrocardiologic methods for non-invasive detection and risk stratification in coronary artery disease (CAD) and myocardial ischemia; secondarily, to explore the future perspectives of these methods. Methods: In follow-up to the Expert Discussion at the 2008 Workshop on "Biosignal Analysis" of the German Society of Biomedical Engineering in Potsdam, Germany, we comprehensively searched the pertinent literature and databases and compiled the results into this review. Then, we categorized the state-of-the-art methods and selected new approaches based on their applications in detection and risk stratification of myocardial ischemia. Finally, we compared the pros and cons of the methods and explored their future potentials for cardiology. Results: Resting ECG, particularly suited for detecting ST-elevation myocardial infarctions, and exercise ECG, for the diagnosis of stable CAD, are state-of-the-art methods. New exercise-free methods for detecting stable CAD include cardiogoniometry (CGM); methods for detecting acute coronary syndrome without ST elevation are Body Surface Potential Mapping, functional imaging and CGM. Heart rate variability and blood pressure variability analyses, microvolt T-wave alternans and signal-averaged ECG mainly serve in detecting and stratifying the risk for lethal arrythmias in patients with myocardial ischemia or previous myocardial infarctions. Telemedicine and ambient-assisted living support the electrocardiological monitoring of at-risk patients. Conclusions: There are many promising methods for the exercise-free, non-invasive detection of CAD and myocardial ischemia in the stable and acute phases. In the coming years, these new methods will help enhance state-of-the-art procedures in routine diagnostics. The future can expect that equally novel methods for risk stratification and telemedicine will transition into clinical routine. PMID:21063467

Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

Observer training for computer-aided detection of pulmonary nodules in chest radiography.

PubMed

De Boo, Diederick W; van Hoorn, François; van Schuppen, Joost; Schijf, Laura; Scheerder, Maeke J; Freling, Nicole J; Mets, Onno; Weber, Michael; Schaefer-Prokop, Cornelia M

2012-08-01

To assess whether short-term feedback helps readers to increase their performance using computer-aided detection (CAD) for nodule detection in chest radiography. The 140 CXRs (56 with a solitary CT-proven nodules and 84 negative controls) were divided into four subsets of 35; each were read in a different order by six readers. Lesion presence, location and diagnostic confidence were scored without and with CAD (IQQA-Chest, EDDA Technology) as second reader. Readers received individual feedback after each subset. Sensitivity, specificity and area under the receiver-operating characteristics curve (AUC) were calculated for readings with and without CAD with respect to change over time and impact of CAD. CAD stand-alone sensitivity was 59 % with 1.9 false-positives per image. Mean AUC slightly increased over time with and without CAD (0.78 vs. 0.84 with and 0.76 vs. 0.82 without CAD) but differences did not reach significance. The sensitivity increased (65 % vs. 70 % and 66 % vs. 70 %) and specificity decreased over time (79 % vs. 74 % and 80 % vs. 77 %) but no significant impact of CAD was found. Short-term feedback does not increase the ability of readers to differentiate true- from false-positive candidate lesions and to use CAD more effectively. • Computer-aided detection (CAD) is increasingly used as an adjunct for many radiological techniques. • Short-term feedback does not improve reader performance with CAD in chest radiography. • Differentiation between true- and false-positive CAD for low conspicious possible lesions proves difficult. • CAD can potentially increase reader performance for nodule detection in chest radiography.

A completely automated CAD system for mass detection in a large mammographic database.

PubMed

Bellotti, R; De Carlo, F; Tangaro, S; Gargano, G; Maggipinto, G; Castellano, M; Massafra, R; Cascio, D; Fauci, F; Magro, R; Raso, G; Lauria, A; Forni, G; Bagnasco, S; Cerello, P; Zanon, E; Cheran, S C; Lopez Torres, E; Bottigli, U; Masala, G L; Oliva, P; Retico, A; Fantacci, M E; Cataldo, R; De Mitri, I; De Nunzio, G

2006-08-01

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

Strategies for Improved Interpretation of Computer-Aided Detections for CT Colonography Utilizing Distributed Human Intelligence

PubMed Central

McKenna, Matthew T.; Wang, Shijun; Nguyen, Tan B.; Burns, Joseph E.; Petrick, Nicholas; Summers, Ronald M.

2012-01-01

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp • 6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for “easy” and “moderate” polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC. PMID:22705287

Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence.

PubMed

McKenna, Matthew T; Wang, Shijun; Nguyen, Tan B; Burns, Joseph E; Petrick, Nicholas; Summers, Ronald M

2012-08-01

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp ≥6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for "easy" and "moderate" polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC. Copyright © 2012. Published by Elsevier B.V.

Application of the Minkowski-functionals for automated pattern classification of breast parenchyma depicted by digital mammography

NASA Astrophysics Data System (ADS)

Boehm, Holger F.; Fischer, Tanja; Riosk, Dororthea; Britsch, Stefanie; Reiser, Maximilian

2008-03-01

With an estimated life-time-risk of about 10%, breast cancer is the most common cancer among women in western societies. Extensive mammography-screening programs have been implemented for diagnosis of the disease at an early stage. Several algorithms for computer-aided detection (CAD) have been proposed to help radiologists manage the increasing number of mammographic image-data and identify new cases of cancer. However, a major issue with most CAD-solutions is the fact that performance strongly depends on the structure and density of the breast tissue. Prior information about the global tissue quality in a patient would be helpful for selecting the most effective CAD-approach in order to increase the sensitivity of lesion-detection. In our study, we propose an automated method for textural evaluation of digital mammograms using the Minkowski Functionals in 2D. 80 mammograms are consensus-classified by two experienced readers as fibrosis, involution/atrophy, or normal. For each case, the topology of graylevel distribution is evaluated within a retromamillary image-section of 512 x 512 pixels. In addition, we obtain parameters from the graylevel-histogram (20th percentile, median and mean graylevel intensity). As a result, correct classification of the mammograms based on the densitometic parameters is achieved in between 38 and 48%, whereas topological analysis increases the rate to 83%. The findings demonstrate the effectiveness of the proposed algorithm. Compared to features obtained from graylevel histograms and comparable studies, we draw the conclusion that the presented method performs equally good or better. Our future work will be focused on the characterization of the mammographic tissue according to the Breast Imaging Reporting and Data System (BI-RADS). Moreover, other databases will be tested for an in-depth evaluation of the efficiency of our proposal.

A concurrent computer aided detection (CAD) tool for articular cartilage disease of the knee on MR imaging using active shape models

NASA Astrophysics Data System (ADS)

Ramakrishna, Bharath; Saiprasad, Ganesh; Safdar, Nabile; Siddiqui, Khan; Chang, Chein-I.; Siegel, Eliot

2008-03-01

Osteoarthritis (OA) is the most common form of arthritis and a major cause of morbidity affecting millions of adults in the US and world wide. In the knee, OA begins with the degeneration of joint articular cartilage, eventually resulting in the femur and tibia coming in contact, and leading to severe pain and stiffness. There has been extensive research examining 3D MR imaging sequences and automatic/semi-automatic techniques for 2D/3D articular cartilage extraction. However, in routine clinical practice the most popular technique still remain radiographic examination and qualitative assessment of the joint space. This may be in large part because of a lack of tools that can provide clinically relevant diagnosis in adjunct (in near real time fashion) with the radiologist and which can serve the needs of the radiologists and reduce inter-observer variation. Our work aims to fill this void by developing a CAD application that can generate clinically relevant diagnosis of the articular cartilage damage in near real time fashion. The algorithm features a 2D Active Shape Model (ASM) for modeling the bone-cartilage interface on all the slices of a Double Echo Steady State (DESS) MR sequence, followed by measurement of the cartilage thickness from the surface of the bone, and finally by the identification of regions of abnormal thinness and focal/degenerative lesions. A preliminary evaluation of CAD tool was carried out on 10 cases taken from the Osteoarthritis Initiative (OAI) database. When compared with 2 board-certified musculoskeletal radiologists, the automatic CAD application was able to get segmentation/thickness maps in little over 60 seconds for all of the cases. This observation poses interesting possibilities for increasing radiologist productivity and confidence, improving patient outcomes, and applying more sophisticated CAD algorithms to routine orthopedic imaging tasks.

Analog Computer-Aided Detection (CAD) information can be more effective than binary marks

PubMed Central

Cunningham, Corbin A.; Drew, Trafton; Wolfe, Jeremy M.

2017-01-01

In socially important visual search tasks such as baggage screening and diagnostic radiology, experts miss more targets than is desirable. Computer Aided Detection (CAD) programs have been developed specifically to help improve performance in these professional search tasks. For example, in breast cancer screening, many CAD systems are capable of detecting approximately 90% of breast cancer, with approximately 0.5 false positive detections per image. Nevertheless, benefits of CAD in clinical settings tend to be small (Birdwell, 2009) or even absent (Meziane et al., 2011; Philpotts, 2009). The marks made by a CAD system can be “Binary”, giving the same signal to any location where the signal is above some threshold. Alternatively, a CAD system present an Analog signal that reflected strength of the signal at a location. In the experiments reported here, we compare analog and binary CAD presentations using non-expert observers and artificial stimuli defined by two noisy signals: a visible color signal and an "invisible" signal that informed our simulated CAD system. We found that analog CAD generally yielded better overall performance than binary CAD. The analog benefit is similar at high and low target prevalence. Our data suggest that the form of the CAD signal can directly influence performance. Analog CAD may allow the computer to be more helpful to the searcher. PMID:27928658

Real-time slicing algorithm for Stereolithography (STL) CAD model applied in additive manufacturing industry

NASA Astrophysics Data System (ADS)

Adnan, F. A.; Romlay, F. R. M.; Shafiq, M.

2018-04-01

Owing to the advent of the industrial revolution 4.0, the need for further evaluating processes applied in the additive manufacturing application particularly the computational process for slicing is non-trivial. This paper evaluates a real-time slicing algorithm for slicing an STL formatted computer-aided design (CAD). A line-plane intersection equation was applied to perform the slicing procedure at any given height. The application of this algorithm has found to provide a better computational time regardless the number of facet in the STL model. The performance of this algorithm is evaluated by comparing the results of the computational time for different geometry.

On the Use of CAD-Native Predicates and Geometry in Surface Meshing

NASA Technical Reports Server (NTRS)

Aftosmis, M. J.

1999-01-01

Several paradigms for accessing CAD geometry during surface meshing for CFD are discussed. File translation, inconsistent geometry engines and non-native point construction are all identified as sources of non-robustness. The paper argues in favor of accessing CAD parts and assemblies in their native format, without translation, and for the use of CAD-native predicates and constructors in surface mesh generation. The discussion also emphasizes the importance of examining the computational requirements for exact evaluation of triangulation predicates during surface meshing. The native approach is demonstrated through an algorithm for the generation of closed manifold surface triangulations from CAD geometry. CAD parts and assemblies are used in their native format, and a part's native geometry engine is accessed through a modeler-independent application programming interface (API). In seeking a robust and fully automated procedure, the algorithm is based on a new physical space manifold triangulation technique specially developed to avoid robustness issues associated with poorly conditioned mappings. In addition, this approach avoids the usual ambiguities associated with floating-point predicate evaluation on constructed coordinate geometry in a mapped space. The technique is incremental, so that each new site improves the triangulation by some well defined quality measure. The algorithm terminates after achieving a prespecified measure of mesh quality and produces a triangulation such that no angle is less than a given angle bound, a or greater than pi - 2alpha. This result also sets bounds on the maximum vertex degree, triangle aspect-ratio and maximum stretching rate for the triangulation. In addition to the output triangulations for a variety of CAD parts, the discussion presents related theoretical results which assert the existence of such an angle bound, and demonstrate that maximum bounds of between 25 deg and 30 deg may be achieved in practice.

CAD system for automatic analysis of CT perfusion maps

NASA Astrophysics Data System (ADS)

Hachaj, T.; Ogiela, M. R.

2011-03-01

In this article, authors present novel algorithms developed for the computer-assisted diagnosis (CAD) system for analysis of dynamic brain perfusion, computer tomography (CT) maps, cerebral blood flow (CBF), and cerebral blood volume (CBV). Those methods perform both quantitative analysis [detection and measurement and description with brain anatomy atlas (AA) of potential asymmetries/lesions] and qualitative analysis (semantic interpretation of visualized symptoms). The semantic interpretation (decision about type of lesion: ischemic/hemorrhagic, is the brain tissue at risk of infraction or not) of visualized symptoms is done by, so-called, cognitive inference processes allowing for reasoning on character of pathological regions based on specialist image knowledge. The whole system is implemented in.NET platform (C# programming language) and can be used on any standard PC computer with.NET framework installed.

A new data integration approach for AutoCAD and GIS

NASA Astrophysics Data System (ADS)

Ye, Hongmei; Li, Yuhong; Wang, Cheng; Li, Lijun

2006-10-01

GIS has its advantages both on spatial data analysis and management, particularly on the geometric and attributive information management, which has also attracted lots attentions among researchers around world. AutoCAD plays more and more important roles as one of the main data sources of GIS. Various work and achievements can be found in the related literature. However, the conventional data integration from AutoCAD to GIS is time-consuming, which also can cause the information loss both in the geometric aspects and the attributive aspects for a large system. It is necessary and urgent to sort out new approach and algorithm for the efficient high-quality data integration. In this paper, a novel data integration approach from AutoCAD to GIS will be introduced based on the spatial data mining technique through the data structure analysis both in the AutoCAD and GIS. A practicable algorithm for the data conversion from CAD to GIS will be given as well. By a designed evaluation scheme, the accuracy of the conversion both in the geometric and the attributive information will be demonstrated. Finally, the validity and feasibility of the new approach will be shown by an experimental analysis.

Evaluation of computer-aided detection and diagnosis systems.

PubMed

Petrick, Nicholas; Sahiner, Berkman; Armato, Samuel G; Bert, Alberto; Correale, Loredana; Delsanto, Silvia; Freedman, Matthew T; Fryd, David; Gur, David; Hadjiiski, Lubomir; Huo, Zhimin; Jiang, Yulei; Morra, Lia; Paquerault, Sophie; Raykar, Vikas; Samuelson, Frank; Summers, Ronald M; Tourassi, Georgia; Yoshida, Hiroyuki; Zheng, Bin; Zhou, Chuan; Chan, Heang-Ping

2013-08-01

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and "best practices" for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.

Evaluation of computer-aided detection and diagnosis systemsa)

PubMed Central

Petrick, Nicholas; Sahiner, Berkman; Armato, Samuel G.; Bert, Alberto; Correale, Loredana; Delsanto, Silvia; Freedman, Matthew T.; Fryd, David; Gur, David; Hadjiiski, Lubomir; Huo, Zhimin; Jiang, Yulei; Morra, Lia; Paquerault, Sophie; Raykar, Vikas; Samuelson, Frank; Summers, Ronald M.; Tourassi, Georgia; Yoshida, Hiroyuki; Zheng, Bin; Zhou, Chuan; Chan, Heang-Ping

2013-01-01

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and “best practices” for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice. PMID:23927365

Aerodynamic Design of Complex Configurations Using Cartesian Methods and CAD Geometry

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.; Pulliam, Thomas H.

2003-01-01

The objective for this paper is to present the development of an optimization capability for the Cartesian inviscid-flow analysis package of Aftosmis et al. We evaluate and characterize the following modules within the new optimization framework: (1) A component-based geometry parameterization approach using a CAD solid representation and the CAPRI interface. (2) The use of Cartesian methods in the development Optimization techniques using a genetic algorithm. The discussion and investigations focus on several real world problems of the optimization process. We examine the architectural issues associated with the deployment of a CAD-based design approach in a heterogeneous parallel computing environment that contains both CAD workstations and dedicated compute nodes. In addition, we study the influence of noise on the performance of optimization techniques, and the overall efficiency of the optimization process for aerodynamic design of complex three-dimensional configurations. of automated optimization tools. rithm and a gradient-based algorithm.

Predicting coronary artery disease using different artificial neural network models.

PubMed

Colak, M Cengiz; Colak, Cemil; Kocatürk, Hasan; Sağiroğlu, Seref; Barutçu, Irfan

2008-08-01

Eight different learning algorithms used for creating artificial neural network (ANN) models and the different ANN models in the prediction of coronary artery disease (CAD) are introduced. This work was carried out as a retrospective case-control study. Overall, 124 consecutive patients who had been diagnosed with CAD by coronary angiography (at least 1 coronary stenosis > 50% in major epicardial arteries) were enrolled in the work. Angiographically, the 113 people (group 2) with normal coronary arteries were taken as control subjects. Multi-layered perceptrons ANN architecture were applied. The ANN models trained with different learning algorithms were performed in 237 records, divided into training (n=171) and testing (n=66) data sets. The performance of prediction was evaluated by sensitivity, specificity and accuracy values based on standard definitions. The results have demonstrated that ANN models trained with eight different learning algorithms are promising because of high (greater than 71%) sensitivity, specificity and accuracy values in the prediction of CAD. Accuracy, sensitivity and specificity values varied between 83.63%-100%, 86.46%-100% and 74.67%-100% for training, respectively. For testing, the values were more than 71% for sensitivity, 76% for specificity and 81% for accuracy. It may be proposed that the use of different learning algorithms other than backpropagation and larger sample sizes can improve the performance of prediction. The proposed ANN models trained with these learning algorithms could be used a promising approach for predicting CAD without the need for invasive diagnostic methods and could help in the prognostic clinical decision.

Pre-test probability of obstructive coronary stenosis in patients undergoing coronary CT angiography: Comparative performance of the modified diamond-Forrester algorithm versus methods incorporating cardiovascular risk factors.

PubMed

Ferreira, António Miguel; Marques, Hugo; Tralhão, António; Santos, Miguel Borges; Santos, Ana Rita; Cardoso, Gonçalo; Dores, Hélder; Carvalho, Maria Salomé; Madeira, Sérgio; Machado, Francisco Pereira; Cardim, Nuno; de Araújo Gonçalves, Pedro

2016-11-01

Current guidelines recommend the use of the Modified Diamond-Forrester (MDF) method to assess the pre-test likelihood of obstructive coronary artery disease (CAD). We aimed to compare the performance of the MDF method with two contemporary algorithms derived from multicenter trials that additionally incorporate cardiovascular risk factors: the calculator-based 'CAD Consortium 2' method, and the integer-based CONFIRM score. We assessed 1069 consecutive patients without known CAD undergoing coronary CT angiography (CCTA) for stable chest pain. Obstructive CAD was defined as the presence of coronary stenosis ≥50% on 64-slice dual-source CT. The three methods were assessed for calibration, discrimination, net reclassification, and changes in proposed downstream testing based upon calculated pre-test likelihoods. The observed prevalence of obstructive CAD was 13.8% (n=147). Overestimations of the likelihood of obstructive CAD were 140.1%, 9.8%, and 18.8%, respectively, for the MDF, CAD Consortium 2 and CONFIRM methods. The CAD Consortium 2 showed greater discriminative power than the MDF method, with a C-statistic of 0.73 vs. 0.70 (p<0.001), while the CONFIRM score did not (C-statistic 0.71, p=0.492). Reclassification of pre-test likelihood using the 'CAD Consortium 2' or CONFIRM scores resulted in a net reclassification improvement of 0.19 and 0.18, respectively, which would change the diagnostic strategy in approximately half of the patients. Newer risk factor-encompassing models allow for a more precise estimation of pre-test probabilities of obstructive CAD than the guideline-recommended MDF method. Adoption of these scores may improve disease prediction and change the diagnostic pathway in a significant proportion of patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Mammography screening using independent double reading with consensus: is there a potential benefit for computer-aided detection?

PubMed

Skaane, Per; Kshirsagar, Ashwini; Hofvind, Solveig; Jahr, Gunnar; Castellino, Ronald A

2012-04-01

Double reading improves the cancer detection rate in mammography screening. Single reading with computer-aided detection (CAD) has been considered to be an alternative to double reading. Little is known about the potential benefit of CAD in breast cancer screening with double reading. To compare prospective independent double reading of screen-film (SFM) and full-field digital (FFDM) mammography in population-based screening with retrospective standalone CAD performance on the baseline mammograms of the screen-detected cancers and subsequent cancers diagnosed during the follow-up period. The study had ethics committee approval. A 5-point rating scale for probability of cancer was used for 23,923 (SFM = 16,983; FFDM = 6940) screening mammograms. Of 208 evaluable cancers, 104 were screen-detected and 104 were subsequent (44 interval and 60 next screening round) cancers. Baseline mammograms of subsequent cancers were retrospectively classified in consensus without information about cancer location, histology, or CAD prompting as normal, non-specific minimal signs, significant minimal signs, and false-negatives. The baseline mammograms of the screen-detected cancers and subsequent cancers were evaluated by CAD. Significant minimal signs and false-negatives were considered 'actionable' and potentially diagnosable if correctly prompted by CAD. CAD correctly marked 94% (98/104) of the baseline mammograms of the screen-detected cancers (SFM = 95% [61/64]; FFDM = 93% [37/40]), including 96% (23/24) of those with discordant interpretations. Considering only those baseline examinations of subsequent cancers prospectively interpreted as normal and retrospectively categorized as 'actionable', CAD input at baseline screening had the potential to increase the cancer detection rate from 0.43% to 0.51% (P = 0.13); and to increase cancer detection by 16% ([104 + 17]/104) and decrease interval cancers by 20% (from 44 to 35). CAD may have the potential to increase cancer detection by up to 16%, and to reduce the number of interval cancers by up to 20% in SFM and FFDM screening programs using independent double reading with consensus review. The influence of true- and false-positive CAD marks on decision-making can, however, only be evaluated in a prospective clinical study.

Computed-aided diagnosis (CAD) in the detection of breast cancer.

PubMed

Dromain, C; Boyer, B; Ferré, R; Canale, S; Delaloge, S; Balleyguier, C

2013-03-01

Computer-aided detection (CAD) systems have been developed for interpretation to improve mammographic detection of breast cancer at screening by reducing the number of false-negative interpretation that can be caused by subtle findings, radiologist distraction and complex architecture. They use a digitized mammographic image that can be obtained from both screen-film mammography and full field digital mammography. Its performance in breast cancer detection is dependent on the performance of the CAD itself, the population to which it is applied and the radiologists who use it. There is a clear benefit to the use of CAD in less experienced radiologist and in detecting breast carcinomas presenting as microcalcifications. This review gives a detailed description CAD systems used in mammography and their performance in assistance of reading in screening mammography and as an alternative to double reading. Other CAD systems developed for MRI and ultrasound are also presented and discussed. Copyright © 2012. Published by Elsevier Ireland Ltd.

[Coronary artery disease and cardiac ischemic disease: two different pathologies with different diagnostic procedures].

PubMed

Vallejo, Enrique

2009-01-01

Coronary artery disease (CAD) remains the leading cause of death in the Western world, and early detection of CAD allows optimal therapeutic management. The gold standard has always been invasive coronary angiography, but over the years various non-invasive techniques have been developed to detect CAD, including cardiac SPECT and cardiac computed tomography (Cardiac CT). Cardiac SPECT permitted visualization of myocardial perfusion and have focused on the assessment of the hemodynamic consequences of obstructive coronary lesions as a marker of CAD. Cardiac CT focuses on the detection of atherosclerosis rather than ischemia, and permit detection of CAD at an earlier stage. Objectives of this manuscript are to discuss the clinical experience with both modalities and to provide a critical review of the strengths and limitations of Cardiac SPECT and Cardiac CT for the diagnostic and management of patients with suspected CAD or cardiac ischemic disease.

CT Colonography with Computer-aided Detection: Recognizing the Causes of False-Positive Reader Results

PubMed Central

Dachman, Abraham H.; Wroblewski, Kristen; Vannier, Michael W.; Horne, John M.

2014-01-01

Computed tomography (CT) colonography is a screening modality used to detect colonic polyps before they progress to colorectal cancer. Computer-aided detection (CAD) is designed to decrease errors of detection by finding and displaying polyp candidates for evaluation by the reader. CT colonography CAD false-positive results are common and have numerous causes. The relative frequency of CAD false-positive results and their effect on reader performance on the basis of a 19-reader, 100-case trial shows that the vast majority of CAD false-positive results were dismissed by readers. Many CAD false-positive results are easily disregarded, including those that result from coarse mucosa, reconstruction, peristalsis, motion, streak artifacts, diverticulum, rectal tubes, and lipomas. CAD false-positive results caused by haustral folds, extracolonic candidates, diminutive lesions (<6 mm), anal papillae, internal hemorrhoids, varices, extrinsic compression, and flexural pseudotumors are almost always recognized and disregarded. The ileocecal valve and tagged stool are common sources of CAD false-positive results associated with reader false-positive results. Nondismissable CAD soft-tissue polyp candidates larger than 6 mm are another common cause of reader false-positive results that may lead to further evaluation with follow-up CT colonography or optical colonoscopy. Strategies for correctly evaluating CAD polyp candidates are important to avoid pitfalls from common sources of CAD false-positive results. ©RSNA, 2014 PMID:25384290

Computer-aided detection (CAD) of breast cancer on full field digital and screening film mammograms

NASA Astrophysics Data System (ADS)

Sun, Xuejun; Qian, Wei; Song, Xiaoshan; Qian, Yuyan; Song, Dansheng; Clark, Robert A.

2003-05-01

Full-field digital mammography (FFDM) as a new breast imaging modality has potential to detect more breast cancers or to detect them at smaller sizes and earlier stages compared with screening film mammography (SFM). However, its performance needs verification, and it would pose new problems for the development of CAD methods for breast cancer detection and diagnosis. Performance evaluation of CAD systems on FFDM and SFM has been conducted in this study, respectively. First, an adaptive CAD system employing a series of advanced modules has been developed on FFDM. Second, a standardization approach has been developed to make the CAD system independent of characteristics of digitizer or imaging modalities for mammography. CAD systems developed previously for SFM and developed in this study for FFDM have been evaluated on FFDM and SFM images without and with standardization, respectively, to examine the performance improvement of the CAD system developed in this study. Computerized free-response receiver operating characteristic (FROC) analysis has been adopted as performance evaluation method. Compared with previous one, the CAD system developed in this study demonstrated significantly performance improvements. However, the comparison results have shown that the performances of final CAD system in this study are not significantly different on FFDM and on SFM after standardization. It needs further study on the assessment of CAD system performance on FFDM and SFM modalities.

An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

PubMed

Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

2014-01-01

The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

[Computed tomography with computer-assisted detection of pulmonary nodules in dogs and cats].

PubMed

Niesterok, C; Piesnack, S; Köhler, C; Ludewig, E; Alef, M; Kiefer, I

2015-01-01

The aim of this study was to assess the potential benefit of computer-assisted detection (CAD) of pulmonary nodules in veterinary medicine. Therefore, the CAD rate was compared to the detection rates of two individual examiners in terms of its sensitivity and false-positive findings. We included 51 dogs and 16 cats with pulmonary nodules previously diagnosed by computed tomography. First, the number of nodules ≥ 3 mm was recorded for each patient by two independent examiners. Subsequently, each examiner used the CAD software for automated nodule detection. With the knowledge of the CAD results, a final consensus decision on the number of nodules was achieved. The software used was a commercially available CAD program. The sensitivity of examiner 1 was 89.2%, while that of examiner 2 reached 87.4%. CAD had a sensitivity of 69.4%. With CAD, the sensitivity of examiner 1 increased to 94.7% and that of examiner 2 to 90.8%. The CAD-system, which we used in our study, had a moderate sensitivity of 69.4%. Despite its severe limitations, with a high level of false-positive and false-negative results, CAD increased the examiners' sensitivity. Therefore, its supportive role in diagnostics appears to be evident.

Path length entropy analysis of diastolic heart sounds.

PubMed

Griffel, Benjamin; Zia, Mohammad K; Fridman, Vladamir; Saponieri, Cesare; Semmlow, John L

2013-09-01

Early detection of coronary artery disease (CAD) using the acoustic approach, a noninvasive and cost-effective method, would greatly improve the outcome of CAD patients. To detect CAD, we analyze diastolic sounds for possible CAD murmurs. We observed diastolic sounds to exhibit 1/f structure and developed a new method, path length entropy (PLE) and a scaled version (SPLE), to characterize this structure to improve CAD detection. We compare SPLE results to Hurst exponent, Sample entropy and Multiscale entropy for distinguishing between normal and CAD patients. SPLE achieved a sensitivity-specificity of 80%-81%, the best of the tested methods. However, PLE and SPLE are not sufficient to prove nonlinearity, and evaluation using surrogate data suggests that our cardiovascular sound recordings do not contain significant nonlinear properties. Copyright © 2013 Elsevier Ltd. All rights reserved.

Path Length Entropy Analysis of Diastolic Heart Sounds

PubMed Central

Griffel, B.; Zia, M. K.; Fridman, V.; Saponieri, C.; Semmlow, J. L.

2013-01-01

Early detection of coronary artery disease (CAD) using the acoustic approach, a noninvasive and cost-effective method, would greatly improve the outcome of CAD patients. To detect CAD, we analyze diastolic sounds for possible CAD murmurs. We observed diastolic sounds to exhibit 1/f structure and developed a new method, path length entropy (PLE) and a scaled version (SPLE), to characterize this structure to improve CAD detection. We compare SPLE results to Hurst exponent, Sample entropy and Multi-scale entropy for distinguishing between normal and CAD patients. SPLE achieved a sensitivity-specificity of 80%–81%, the best of the tested methods. However, PLE and SPLE are not sufficient to prove nonlinearity, and evaluation using surrogate data suggests that our cardiovascular sound recordings do not contain significant nonlinear properties. PMID:23930808

Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT.

PubMed

Godoy, Myrna C B; Kim, Tae Jung; White, Charles S; Bogoni, Luca; de Groot, Patricia; Florin, Charles; Obuchowski, Nancy; Babb, James S; Salganicoff, Marcos; Naidich, David P; Anand, Vikram; Park, Sangmin; Vlahos, Ioannis; Ko, Jane P

2013-01-01

The objective of our study was to evaluate the impact of computer-aided detection (CAD) on the identification of subsolid and solid lung nodules on thin- and thick-section CT. For 46 chest CT examinations with ground-glass opacity (GGO) nodules, CAD marks computed using thin data were evaluated in two phases. First, four chest radiologists reviewed thin sections (reader(thin)) for nodules and subsequently CAD marks (reader(thin) + CAD(thin)). After 4 months, the same cases were reviewed on thick sections (reader(thick)) and subsequently with CAD marks (reader(thick) + CAD(thick)). Sensitivities were evaluated. Additionally, reader(thick) sensitivity with assessment of CAD marks on thin sections was estimated (reader(thick) + CAD(thin)). For 155 nodules (mean, 5.5 mm; range, 4.0-27.5 mm)-74 solid nodules, 22 part-solid (part-solid nodules), and 59 GGO nodules-CAD stand-alone sensitivity was 80%, 95%, and 71%, respectively, with three false-positives on average (0-12) per CT study. Reader(thin) + CAD(thin) sensitivities were higher than reader(thin) for solid nodules (82% vs 57%, p < 0.001), part-solid nodules (97% vs 81%, p = 0.0027), and GGO nodules (82% vs 69%, p < 0.001) for all readers (p < 0.001). Respective sensitivities for reader(thick), reader(thick) + CAD(thick), reader(thick) + CAD(thin) were 40%, 58% (p < 0.001), and 77% (p < 0.001) for solid nodules; 72%, 73% (p = 0.322), and 94% (p < 0.001) for part-solid nodules; and 53%, 58% (p = 0.008), and 79% (p < 0.001) for GGO nodules. For reader(thin), false-positives increased from 0.64 per case to 0.90 with CAD(thin) (p < 0.001) but not for reader(thick); false-positive rates were 1.17, 1.19, and 1.26 per case for reader(thick), reader(thick) + CAD(thick), and reader(thick) + CAD(thin), respectively. Detection of GGO nodules and solid nodules is significantly improved with CAD. When interpretation is performed on thick sections, the benefit is greater when CAD marks are reviewed on thin rather than thick sections.

Applying a 2D based CAD scheme for detecting micro-calcification clusters using digital breast tomosynthesis images: an assessment

NASA Astrophysics Data System (ADS)

Park, Sang Cheol; Zheng, Bin; Wang, Xiao-Hui; Gur, David

2008-03-01

Digital breast tomosynthesis (DBT) has emerged as a promising imaging modality for screening mammography. However, visually detecting micro-calcification clusters depicted on DBT images is a difficult task. Computer-aided detection (CAD) schemes for detecting micro-calcification clusters depicted on mammograms can achieve high performance and the use of CAD results can assist radiologists in detecting subtle micro-calcification clusters. In this study, we compared the performance of an available 2D based CAD scheme with one that includes a new grouping and scoring method when applied to both projection and reconstructed DBT images. We selected a dataset involving 96 DBT examinations acquired on 45 women. Each DBT image set included 11 low dose projection images and a varying number of reconstructed image slices ranging from 18 to 87. In this dataset 20 true-positive micro-calcification clusters were visually detected on the projection images and 40 were visually detected on the reconstructed images, respectively. We first applied the CAD scheme that was previously developed in our laboratory to the DBT dataset. We then tested a new grouping method that defines an independent cluster by grouping the same cluster detected on different projection or reconstructed images. We then compared four scoring methods to assess the CAD performance. The maximum sensitivity level observed for the different grouping and scoring methods were 70% and 88% for the projection and reconstructed images with a maximum false-positive rate of 4.0 and 15.9 per examination, respectively. This preliminary study demonstrates that (1) among the maximum, the minimum or the average CAD generated scores, using the maximum score of the grouped cluster regions achieved the highest performance level, (2) the histogram based scoring method is reasonably effective in reducing false-positive detections on the projection images but the overall CAD sensitivity is lower due to lower signal-to-noise ratio, and (3) CAD achieved higher sensitivity and higher false-positive rate (per examination) on the reconstructed images. We concluded that without changing the detection threshold or performing pre-filtering to possibly increase detection sensitivity, current CAD schemes developed and optimized for 2D mammograms perform relatively poorly and need to be re-optimized using DBT datasets and new grouping and scoring methods need to be incorporated into the schemes if these are to be used on the DBT examinations.

Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection.

PubMed

van Zelst, J C M; Tan, T; Platel, B; de Jong, M; Steenbakkers, A; Mourits, M; Grivegnee, A; Borelli, C; Karssemeijer, N; Mann, R M

2017-04-01

To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n=40) with >1year of follow up, benign (n=30) lesions that were either biopsied or remained stable, and malignant lesions (n=20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p=0.001). Sensitivity of all readers improved (range 5.2-10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4-5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

Computer-aided diagnosis and artificial intelligence in clinical imaging.

PubMed

Shiraishi, Junji; Li, Qiang; Appelbaum, Daniel; Doi, Kunio

2011-11-01

Computer-aided diagnosis (CAD) is rapidly entering the radiology mainstream. It has already become a part of the routine clinical work for the detection of breast cancer with mammograms. The computer output is used as a "second opinion" in assisting radiologists' image interpretations. The computer algorithm generally consists of several steps that may include image processing, image feature analysis, and data classification via the use of tools such as artificial neural networks (ANN). In this article, we will explore these and other current processes that have come to be referred to as "artificial intelligence." One element of CAD, temporal subtraction, has been applied for enhancing interval changes and for suppressing unchanged structures (eg, normal structures) between 2 successive radiologic images. To reduce misregistration artifacts on the temporal subtraction images, a nonlinear image warping technique for matching the previous image to the current one has been developed. Development of the temporal subtraction method originated with chest radiographs, with the method subsequently being applied to chest computed tomography (CT) and nuclear medicine bone scans. The usefulness of the temporal subtraction method for bone scans was demonstrated by an observer study in which reading times and diagnostic accuracy improved significantly. An additional prospective clinical study verified that the temporal subtraction image could be used as a "second opinion" by radiologists with negligible detrimental effects. ANN was first used in 1990 for computerized differential diagnosis of interstitial lung diseases in CAD. Since then, ANN has been widely used in CAD schemes for the detection and diagnosis of various diseases in different imaging modalities, including the differential diagnosis of lung nodules and interstitial lung diseases in chest radiography, CT, and position emission tomography/CT. It is likely that CAD will be integrated into picture archiving and communication systems and will become a standard of care for diagnostic examinations in daily clinical work. Copyright © 2011 Elsevier Inc. All rights reserved.

Hierarchical detection of red lesions in retinal images by multiscale correlation filtering

NASA Astrophysics Data System (ADS)

Zhang, Bob; Wu, Xiangqian; You, Jane; Li, Qin; Karray, Fakhri

2009-02-01

This paper presents an approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR) -- a common and severe complication of long-term diabetes which damages the retina and cause blindness. Since red lesions are regarded as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities in retinal images. In contrast to existing algorithms, a new approach based on Multiscale Correlation Filtering (MSCF) and dynamic thresholding is developed. This consists of two levels, Red Lesion Candidate Detection (coarse level) and True Red Lesion Detection (fine level). The approach was evaluated using data from Retinopathy On-line Challenge (ROC) competition website and we conclude our method to be effective and efficient.

Comparative performance analysis for computer aided lung nodule detection and segmentation on ultra-low-dose vs. standard-dose CT

NASA Astrophysics Data System (ADS)

Wiemker, Rafael; Rogalla, Patrik; Opfer, Roland; Ekin, Ahmet; Romano, Valentina; Bülow, Thomas

2006-03-01

The performance of computer aided lung nodule detection (CAD) and computer aided nodule volumetry is compared between standard-dose (70-100 mAs) and ultra-low-dose CT images (5-10 mAs). A direct quantitative performance comparison was possible, since for each patient both an ultra-low-dose and a standard-dose CT scan were acquired within the same examination session. The data sets were recorded with a multi-slice CT scanner at the Charite university hospital Berlin with 1 mm slice thickness. Our computer aided nodule detection and segmentation algorithms were deployed on both ultra-low-dose and standard-dose CT data without any dose-specific fine-tuning or preprocessing. As a reference standard 292 nodules from 20 patients were visually identified, each nodule both in ultra-low-dose and standard-dose data sets. The CAD performance was analyzed by virtue of multiple FROC curves for different lower thresholds of the nodule diameter. For nodules with a volume-equivalent diameter equal or larger than 4 mm (149 nodules pairs), we observed a detection rate of 88% at a median false positive rate of 2 per patient in standard-dose images, and 86% detection rate in ultra-low-dose images, also at 2 FPs per patient. Including even smaller nodules equal or larger than 2 mm (272 nodules pairs), we observed a detection rate of 86% in standard-dose images, and 84% detection rate in ultra-low-dose images, both at a rate of 5 FPs per patient. Moreover, we observed a correlation of 94% between the volume-equivalent nodule diameter as automatically measured on ultra-low-dose versus on standard-dose images, indicating that ultra-low-dose CT is also feasible for growth-rate assessment in follow-up examinations. The comparable performance of lung nodule CAD in ultra-low-dose and standard-dose images is of particular interest with respect to lung cancer screening of asymptomatic patients.

Efficient seeding and defragmentation of curvature streamlines for colonic polyp detection

NASA Astrophysics Data System (ADS)

Zhao, Lingxiao; Botha, Charl P.; Truyen, Roel; Vos, Frans M.; Post, Frits H.

2008-03-01

Many computer aided diagnosis (CAD) schemes have been developed for colon cancer detection using Virtual Colonoscopy (VC). In earlier work, we developed an automatic polyp detection method integrating flow visualization techniques, that forms part of the CAD functionality of an existing Virtual Colonoscopy pipeline. Curvature streamlines were used to characterize polyp surface shape. Features derived from curvature streamlines correlated highly with true polyp detections. During testing with a large number of patient data sets, we found that the correlation between streamline features and true polyps could be affected by noise and our streamline generation technique. The seeding and spacing constraints and CT noise could lead to streamline fragmentation, which reduced the discriminating power of our streamline features. In this paper, we present two major improvements of our curvature streamline generation. First, we adapted our streamline seeding strategy to the local surface properties and made the streamline generation faster. It generates a significantly smaller number of seeds but still results in a comparable and suitable streamline distribution. Second, based on our observation that longer streamlines are better surface shape descriptors, we improved our streamline tracing algorithm to produce longer streamlines. Our improved techniques are more effcient and also guide the streamline geometry to correspond better to colonic surface shape. These two adaptations support a robust and high correlation between our streamline features and true positive detections and lead to better polyp detection results.

Particle swarm optimization method for small retinal vessels detection on multiresolution fundus images.

PubMed

Khomri, Bilal; Christodoulidis, Argyrios; Djerou, Leila; Babahenini, Mohamed Chaouki; Cheriet, Farida

2018-05-01

Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high- and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination. The performance of the proposed method was evaluated on two low-resolution (DRIVE and STARE) and one high-resolution fundus (HRF) image datasets. The data include healthy (H) and diabetic retinopathy (DR) cases. The proposed approach improved the sensitivity rate against the MSLD by 4.7% for the DRIVE dataset and by 1.8% for the STARE dataset. For the high-resolution dataset, the proposed approach achieved 87.09% sensitivity rate, whereas the MSLD method achieves 82.58% sensitivity rate at the same specificity level. When only the smallest vessels were considered, the proposed approach improved the sensitivity rate by 11.02% and by 4.42% for the healthy and the diabetic cases, respectively. Integrating the proposed method in a comprehensive CAD system for DR screening would allow the reduction of false positives due to missed small vessels, misclassified as red lesions. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Computer-aided detection of early cancer in the esophagus using HD endoscopy images

NASA Astrophysics Data System (ADS)

van der Sommen, Fons; Zinger, Svitlana; Schoon, Erik J.; de With, Peter H. N.

2013-02-01

Esophageal cancer is the fastest rising type of cancer in the Western world. The recent development of High-Definition (HD) endoscopy has enabled the specialist physician to identify cancer at an early stage. Nevertheless, it still requires considerable effort and training to be able to recognize these irregularities associated with early cancer. As a first step towards a Computer-Aided Detection (CAD) system that supports the physician in finding these early stages of cancer, we propose an algorithm that is able to identify irregularities in the esophagus automatically, based on HD endoscopic images. The concept employs tile-based processing, so our system is not only able to identify that an endoscopic image contains early cancer, but it can also locate it. The identification is based on the following steps: (1) preprocessing, (2) feature extraction with dimensionality reduction, (3) classification. We evaluate the detection performance in RGB, HSI and YCbCr color space using the Color Histogram (CH) and Gabor features and we compare with other well-known features to describe texture. For classification, we employ a Support Vector Machine (SVM) and evaluate its performance using different parameters and kernel functions. In experiments, our system achieves a classification accuracy of 95.9% on 50×50 pixel tiles of tumorous and normal tissue and reaches an Area Under the Curve (AUC) of 0.990. In 22 clinical examples our algorithm was able to identify all (pre-)cancerous regions and annotate those regions reasonably well. The experimental and clinical validation are considered promising for a CAD system that supports the physician in finding early stage cancer.

A comparison of computer-assisted detection (CAD) programs for the identification of colorectal polyps: performance and sensitivity analysis, current limitations and practical tips for radiologists.

PubMed

Bell, L T O; Gandhi, S

2018-06-01

To directly compare the accuracy and speed of analysis of two commercially available computer-assisted detection (CAD) programs in detecting colorectal polyps. In this retrospective single-centre study, patients who had colorectal polyps identified on computed tomography colonography (CTC) and subsequent lower gastrointestinal endoscopy, were analysed using two commercially available CAD programs (CAD1 and CAD2). Results were compared against endoscopy to ascertain sensitivity and positive predictive value (PPV) for colorectal polyps. Time taken for CAD analysis was also calculated. CAD1 demonstrated a sensitivity of 89.8%, PPV of 17.6% and mean analysis time of 125.8 seconds. CAD2 demonstrated a sensitivity of 75.5%, PPV of 44.0% and mean analysis time of 84.6 seconds. The sensitivity and PPV for colorectal polyps and CAD analysis times can vary widely between current commercially available CAD programs. There is still room for improvement. Generally, there is a trade-off between sensitivity and PPV, and so further developments should aim to optimise both. Information on these factors should be made routinely available, so that an informed choice on their use can be made. This information could also potentially influence the radiologist's use of CAD results. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease.

PubMed

Small, Aeron M; Kiss, Daniel H; Zlatsin, Yevgeny; Birtwell, David L; Williams, Heather; Guerraty, Marie A; Han, Yuchi; Anwaruddin, Saif; Holmes, John H; Chirinos, Julio A; Wilensky, Robert L; Giri, Jay; Rader, Daniel J

2017-08-01

Interrogation of the electronic health record (EHR) using billing codes as a surrogate for diagnoses of interest has been widely used for clinical research. However, the accuracy of this methodology is variable, as it reflects billing codes rather than severity of disease, and depends on the disease and the accuracy of the coding practitioner. Systematic application of text mining to the EHR has had variable success for the detection of cardiovascular phenotypes. We hypothesize that the application of text mining algorithms to cardiovascular procedure reports may be a superior method to identify patients with cardiovascular conditions of interest. We adapted the Oracle product Endeca, which utilizes text mining to identify terms of interest from a NoSQL-like database, for purposes of searching cardiovascular procedure reports and termed the tool "PennSeek". We imported 282,569 echocardiography reports representing 81,164 individuals and 27,205 cardiac catheterization reports representing 14,567 individuals from non-searchable databases into PennSeek. We then applied clinical criteria to these reports in PennSeek to identify patients with trileaflet aortic stenosis (TAS) and coronary artery disease (CAD). Accuracy of patient identification by text mining through PennSeek was compared with ICD-9 billing codes. Text mining identified 7115 patients with TAS and 9247 patients with CAD. ICD-9 codes identified 8272 patients with TAS and 6913 patients with CAD. 4346 patients with AS and 6024 patients with CAD were identified by both approaches. A randomly selected sample of 200-250 patients uniquely identified by text mining was compared with 200-250 patients uniquely identified by billing codes for both diseases. We demonstrate that text mining was superior, with a positive predictive value (PPV) of 0.95 compared to 0.53 by ICD-9 for TAS, and a PPV of 0.97 compared to 0.86 for CAD. These results highlight the superiority of text mining algorithms applied to electronic cardiovascular procedure reports in the identification of phenotypes of interest for cardiovascular research. Copyright © 2017. Published by Elsevier Inc.

[In vitro study of joint intervention of E-cad and Bmi-1 mediated by transcription activator-like effector nuclease in nasopharyngeal carcinoma].

PubMed

Luo, Tingting; Yan, Aifen; Liu, Lian; Jiang, Hong; Feng, Cuilan; Liu, Guannan; Liu, Fang; Tang, Dongsheng; Zhou, Tianhong

2018-03-28

To explore the effect of intervention of E-cadherin (E-cad) and B-lymphoma Moloney murine leukemia virus insertion region-1 (Bmi-1) mediated by transcription activator-like effector nuclease (TALEN) on the biological behaviors of nasopharyngeal carcinoma cells.
 Methods: Multi-locus gene targeting vectors pUC-DS1-CMV-E-cad-2A-Neo-DS2 and pUC-DS1-Bmi-1 shRNA-Zeo-DS2 were constructed, and the E-cad and Bmi-1 targeting vectors were transferred with TALEN plasmids to CNE-2 cells individually or simultaneously. The integration of target genes were detected by PCR, the expressions of E-cad and Bmi-1 were detected by Western blot. The changes of cell proliferation were detected by cell counting kit-8 (CCK-8) assay. The cell cycle and apoptosis were detected by flow cytometry. The cell migration and invasion were detected by Transwell assay.
 Results: The E-cad and Bmi-1 shRNA expression elements were successfully integrated into the genome of CNE-2 cells, the protein expression level of E-cad was up-regulated, and the protein expression level of Bmi-1 was down-regulated. The intervention of E-cad and Bmi-1 didn't affect the proliferation, cell cycle and apoptosis of CNE-2 cells, but it significantly inhibited the migration and invasion ability of CNE-2 cells. Furthermore, the intervention of E-cad and Bmi-1 together significantly inhibited the migration ability of nasopharyngeal carcinoma cells compared with the intervention of E-cad or Bmi-1 alone (all P<0.01).
 Conclusion: The joint intervention of E-cad and Bmi-1 mediated by TALEN can effectively inhibit the migration and invasion of nasopharyngeal carcinoma cells in vitro, which may lay the preliminary experimental basis for gene therapy of human cancer.

Challenges facing developers of CAD/CAM models that seek to predict human working postures

NASA Astrophysics Data System (ADS)

Wiker, Steven F.

2005-11-01

This paper outlines the need for development of human posture prediction models for Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) design applications in product, facility and work design. Challenges facing developers of posture prediction algorithms are presented and discussed.

Automatic detection of lung vessel bifurcation in thoracic CT images

NASA Astrophysics Data System (ADS)

Maduskar, Pragnya; Vikal, Siddharth; Devarakota, Pandu

2011-03-01

Computer-aided diagnosis (CAD) systems for detection of lung nodules have been an active topic of research for last few years. It is desirable that a CAD system should generate very low false positives (FPs) while maintaining high sensitivity. This work aims to reduce the number of false positives occurring at vessel bifurcation point. FPs occur quite frequently on vessel branching point due to its shape which can appear locally spherical due to the intrinsic geometry of intersecting tubular vessel structures combined with partial volume effects and soft tissue attenuation appearance surrounded by parenchyma. We propose a model-based technique for detection of vessel branching points using skeletonization, followed by branch-point analysis. First we perform vessel structure enhancement using a multi-scale Hessian filter to accurately segment tubular structures of various sizes followed by thresholding to get binary vessel structure segmentation [6]. A modified Reebgraph [7] is applied next to extract the critical points of structure and these are joined by a nearest neighbor criterion to obtain complete skeletal model of vessel structure. Finally, the skeletal model is traversed to identify branch points, and extract metrics including individual branch length, number of branches and angle between various branches. Results on 80 sub-volumes consisting of 60 actual vessel-branching and 20 solitary solid nodules show that the algorithm identified correctly vessel branching points for 57 sub-volumes (95% sensitivity) and misclassified 2 nodules as vessel branch. Thus, this technique has potential in explicit identification of vessel branching points for general vessel analysis, and could be useful in false positive reduction in a lung CAD system.

A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.

PubMed

Timp, Sheila; Karssemeijer, Nico

2004-05-01

Mass segmentation plays a crucial role in computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique--based on dynamic programming--to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free-response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area Az under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in Az values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant.

Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images

NASA Astrophysics Data System (ADS)

Sánchez, Clara I.; Niemeijer, Meindert; Kockelkorn, Thessa; Abràmoff, Michael D.; van Ginneken, Bram

2009-02-01

Computer-aided Diagnosis (CAD) systems for the automatic identification of abnormalities in retinal images are gaining importance in diabetic retinopathy screening programs. A huge amount of retinal images are collected during these programs and they provide a starting point for the design of machine learning algorithms. However, manual annotations of retinal images are scarce and expensive to obtain. This paper proposes a dynamic CAD system based on active learning for the automatic identification of hard exudates, cotton wool spots and drusen in retinal images. An uncertainty sampling method is applied to select samples that need to be labeled by an expert from an unlabeled set of 4000 retinal images. It reduces the number of training samples needed to obtain an optimum accuracy by dynamically selecting the most informative samples. Results show that the proposed method increases the classification accuracy compared to alternative techniques, achieving an area under the ROC curve of 0.87, 0.82 and 0.78 for the detection of hard exudates, cotton wool spots and drusen, respectively.

Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks.

PubMed

Längkvist, Martin; Jendeberg, Johan; Thunberg, Per; Loutfi, Amy; Lidén, Mats

2018-06-01

Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Assessment of the Incremental Benefit of Computer-Aided Detection (CAD) for Interpretation of CT Colonography by Experienced and Inexperienced Readers

PubMed Central

Boone, Darren; Mallett, Susan; McQuillan, Justine; Taylor, Stuart A.; Altman, Douglas G.; Halligan, Steve

2015-01-01

Objectives To quantify the incremental benefit of computer-assisted-detection (CAD) for polyps, for inexperienced readers versus experienced readers of CT colonography. Methods 10 inexperienced and 16 experienced radiologists interpreted 102 colonography studies unassisted and with CAD utilised in a concurrent paradigm. They indicated any polyps detected on a study sheet. Readers’ interpretations were compared against a ground-truth reference standard: 46 studies were normal and 56 had at least one polyp (132 polyps in total). The primary study outcome was the difference in CAD net benefit (a combination of change in sensitivity and change in specificity with CAD, weighted towards sensitivity) for detection of patients with polyps. Results Inexperienced readers’ per-patient sensitivity rose from 39.1% to 53.2% with CAD and specificity fell from 94.1% to 88.0%, both statistically significant. Experienced readers’ sensitivity rose from 57.5% to 62.1% and specificity fell from 91.0% to 88.3%, both non-significant. Net benefit with CAD assistance was significant for inexperienced readers but not for experienced readers: 11.2% (95%CI 3.1% to 18.9%) versus 3.2% (95%CI -1.9% to 8.3%) respectively. Conclusions Concurrent CAD resulted in a significant net benefit when used by inexperienced readers to identify patients with polyps by CT colonography. The net benefit was nearly four times the magnitude of that observed for experienced readers. Experienced readers did not benefit significantly from concurrent CAD. PMID:26355745

Computer-aided diagnosis: A survey with bibliometric analysis.

PubMed

Takahashi, Ryohei; Kajikawa, Yuya

2017-05-01

Computer-aided diagnosis (CAD) has been a promising area of research over the last two decades. However, CAD is a very complicated subject because it involves a number of medicine and engineering-related fields. To develop a research overview of CAD, we conducted a literature survey with bibliometric analysis, which we report here. Our study determined that CAD research has been classified and categorized according to disease type and imaging modality. This classification began with the CAD of mammograms and eventually progressed to that of brain disease. Furthermore, based on our results, we discuss future directions and opportunities for CAD research. First, in contrast to the typical hypothetical approach, the data-driven approach has shown promise. Second, the normalization of the test datasets and an evaluation method is necessary when adopting an algorithm and a system. Third, we discuss opportunities for the co-evolution of CAD research and imaging instruments-for example, the CAD of bones and pancreatic cancer. Fourth, the potential of synergy with CAD and clinical decision support systems is also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

A computerized scheme for lung nodule detection in multiprojection chest radiography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo Wei; Li Qiang; Boyce, Sarah J.

2012-04-15

Purpose: Our previous study indicated that multiprojection chest radiography could significantly improve radiologists' performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme. Methods: Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification ofmore » initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. The integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave-one-subject-out testing method was used for evaluation of the performance levels of the two CAD schemes. Results: At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively. The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules. Conclusions: This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection.« less

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.

PubMed

Jalalian, Afsaneh; Mashohor, Syamsiah; Mahmud, Rozi; Karasfi, Babak; Saripan, M Iqbal B; Ramli, Abdul Rahman B

2017-01-01

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

PubMed Central

Jalalian, Afsaneh; Mashohor, Syamsiah; Mahmud, Rozi; Karasfi, Babak; Saripan, M. Iqbal B.; Ramli, Abdul Rahman B.

2017-01-01

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed. PMID:28435432

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

PubMed

Al-Masni, Mohammed A; Al-Antari, Mugahed A; Park, Jeong-Min; Gi, Geon; Kim, Tae-Yeon; Rivera, Patricio; Valarezo, Edwin; Choi, Mun-Taek; Han, Seung-Moo; Kim, Tae-Seong

2018-04-01

Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework. The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant. Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%. Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions. Copyright © 2018 Elsevier B.V. All rights reserved.

Applying a new unequally weighted feature fusion method to improve CAD performance of classifying breast lesions

NASA Astrophysics Data System (ADS)

Zargari Khuzani, Abolfazl; Danala, Gopichandh; Heidari, Morteza; Du, Yue; Mashhadi, Najmeh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Higher recall rates are a major challenge in mammography screening. Thus, developing computer-aided diagnosis (CAD) scheme to classify between malignant and benign breast lesions can play an important role to improve efficacy of mammography screening. Objective of this study is to develop and test a unique image feature fusion framework to improve performance in classifying suspicious mass-like breast lesions depicting on mammograms. The image dataset consists of 302 suspicious masses detected on both craniocaudal and mediolateral-oblique view images. Amongst them, 151 were malignant and 151 were benign. The study consists of following 3 image processing and feature analysis steps. First, an adaptive region growing segmentation algorithm was used to automatically segment mass regions. Second, a set of 70 image features related to spatial and frequency characteristics of mass regions were initially computed. Third, a generalized linear regression model (GLM) based machine learning classifier combined with a bat optimization algorithm was used to optimally fuse the selected image features based on predefined assessment performance index. An area under ROC curve (AUC) with was used as a performance assessment index. Applying CAD scheme to the testing dataset, AUC was 0.75+/-0.04, which was significantly higher than using a single best feature (AUC=0.69+/-0.05) or the classifier with equally weighted features (AUC=0.73+/-0.05). This study demonstrated that comparing to the conventional equal-weighted approach, using an unequal-weighted feature fusion approach had potential to significantly improve accuracy in classifying between malignant and benign breast masses.

Diagnostic power of longitudinal strain at rest for the detection of obstructive coronary artery disease in patients with type 2 diabetes mellitus.

PubMed

Zuo, Houjuan; Yan, Jiangtao; Zeng, Hesong; Li, Wenyu; Li, Pengcheng; Liu, Zhengxiang; Cui, Guanglin; Lv, Jiagao; Wang, Daowen; Wang, Hong

2015-01-01

Global longitudinal strain (GLS) measured by 2-D speckle-tracking echocardiography (2-D STE) at rest has been recognized as a sensitive parameter in the detection of significant coronary artery disease (CAD). However, the diagnostic power of 2-D STE in the detection of significant CAD in patients with diabetes mellitus is unknown. Two-dimensional STE features were studied in total of 143 consecutive patients who underwent echocardiography and coronary angiography. Left ventricular global and segmental peak systolic longitudinal strains (PSLSs) were quantified by speckle-tracking imaging. In the presence of obstructive CAD (defined as stenosis ≥75%), global PSLS was significantly lower in patients with diabetes mellitus than in patients without (16.65 ± 2.29% vs. 17.32 ± 2.27%, p < 0.05). Receiver operating characteristic analysis revealed that global PSLS could effectively detect obstructive CAD in patients without diabetes mellitus (cutoff value: -18.35%, sensitivity: 78.8%, specificity: 77.5%). However, global PSLS could detect obstructive CAD in diabetic patients at a lower cutoff value with inadequate sensitivity and specificity (cutoff value: -17.15%; sensitivity: 61.1%, specificity: 52.9%). In addition, the results for segmental PSLS were similar to those for global PSLS. In conclusion, global and segmental PSLSs at rest were significantly lower in patients with both obstructive CAD and diabetes mellitus than in patients with obstructive CAD only; thus, PSLSs at rest might not be a useful parameter in the detection of obstructive CAD in patients with diabetes mellitus. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density

PubMed Central

Kobayashi, Hajime; Ohkubo, Masaki; Narita, Akihiro; Marasinghe, Janaka C; Murao, Kohei; Matsumoto, Toru; Sone, Shusuke

2017-01-01

Objective: We propose the application of virtual nodules to evaluate the performance of computer-aided detection (CAD) of lung nodules in cancer screening using low-dose CT. Methods: The virtual nodules were generated based on the spatial resolution measured for a CT system used in an institution providing cancer screening and were fused into clinical lung images obtained at that institution, allowing site specificity. First, we validated virtual nodules as an alternative to artificial nodules inserted into a phantom. In addition, we compared the results of CAD analysis between the real nodules (n = 6) and the corresponding virtual nodules. Subsequently, virtual nodules of various sizes and contrasts between nodule density and background density (ΔCT) were inserted into clinical images (n = 10) and submitted for CAD analysis. Results: In the validation study, 46 of 48 virtual nodules had the same CAD results as artificial nodules (kappa coefficient = 0.913). Real nodules and the corresponding virtual nodules showed the same CAD results. The detection limits of the tested CAD system were determined in terms of size and density of peripheral lung nodules; we demonstrated that a nodule with a 5-mm diameter was detected when the nodule had a ΔCT > 220 HU. Conclusion: Virtual nodules are effective in evaluating CAD performance using site-specific scan/reconstruction conditions. Advances in knowledge: Virtual nodules can be an effective means of evaluating site-specific CAD performance. The methodology for guiding the detection limit for nodule size/density might be a useful evaluation strategy. PMID:27897029

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms.

PubMed

Tourassi, Georgia D; Harrawood, Brian; Singh, Swatee; Lo, Joseph Y; Floyd, Carey E

2007-01-01

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrieval precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tourassi, Georgia D.; Harrawood, Brian; Singh, Swatee

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrievalmore » precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.« less

CAD-Based Aerodynamic Design of Complex Configurations using a Cartesian Method

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.; Pulliam, Thomas H.

2003-01-01

A modular framework for aerodynamic optimization of complex geometries is developed. By working directly with a parametric CAD system, complex-geometry models are modified nnd tessellated in an automatic fashion. The use of a component-based Cartesian method significantly reduces the demands on the CAD system, and also provides for robust and efficient flowfield analysis. The optimization is controlled using either a genetic or quasi-Newton algorithm. Parallel efficiency of the framework is maintained even when subject to limited CAD resources by dynamically re-allocating the processors of the flow solver. Overall, the resulting framework can explore designs incorporating large shape modifications and changes in topology.

Excretion of anti-angiogenic proteins in patients with chronic allograft dysfunction.

PubMed

Moskowitz-Kassai, Eliza; Mackelaite, Lina; Chen, Jun; Patel, Kaushal; Dadhania, Darshana M; Gross, Steven S; Chander, Praveen; Delaney, Vera; Deng, Luqin; Chen, Ligong; Cui, Xiangqin; Suthanthiran, Manikkam; Goligorsky, Michael S

2012-02-01

We have recently documented the appearance of an anti-angiogenic peptide, endorepellin, in the urine of patients with chronic allograft dysfunction (CAD). Here, we analyzed using enzyme-linked immunosorbent assay the excretion of anti-angiogenic peptides endostatin, pigment epithelium-derived factor (PEDF) and Kruppel-like factor-2 (KLF-2), in healthy individuals, patients with stable graft function and patients with various degrees of CAD. In healthy subjects and patients with CAD-0, endostatin, PEDF and KLF-2 excretions were at the level of detection. In contrast, there were significant differences between the patients with CAD-3 and CAD-0, CAD-1 and healthy controls for endostatin and CAD-0 versus CAD-3 for PEDF, but no differences in KLF-2 excretion. Receiver operating characteristic (ROC) curve analyses demonstrated a highly discriminative profile for all three biomarkers: the combination of these parameters offered 83% sensitivity and 90% specificity in distinguishing CAD-0 from CAD-1-3. The quality of these potential biomarkers of CAD was, however, highest in discriminating CAD status in biopsy-proven cases and dropped when CAD-0 was diagnosed based on clinical criteria. In conclusion, these findings indicate the diagnostic potential of urinary detection of endostatin, PEDF and to lesser degree KLF-2 and suggest a mechanistic role played by anti-angiogenic substances in the developing vasculopathy and vascular rarefaction in patients with CAD.

The clinical evaluation of the CADence device in the acoustic detection of coronary artery disease.

PubMed

Thomas, Joseph L; Ridner, Michael; Cole, Jason H; Chambers, Jeffrey W; Bokhari, Sabahat; Yannopoulos, Demetris; Kern, Morton; Wilson, Robert F; Budoff, Matthew J

2018-06-23

The noninvasive detection of turbulent coronary flow may enable diagnosis of significant coronary artery disease (CAD) using novel sensor and analytic technology. Eligible patients (n = 1013) with chest pain and CAD risk factors undergoing nuclear stress testing were studied using the CADence (AUM Cardiovascular Inc., Northfield MN) acoustic detection (AD) system. The trial was designed to demonstrate non-inferiority of AD for diagnostic accuracy in detecting significant CAD as compared to an objective performance criteria (sensitivity 83% and specificity 80%, with 15% non-inferiority margins) for nuclear stress testing. AD analysis was blinded to clinical, core lab-adjudicated angiographic, and nuclear data. The presence of significant CAD was determined by computed tomographic (CCTA) or invasive angiography. A total of 1013 subjects without prior coronary revascularization or Q-wave myocardial infarction were enrolled. Primary analysis was performed on subjects with complete angiographic and AD data (n = 763) including 111 subjects (15%) with severe CAD based on CCTA (n = 34) and invasive angiography (n = 77). The sensitivity and specificity of AD were 78% (p = 0.012 for non-inferiority) and 35% (p < 0.001 for failure to demonstrate non-inferiority), respectively. AD results had a high 91% negative predictive value for the presence of significant CAD. AD testing failed to demonstrate non-inferior diagnostic accuracy as compared to the historical performance of a nuclear stress OPC due to low specificity. AD sensitivity was non-inferior in detecting significant CAD with a high negative predictive value supporting a potential value in excluding CAD.

Effectiveness of Computer-Aided Detection in Community Mammography Practice

PubMed Central

Abraham, Linn; Taplin, Stephen H.; Geller, Berta M.; Carney, Patricia A.; D’Orsi, Carl; Elmore, Joann G.; Barlow, William E.

2011-01-01

Background Computer-aided detection (CAD) is applied during screening mammography for millions of US women annually, although it is uncertain whether CAD improves breast cancer detection when used by community radiologists. Methods We investigated the association between CAD use during film-screen screening mammography and specificity, sensitivity, positive predictive value, cancer detection rates, and prognostic characteristics of breast cancers (stage, size, and node involvement). Records from 684 956 women who received more than 1.6 million film-screen mammograms at Breast Cancer Surveillance Consortium facilities in seven states in the United States from 1998 to 2006 were analyzed. We used random-effects logistic regression to estimate associations between CAD and specificity (true-negative examinations among women without breast cancer), sensitivity (true-positive examinations among women with breast cancer diagnosed within 1 year of mammography), and positive predictive value (breast cancer diagnosed after positive mammograms) while adjusting for mammography registry, patient age, time since previous mammography, breast density, use of hormone replacement therapy, and year of examination (1998–2002 vs 2003–2006). All statistical tests were two-sided. Results Of 90 total facilities, 25 (27.8%) adopted CAD and used it for an average of 27.5 study months. In adjusted analyses, CAD use was associated with statistically significantly lower specificity (OR = 0.87, 95% confidence interval [CI] = 0.85 to 0.89, P < .001) and positive predictive value (OR = 0.89, 95% CI = 0.80 to 0.99, P = .03). A non-statistically significant increase in overall sensitivity with CAD (OR = 1.06, 95% CI = 0.84 to 1.33, P = .62) was attributed to increased sensitivity for ductal carcinoma in situ (OR = 1.55, 95% CI = 0.83 to 2.91; P = .17), although sensitivity for invasive cancer was similar with or without CAD (OR = 0.96, 95% CI = 0.75 to 1.24; P = .77). CAD was not associated with higher breast cancer detection rates or more favorable stage, size, or lymph node status of invasive breast cancer. Conclusion CAD use during film-screen screening mammography in the United States is associated with decreased specificity but not with improvement in the detection rate or prognostic characteristics of invasive breast cancer. PMID:21795668

Potential effect of CAD systems on the detection of actionable nodules in chest CT scans during routine reporting

NASA Astrophysics Data System (ADS)

Wormanns, Dag; Beyer, Florian; Butzbach, Arnauld; Zierott, Livia; Heindel, Walter

2006-03-01

The purpose of the presented study was to determine the impact of two different CAD systems used as concur-rent reader for detection of actionable nodules (>4 mm) on the interpretation of chest CT scans during routine reporting. Fifty consecutive MDCT scans (1 mm or 1.25 mm slice thickness, 0.8 mm reconstruction increment) were se-lected from clinical routine. All cases were read by a resident and a staff radiologist, and a written report was available in the radiology information system (RIS). The RIS report mentioned at least one actionable pulmonary nodule in 18 cases (50%) and did not report any pulmonary nodule in the remaining 32 cases. Two different recent CAD systems were independently applied to the 50 CT scans as concurrent reader with two radiologists: Siemens LungCare NEV and MEDIAN CAD-Lung. Two radiologists independently reviewed the CAD results and determined if a CAD result was a true positive or a false positive finding. Patients were classified into two groups: in group A if at least one actionable nodule was detected and in group B if no actionable nodules were found. The effect of CAD on routine reporting was simulated as set union of the findings of routine reporting and CAD thus applying CAD as concurrent reader. According to the RIS report group A (patients with at least one actionable nodule) contained 18 cases (36% of all 50 cases), and group B contained 32 cases. Application of a CAD system as concurrent reader resulted in detec-tion of additional CT scans with actionable nodules and reclassification into group A in 16 resp. 18 cases (radi-ologist 1 resp. radiologist 2) with Siemens NEV and in 19 resp. 18 cases with MEDIAN CAD-Lung. In seven cases MEDIAN CAD-Lung and in four cases Siemens NEV reclassified a case into group A while the other CAD system missed the relevant finding. Sensitivity on a nodule (>4 mm) base was .45 for Siemens NEV and .55 for MEDIAN CAD-Lung; the difference was not yet significant (p=.077). In our study use of CAD as second reader in routine reporting doubled the percentage of patients with actionable nodules larger than 4 mm.

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

PubMed Central

Wang, Yu; Zhang, Yaonan; Yao, Zhaomin; Zhao, Ruixue; Zhou, Fengfeng

2016-01-01

Non-lethal macular diseases greatly impact patients’ life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples. PMID:28018716

Computer-aided detection in musculoskeletal projection radiography: A systematic review.

PubMed

Gundry, M; Knapp, K; Meertens, R; Meakin, J R

2018-05-01

To investigated the accuracy of computer-aided detection (CAD) software in musculoskeletal projection radiography via a systematic review. Following selection screening, eligible studies were assessed for bias, and had their study characteristics extracted resulting in 22 studies being included. Of these 22 three studies had tested their CAD software in a clinical setting; the first study investigated vertebral fractures, reporting a sensitivity score of 69.3% with CAD, compared to 59.8% sensitivity without CAD. The second study tested dental caries diagnosis producing a sensitivity score of 68.8% and specificity of 94.1% with CAD, compared to sensitivity of 39.3% and specificity of 96.7% without CAD. The third indicated osteoporotic cases based on CAD, resulting in 100% sensitivity and 81.3% specificity. The current evidence reported shows a lack of development into the clinical testing phase; however the research does show future promise in the variation of different CAD systems. Copyright © 2017 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images

NASA Astrophysics Data System (ADS)

Tsehay, Yohannes K.; Lay, Nathan S.; Roth, Holger R.; Wang, Xiaosong; Kwak, Jin Tae; Turkbey, Baris I.; Pinto, Peter A.; Wood, Brad J.; Summers, Ronald M.

2017-03-01

Prostate cancer (PCa) is the second most common cause of cancer related deaths in men. Multiparametric MRI (mpMRI) is the most accurate imaging method for PCa detection; however, it requires the expertise of experienced radiologists leading to inconsistency across readers of varying experience. To increase inter-reader agreement and sensitivity, we developed a computer-aided detection (CAD) system that can automatically detect lesions on mpMRI that readers can use as a reference. We investigated a convolutional neural network based deep-learing (DCNN) architecture to find an improved solution for PCa detection on mpMRI. We adopted a network architecture from a state-of-the-art edge detector that takes an image as an input and produces an image probability map. Two-fold cross validation along with a receiver operating characteristic (ROC) analysis and free-response ROC (FROC) were used to determine our deep-learning based prostate-CAD's (CADDL) performance. The efficacy was compared to an existing prostate CAD system that is based on hand-crafted features, which was evaluated on the same test-set. CADDL had an 86% detection rate at 20% false-positive rate while the top-down learning CAD had 80% detection rate at the same false-positive rate, which translated to 94% and 85% detection rate at 10 false-positives per patient on the FROC. A CNN based CAD is able to detect cancerous lesions on mpMRI of the prostate with results comparable to an existing prostate-CAD showing potential for further development.

Semiautomatic computer-aided classification of degenerative lumbar spine disease in magnetic resonance imaging.

PubMed

Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

2015-07-01

Computer-aided diagnosis (CAD) methods for detecting and classifying lumbar spine disease in Magnetic Resonance imaging (MRI) can assist radiologists to perform their decision-making tasks. In this paper, a CAD software has been developed able to classify and quantify spine disease (disc degeneration, herniation and spinal stenosis) in two-dimensional MRI. A set of 52 lumbar discs from 14 patients was used for training and 243 lumbar discs from 53 patients for testing in conventional two-dimensional MRI of the lumbar spine. To classify disc degeneration according to the gold standard, Pfirrmann classification, a method based on the measurement of disc signal intensity and structure was developed. A gradient Vector Flow algorithm was used to extract disc shape features and for detecting contour abnormalities. Also, a signal intensity method was used for segmenting and detecting spinal stenosis. Novel algorithms have also been developed to quantify the severity of these pathologies. Variability was evaluated by kappa (k) and intra-class correlation (ICC) statistics. Segmentation inaccuracy was below 1%. Almost perfect agreement, as measured by the k and ICC statistics, was obtained for all the analyzed pathologies: disc degeneration (k=0.81 with 95% CI=[0.75..0.88]) with a sensitivity of 95.8% and a specificity of 92.6%, disc herniation (k=0.94 with 95% CI=[0.87..1]) with a sensitivity of 60% and a specificity of 87.1%, categorical stenosis (k=0.94 with 95% CI=[0.90..0.98]) and quantitative stenosis (ICC=0.98 with 95% CI=[0.97..0.98]) with a sensitivity of 70% and a specificity of 81.7%. The proposed methods are reproducible and should be considered as a possible alternative when compared to reference standards. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical usea)

PubMed Central

Huo, Zhimin; Summers, Ronald M.; Paquerault, Sophie; Lo, Joseph; Hoffmeister, Jeffrey; Armato, Samuel G.; Freedman, Matthew T.; Lin, Jesse; Ben Lo, Shih-Chung; Petrick, Nicholas; Sahiner, Berkman; Fryd, David; Yoshida, Hiroyuki; Chan, Heang-Ping

2013-01-01

Computer-aided detection/diagnosis (CAD) is increasingly used for decision support by clinicians for detection and interpretation of diseases. However, there are no quality assurance (QA) requirements for CAD in clinical use at present. QA of CAD is important so that end users can be made aware of changes in CAD performance both due to intentional or unintentional causes. In addition, end-user training is critical to prevent improper use of CAD, which could potentially result in lower overall clinical performance. Research on QA of CAD and user training are limited to date. The purpose of this paper is to bring attention to these issues, inform the readers of the opinions of the members of the American Association of Physicists in Medicine (AAPM) CAD subcommittee, and thus stimulate further discussion in the CAD community on these topics. The recommendations in this paper are intended to be work items for AAPM task groups that will be formed to address QA and user training issues on CAD in the future. The work items may serve as a framework for the discussion and eventual design of detailed QA and training procedures for physicists and users of CAD. Some of the recommendations are considered by the subcommittee to be reasonably easy and practical and can be implemented immediately by the end users; others are considered to be “best practice” approaches, which may require significant effort, additional tools, and proper training to implement. The eventual standardization of the requirements of QA procedures for CAD will have to be determined through consensus from members of the CAD community, and user training may require support of professional societies. It is expected that high-quality CAD and proper use of CAD could allow these systems to achieve their true potential, thus benefiting both the patients and the clinicians, and may bring about more widespread clinical use of CAD for many other diseases and applications. It is hoped that the awareness of the need for appropriate CAD QA and user training will stimulate new ideas and approaches for implementing such procedures efficiently and effectively as well as funding opportunities to fulfill such critical efforts. PMID:23822459

Is computer aided detection (CAD) cost effective in screening mammography? A model based on the CADET II study

PubMed Central

2011-01-01

Background Single reading with computer aided detection (CAD) is an alternative to double reading for detecting cancer in screening mammograms. The aim of this study is to investigate whether the use of a single reader with CAD is more cost-effective than double reading. Methods Based on data from the CADET II study, the cost-effectiveness of single reading with CAD versus double reading was measured in terms of cost per cancer detected. Cost (Pound (£), year 2007/08) of single reading with CAD versus double reading was estimated assuming a health and social service perspective and a 7 year time horizon. As the equipment cost varies according to the unit size a separate analysis was conducted for high, average and low volume screening units. One-way sensitivity analyses were performed by varying the reading time, equipment and assessment cost, recall rate and reader qualification. Results CAD is cost increasing for all sizes of screening unit. The introduction of CAD is cost-increasing compared to double reading because the cost of CAD equipment, staff training and the higher assessment cost associated with CAD are greater than the saving in reading costs. The introduction of single reading with CAD, in place of double reading, would produce an additional cost of £227 and £253 per 1,000 women screened in high and average volume units respectively. In low volume screening units, the high cost of purchasing the equipment will results in an additional cost of £590 per 1,000 women screened. One-way sensitivity analysis showed that the factors having the greatest effect on the cost-effectiveness of CAD with single reading compared with double reading were the reading time and the reader's professional qualification (radiologist versus advanced practitioner). Conclusions Without improvements in CAD effectiveness (e.g. a decrease in the recall rate) CAD is unlikely to be a cost effective alternative to double reading for mammography screening in UK. This study provides updated estimates of CAD costs in a full-field digital system and assessment cost for women who are re-called after initial screening. However, the model is highly sensitive to various parameters e.g. reading time, reader qualification, and equipment cost. PMID:21241473

Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach

NASA Astrophysics Data System (ADS)

Litjens, G. J. S.; Barentsz, J. O.; Karssemeijer, N.; Huisman, H. J.

2012-03-01

MRI has shown to have great potential in prostate cancer localization and grading, but interpreting those exams requires expertise that is not widely available. Therefore, CAD applications are being developed to aid radiologists in detecting prostate cancer. Existing CAD applications focus on the prostate as a whole. However, in clinical practice transition zone cancer and peripheral zone cancer are considered to have different appearances. In this paper we present zone-specific CAD, in addition to an atlas based segmentation technique which includes zonal segmentation. Our CAD system consists of a detection and a classification stage. Prior to the detection stage the prostate is segmented into two zones. After segmentation features are extracted. Subsequently a likelihood map is generated on which local maxima detection is performed. For each local maximum a region is segmented. In the classification stage additional shape features are calculated, after which the regions are classified. Validation was performed on 288 data sets with MR-guided biopsy results as ground truth. Freeresponse Receiver Operating Characteristic (FROC) analysis was used for statistical evaluation. The difference between whole-prostate and zone-specific CAD was assessed using the difference between the FROCs. Our results show that evaluating the two zones separately results in an increase in performance compared to whole-prostate CAD. The FROC curves at .1, 1 and 3 false positives have a sensitivity of 0.0, 0.55 and 0.72 for whole-prostate and 0.08, 0.57 and 0.80 for zone-specific CAD. The FROC curve of the zone-specific CAD also showed significantly better performance overall (p < 0.05).

When and why might a Computer Aided Detection (CAD) system interfere with visual search? An eye-tracking study

PubMed Central

Drew, Trafton; Cunningham, Corbin; Wolfe, Jeremy

2012-01-01

Rational and Objectives Computer Aided Detection (CAD) systems are intended to improve performance. This study investigates how CAD might actually interfere with a visual search task. This is a laboratory study with implications for clinical use of CAD. Methods 47 naïve observers in two studies were asked to search for a target, embedded in 1/f2.4 noise while we monitored their eye-movements. For some observers, a CAD system marked 75% of targets and 10% of distractors while other observers completed the study without CAD. In Experiment 1, the CAD system’s primary function was to tell observers where the target might be. In Experiment 2, CAD provided information about target identity. Results In Experiment 1, there was a significant enhancement of observer sensitivity in the presence of CAD (t(22)=4.74, p<.001), but there was also a substantial cost. Targets that were not marked by the CAD system were missed more frequently than equivalent targets in No CAD blocks of the experiment (t(22)=7.02, p<.001). Experiment 2 showed no behavioral benefit from CAD, but also no significant cost on sensitivity to unmarked targets (t(22)=0.6, p=n.s.). Finally, in both experiments, CAD produced reliable changes in eye-movements: CAD observers examined a lower total percentage of the search area than the No CAD observers (Ex 1: t(48)=3.05, p<.005; Ex 2: t(50)=7.31, p<.001). Conclusions CAD signals do not combine with observers’ unaided performance in a straight-forward manner. CAD can engender a sense of certainty that can lead to incomplete search and elevated chances of missing unmarked stimuli. PMID:22958720

Feasibility and Safety of Evaluating Patients with Prior Coronary Artery Disease Using an Accelerated Diagnostic Algorithm in a Chest Pain Unit

PubMed Central

Goldkorn, Ronen; Goitein, Orly; Ben-Zekery, Sagit; Shlomo, Nir; Narodetsky, Michael; Livne, Moran; Sabbag, Avi; Asher, Elad; Matetzky, Shlomi

2016-01-01

An accelerated diagnostic protocol for evaluating low-risk patients with acute chest pain in a cardiologist-based chest pain unit (CPU) is widely employed today. However, limited data exist regarding the feasibility of such an algorithm for patients with a history of prior coronary artery disease (CAD). The aim of the current study was to assess the feasibility and safety of evaluating patients with a history of prior CAD using an accelerated diagnostic protocol. We evaluated 1,220 consecutive patients presenting with acute chest pain and hospitalized in our CPU. Patients were stratified according to whether they had a history of prior CAD or not. The primary composite outcome was defined as a composite of readmission due to chest pain, acute coronary syndrome, coronary revascularization, or death during a 60-day follow-up period. Overall, 268 (22%) patients had a history of prior CAD. Non-invasive evaluation was performed in 1,112 (91%) patients. While patients with a history of prior CAD had more comorbidities, the two study groups were similar regarding hospitalization rates (9% vs. 13%, p = 0.08), coronary angiography (13% vs. 11%, p = 0.41), and revascularization (6.5% vs. 5.7%, p = 0.8) performed during CPU evaluation. At 60-days the primary endpoint was observed in 12 (1.6%) and 6 (3.2%) patients without and with a history of prior CAD, respectively (p = 0.836). No mortalities were recorded. To conclude, Patients with a history of prior CAD can be expeditiously and safely evaluated using an accelerated diagnostic protocol in a CPU with outcomes not differing from patients without such a history. PMID:27669521

Diastolic stress echocardiography detects coronary artery disease in patients with asymptomatic type II diabetes.

PubMed

Paraskevaidis, Ioannis A; Tsougos, Elias; Panou, Fotios; Dagres, Nikolaos; Karatzas, Dimitrios; Boutati, Eleni; Varounis, Christos; Kremastinos, Dimitrios Th

2010-03-01

Diabetes mellitus is considered as an equivalent of coronary artery disease (CAD). Aim of the study was to investigate whether in asymptomatic patients with type II diabetes, diastolic stress echocardiography may represent an alternative tool for the detection of CAD. The study population consisted of 105 patients with diabetes mellitus (age 61+/-9 years, 26% female, duration of diabetes 37+/-14 months). We performed an exercise stress test, followed by an echo-study and a single-positron emission tomography. Coronary angiography was performed within 1 month. Coronary angiography revealed a coronary artery stenosis of at least 70% in 72 patients (69%, CAD group), while the remaining formed the non-CAD group. Exercise induced an increase of both E/E' lateral and septal ratios as well as their average in the CAD group and on the contrary a decrease of these ratios in the non-CAD group. Receiver operating curve analysis for discrimination between patients with and without obstructive CAD showed an optimal cut-off value of -0.0708 for the exercise-induced change of E/E' average (area under curve 0.892, P<0.001). Sensitivities of scintigraphy and of diastolic stress echocardiography for detection of CAD were 75.0 and 93.1%, respectively; specificity was 78.8% for both methods. In asymptomatic patients, sensitivities of scintigraphy and diastolic stress echocardiography were 76.9 and 92.3%; specificity of both was 80%. In patients with type II diabetes, diastolic stress echocardiography, by means of E/E' ratio exercise-induced changes, can be used for the diagnosis and severity of CAD and for the detection of occult myocardial ischemia.

Hierarchical planning for a surface mounting machine placement.

PubMed

Zeng, You-jiao; Ma, Deng-ze; Jin, Ye; Yan, Jun-qi

2004-11-01

For a surface mounting machine (SMM) in printed circuit board (PCB) assembly line, there are four problems, e.g. CAD data conversion, nozzle selection, feeder assignment and placement sequence determination. A hierarchical planning for them to maximize the throughput rate of an SMM is presented here. To minimize set-up time, a CAD data conversion system was first applied that could automatically generate the data for machine placement from CAD design data files. Then an effective nozzle selection approach implemented to minimize the time of nozzle changing. And then, to minimize picking time, an algorithm for feeder assignment was used to make picking multiple components simultaneously as much as possible. Finally, in order to shorten pick-and-place time, a heuristic algorithm was used to determine optimal component placement sequence according to the decided feeder positions. Experiments were conducted on a four head SMM. The experimental results were used to analyse the assembly line performance.

Computer-aided detection of colorectal polyps: can it improve sensitivity of less-experienced readers? Preliminary findings.

PubMed

Baker, Mark E; Bogoni, Luca; Obuchowski, Nancy A; Dass, Chandra; Kendzierski, Renee M; Remer, Erick M; Einstein, David M; Cathier, Pascal; Jerebko, Anna; Lakare, Sarang; Blum, Andrew; Caroline, Dina F; Macari, Michael

2007-10-01

To determine whether computer-aided detection (CAD) applied to computed tomographic (CT) colonography can help improve sensitivity of polyp detection by less-experienced radiologist readers, with colonoscopy or consensus used as the reference standard. The release of the CT colonographic studies was approved by the individual institutional review boards of each institution. Institutions from the United States were HIPAA compliant. Written informed consent was waived at all institutions. The CT colonographic studies in 30 patients from six institutions were collected; 24 images depicted at least one confirmed polyp 6 mm or larger (39 total polyps) and six depicted no polyps. By using an investigational software package, seven less-experienced readers from two institutions evaluated the CT colonographic images and marked or scored polyps by using a five-point scale before and after CAD. The time needed to interpret the CT colonographic findings without CAD and then to re-evaluate them with CAD was recorded. For each reader, the McNemar test, adjusted for clustered data, was used to compare sensitivities for readers without and with CAD; a Wilcoxon signed-rank test was used to analyze the number of false-positive results per patient. The average sensitivity of the seven readers for polyp detection was significantly improved with CAD-from 0.810 to 0.908 (P=.0152). The number of false-positive results per patient without and with CAD increased from 0.70 to 0.96 (95% confidence interval for the increase: -0.39, 0.91). The mean total time for the readings was 17 minutes 54 seconds; for interpretation of CT colonographic findings alone, the mean time was 14 minutes 16 seconds; and for review of CAD findings, the mean time was 3 minutes 38 seconds. Results of this feasibility study suggest that CAD for CT colonography significantly improves per-polyp detection for less-experienced readers. Copyright (c) RSNA, 2007.

Which test for CAD should be used in patients with left bundle branch block?

PubMed

Xu, Bo; Cremer, Paul; Jaber, Wael; Moir, Stuart; Harb, Serge C; Rodriguez, L Leonardo

2018-03-01

Exercise stress electrocardiography is unreliable as a test for obstructive coronary artery disease (CAD) if the patient has left bundle branch block. The authors provide an algorithm for using alternative tests: exercise stress echocardiography, dobutamine echocardiography, computed tomographic (CT) angiography, and nuclear myocardial perfusion imaging. Copyright © 2018 Cleveland Clinic.

Comparison between evaporative light scattering detection and charged aerosol detection for the analysis of saikosaponins.

PubMed

Eom, Han Young; Park, So-Young; Kim, Min Kyung; Suh, Joon Hyuk; Yeom, Hyesun; Min, Jung Won; Kim, Unyong; Lee, Jeongmi; Youm, Jeong-Rok; Han, Sang Beom

2010-06-25

Saikosaponins are triterpene saponins derived from the roots of Bupleurum falcatum L. (Umbelliferae), which has been traditionally used to treat fever, inflammation, liver diseases, and nephritis. It is difficult to analyze saikosaponins using HPLC-UV due to the lack of chromophores. Therefore, evaporative light scattering detection (ELSD) is used as a valuable alternative to UV detection. More recently, a charged aerosol detection (CAD) method has been developed to improve the sensitivity and reproducibility of ELSD. In this study, we compared CAD and ELSD methods in the simultaneous analysis of 10 saikosaponins, including saikosaponins-A, -B(1), -B(2), -B(3), -B(4), -C, -D, -G, -H and -I. A mixture of the 10 saikosaponins was injected into the Ascentis Express C18 column (100 mm x 4.6 mm, 2.7 microm) with gradient elution and detection with CAD and ELSD by splitting. We examined various factors that could affect the sensitivity of the detectors including various concentrations of additives, pH and flow rate of the mobile phase, purity of nitrogen gas and the CAD range. The sensitivity was determined based on the signal-to-noise ratio. The best sensitivity for CAD was achieved with 0.1 mM ammonium acetate at pH 4.0 in the mobile phase with a flow rate of 1.0 mL/min, and the CAD range at 100 pA, whereas that for ELSD was achieved with 0.01% acetic acid in the mobile phase with a flow rate at 0.8 mL/min. The purity of the nitrogen gas had only minor effects on the sensitivities of both detectors. Finally, the sensitivity for CAD was two to six times better than that of ELSD. Taken together, these results suggest that CAD provides a more sensitive analysis of the 10 saikosaponins than does ELSD. Copyright 2010 Elsevier B.V. All rights reserved.

Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.

PubMed

Hirose, Tomohiro; Nitta, Norihisa; Shiraishi, Junji; Nagatani, Yukihiro; Takahashi, Masashi; Murata, Kiyoshi

2008-12-01

The aim of this study was to evaluate the usefulness of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector-row computed tomography (MDCT) in terms of improvement in radiologists' diagnostic accuracy in detecting lung nodules, using jackknife free-response receiver-operating characteristic (JAFROC) analysis. Twenty-one patients (6 without and 15 with lung nodules) were selected randomly from 120 consecutive thoracic computed tomographic examinations. The gold standard for the presence or absence of nodules in the observer study was determined by consensus of two radiologists. Six expert radiologists participated in a free-response receiver operating characteristic study for the detection of lung nodules on MDCT, in which cases were interpreted first without and then with the output of CAD software. Radiologists were asked to indicate the locations of lung nodule candidates on the monitor with their confidence ratings for the presence of lung nodules. The performance of the CAD software indicated that the sensitivity in detecting lung nodules was 71.4%, with 0.95 false-positive results per case. When radiologists used the CAD software, the average sensitivity improved from 39.5% to 81.0%, with an increase in the average number of false-positive results from 0.14 to 0.89 per case. The average figure-of-merit values for the six radiologists were 0.390 without and 0.845 with the output of the CAD software, and there was a statistically significant difference (P < .0001) using the JAFROC analysis. The CAD software for the detection of lung nodules on MDCT has the potential to assist radiologists by increasing their accuracy.

Computerized breast cancer analysis system using three stage semi-supervised learning method.

PubMed

Sun, Wenqing; Tseng, Tzu-Liang Bill; Zhang, Jianying; Qian, Wei

2016-10-01

A large number of labeled medical image data is usually a requirement to train a well-performed computer-aided detection (CAD) system. But the process of data labeling is time consuming, and potential ethical and logistical problems may also present complications. As a result, incorporating unlabeled data into CAD system can be a feasible way to combat these obstacles. In this study we developed a three stage semi-supervised learning (SSL) scheme that combines a small amount of labeled data and larger amount of unlabeled data. The scheme was modified on our existing CAD system using the following three stages: data weighing, feature selection, and newly proposed dividing co-training data labeling algorithm. Global density asymmetry features were incorporated to the feature pool to reduce the false positive rate. Area under the curve (AUC) and accuracy were computed using 10 fold cross validation method to evaluate the performance of our CAD system. The image dataset includes mammograms from 400 women who underwent routine screening examinations, and each pair contains either two cranio-caudal (CC) or two mediolateral-oblique (MLO) view mammograms from the right and the left breasts. From these mammograms 512 regions were extracted and used in this study, and among them 90 regions were treated as labeled while the rest were treated as unlabeled. Using our proposed scheme, the highest AUC observed in our research was 0.841, which included the 90 labeled data and all the unlabeled data. It was 7.4% higher than using labeled data only. With the increasing amount of labeled data, AUC difference between using mixed data and using labeled data only reached its peak when the amount of labeled data was around 60. This study demonstrated that our proposed three stage semi-supervised learning can improve the CAD performance by incorporating unlabeled data. Using unlabeled data is promising in computerized cancer research and may have a significant impact for future CAD system applications. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automatic Generation of CFD-Ready Surface Triangulations from CAD Geometry

NASA Technical Reports Server (NTRS)

Aftosmis, M. J.; Delanaye, M.; Haimes, R.; Nixon, David (Technical Monitor)

1998-01-01

This paper presents an approach for the generation of closed manifold surface triangulations from CAD geometry. CAD parts and assemblies are used in their native format, without translation, and a part's native geometry engine is accessed through a modeler-independent application programming interface (API). In seeking a robust and fully automated procedure, the algorithm is based on a new physical space manifold triangulation technique which was developed to avoid robustness issues associated with poorly conditioned mappings. In addition, this approach avoids the usual ambiguities associated with floating-point predicate evaluation on constructed coordinate geometry in a mapped space, The technique is incremental, so that each new site improves the triangulation by some well defined quality measure. Sites are inserted using a variety of priority queues to ensure that new insertions will address the worst triangles first, As a result of this strategy, the algorithm will return its 'best' mesh for a given (prespecified) number of sites. Alternatively, the algorithm may be allowed to terminate naturally after achieving a prespecified measure of mesh quality. The resulting triangulations are 'CFD-ready' in that: (1) Edges match the underlying part model to within a specified tolerance. (2) Triangles on disjoint surfaces in close proximity have matching length-scales. (3) The algorithm produces a triangulation such that no angle is less than a given angle bound, alpha, or greater than Pi - 2alpha This result also sets bounds on the maximum vertex degree, triangle aspect-ratio and maximum stretching rate for the triangulation. In addition to tile output triangulations for a variety of CAD parts, tile discussion presents related theoretical results which assert the existence of such all angle bound, and demonstrate that maximum bounds of between 25 deg and 30 deg may be achieved in practice.

ADMultiImg: a novel missing modality transfer learning based CAD system for diagnosis of MCI due to AD using incomplete multi-modality imaging data

NASA Astrophysics Data System (ADS)

Liu, Xiaonan; Chen, Kewei; Wu, Teresa; Weidman, David; Lure, Fleming; Li, Jing

2018-02-01

Alzheimer's Disease (AD) is the most common cause of dementia and currently has no cure. Treatments targeting early stages of AD such as Mild Cognitive Impairment (MCI) may be most effective to deaccelerate AD, thus attracting increasing attention. However, MCI has substantial heterogeneity in that it can be caused by various underlying conditions, not only AD. To detect MCI due to AD, NIA-AA published updated consensus criteria in 2011, in which the use of multi-modality images was highlighted as one of the most promising methods. It is of great interest to develop a CAD system based on automatic, quantitative analysis of multi-modality images and machine learning algorithms to help physicians more adequately diagnose MCI due to AD. The challenge, however, is that multi-modality images are not universally available for many patients due to cost, access, safety, and lack of consent. We developed a novel Missing Modality Transfer Learning (MMTL) algorithm capable of utilizing whatever imaging modalities are available for an MCI patient to diagnose the patient's likelihood of MCI due to AD. Furthermore, we integrated MMTL with radiomics steps including image processing, feature extraction, and feature screening, and a post-processing for uncertainty quantification (UQ), and developed a CAD system called "ADMultiImg" to assist clinical diagnosis of MCI due to AD using multi-modality images together with patient demographic and genetic information. Tested on ADNI date, our system can generate a diagnosis with high accuracy even for patients with only partially available image modalities (AUC=0.94), and therefore may have broad clinical utility.

Improving digital breast tomosynthesis reading time: A pilot multi-reader, multi-case study using concurrent Computer-Aided Detection (CAD).

PubMed

Balleyguier, Corinne; Arfi-Rouche, Julia; Levy, Laurent; Toubiana, Patrick R; Cohen-Scali, Franck; Toledano, Alicia Y; Boyer, Bruno

2017-12-01

Evaluate concurrent Computer-Aided Detection (CAD) with Digital Breast Tomosynthesis (DBT) to determine impact on radiologist performance and reading time. The CAD system detects and extracts suspicious masses, architectural distortions and asymmetries from DBT planes that are blended into corresponding synthetic images to form CAD-enhanced synthetic images. Review of CAD-enhanced images and navigation to corresponding planes to confirm or dismiss potential lesions allows radiologists to more quickly review DBT planes. A retrospective, crossover study with and without CAD was conducted with six radiologists who read an enriched sample of 80 DBT cases including 23 malignant lesions in 21 women. Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) compared the readings with and without CAD to determine the effect of CAD on overall interpretation performance. Sensitivity, specificity, recall rate and reading time were also assessed. Multi-reader, multi-case (MRMC) methods accounting for correlation and requiring correct lesion localization were used to analyze all endpoints. AUCs were based on a 0-100% probability of malignancy (POM) score. Sensitivity and specificity were based on BI-RADS scores, where 3 or higher was positive. Average AUC across readers without CAD was 0.854 (range: 0.785-0.891, 95% confidence interval (CI): 0.769,0.939) and 0.850 (range: 0.746-0.905, 95% CI: 0.751,0.949) with CAD (95% CI for difference: -0.046,0.039), demonstrating non-inferiority of AUC. Average reduction in reading time with CAD was 23.5% (95% CI: 7.0-37.0% improvement), from an average 48.2 (95% CI: 39.1,59.6) seconds without CAD to 39.1 (95% CI: 26.2,54.5) seconds with CAD. Per-patient sensitivity was the same with and without CAD (0.865; 95% CI for difference: -0.070,0.070), and there was a small 0.022 improvement (95% CI for difference: -0.046,0.089) in per-lesion sensitivity from 0.790 without CAD to 0.812 with CAD. A slight reduction in specificity with a -0.014 difference (95% CI for difference: -0.079,0.050) and a small 0.025 increase (95% CI for difference: -0.036,0.087) in recall rate in non-cancer cases were observed with CAD. Concurrent CAD resulted in faster reading time with non-inferiority of radiologist interpretation performance. Radiologist sensitivity, specificity and recall rate were similar with and without CAD. Copyright © 2017 Elsevier B.V. All rights reserved.

DFX via the Internet

NASA Astrophysics Data System (ADS)

Wagner, Rick; Castanotto, Giuseppe; Goldberg, Kenneth A.

1995-11-01

The Internet offers tremendous potential for rapid development of mechanical products to meet global competition. In the past several years, a number of geometric algorithms have been developed to evaluate manufacturing properties such as feedability, fixturability, assemblability, etc. This class of algorithms is sometimes termed `DFX: Design for X'. One problem is that most of these algorithms are tailored to a particular CAD system and format and so have not been widely tested by industry. the World Wide Web may offer a solution: its simple interface language may become a de facto standard for the exchange of geometric data. In this preliminary paper we describe one model for remote analysis of CAD models that we believe holds promise for use in industry (e.g. during the design cycle) and in research (e.g. to encourage verification of results).

Chronic obstructive pulmonary disease and coronary disease: COPDCoRi, a simple and effective algorithm for predicting the risk of coronary artery disease in COPD patients.

PubMed

Cazzola, Mario; Calzetta, Luigino; Matera, Maria Gabriella; Muscoli, Saverio; Rogliani, Paola; Romeo, Francesco

2015-08-01

Chronic obstructive pulmonary disease (COPD) is often associated with cardiovascular artery disease (CAD), representing a potential and independent risk factor for cardiovascular morbidity. Therefore, the aim of this study was to identify an algorithm for predicting the risk of CAD in COPD patients. We analyzed data of patients afferent to the Cardiology ward and the Respiratory Diseases outpatient clinic of Tor Vergata University (2010-2012, 1596 records). The study population was clustered as training population (COPD patients undergoing coronary arteriography), control population (non-COPD patients undergoing coronary arteriography), test population (COPD patients whose records reported information on the coronary status). The predicting model was built via causal relationship between variables, stepwise binary logistic regression and Hosmer-Lemeshow analysis. The algorithm was validated via split-sample validation method and receiver operating characteristics (ROC) curve analysis. The diagnostic accuracy was assessed. In training population the variables gender (men/women OR: 1.7, 95%CI: 1.237-2.5, P < 0.05), dyslipidemia (OR: 1.8, 95%CI: 1.2-2.5, P < 0.01) and smoking habit (OR: 1.5, 95%CI: 1.2-1.9, P < 0.001) were significantly associated with CAD in COPD patients, whereas in control population also age and diabetes were correlated. The stepwise binary logistic regressions permitted to build a well fitting predictive model for training population but not for control population. The predictive algorithm shown a diagnostic accuracy of 81.5% (95%CI: 77.78-84.71) and an AUC of 0.81 (95%CI: 0.78-0.85) for the validation set. The proposed algorithm is effective for predicting the risk of CAD in COPD patients via a rapid, inexpensive and non-invasive approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

CT colonography: investigation of the optimum reader paradigm by using computer-aided detection software.

PubMed

Taylor, Stuart A; Charman, Susan C; Lefere, Philippe; McFarland, Elizabeth G; Paulson, Erik K; Yee, Judy; Aslam, Rizwan; Barlow, John M; Gupta, Arun; Kim, David H; Miller, Chad M; Halligan, Steve

2008-02-01

To prospectively compare the diagnostic performance and time efficiency of both second and concurrent computer-aided detection (CAD) reading paradigms for retrospectively obtained computed tomographic (CT) colonography data sets by using consensus reading (three radiologists) of colonoscopic findings as a reference standard. Ethical permission, HIPAA compliance (for U.S. institutions), and patient consent were obtained from all institutions for use of CT colonography data sets in this study. Ten radiologists each read 25 CT colonography data sets (12 men, 13 women; mean age, 61 years) containing 69 polyps (28 were 1-5 mm, 41 were >or=6 mm) by using workstations integrated with CAD software. Reading was randomized to either "second read" CAD (applied only after initial unassisted assessment) or "concurrent read" CAD (applied at the start of assessment). Data sets were reread 6 weeks later by using the opposing paradigm. Polyp sensitivity and reading times were compared by using multilevel logistic and linear regression, respectively. Receiver operating characteristic (ROC) curves were generated. Compared with the unassisted read, odds of improved polyp (>or=6 mm) detection were 1.5 (95% confidence interval [CI]: 1.0, 2.2) and 1.3 (95% CI: 0.9, 1.9) by using CAD as second and concurrent reader, respectively. Detection odds by using CAD concurrently were 0.87 (95% CI: 0.59, 1.3) and 0.76 (95% CI: 0.57, 1.01) those of second read CAD, excluding and including polyps 1-5 mm, respectively. The concurrent read took 2.9 minutes (95% CI: -3.8, -1.9) less than did second read. The mean areas under the ROC curve (95% CI) for the unassisted read, second read CAD, and concurrent read CAD were 0.83 (95% CI: 0.78, 0.87), 0.86 (95% CI: 0.82, 0.90), and 0.88 (95% CI: 0.83, 0.92), respectively. CAD is more time efficient when used concurrently than when used as a second reader, with similar sensitivity for polyps 6 mm or larger. However, use of second read CAD maximizes sensitivity, particularly for smaller lesions. (c) RSNA, 2007.

Detecting Anomalous Insiders in Collaborative Information Systems

PubMed Central

Chen, You; Nyemba, Steve; Malin, Bradley

2012-01-01

Collaborative information systems (CISs) are deployed within a diverse array of environments that manage sensitive information. Current security mechanisms detect insider threats, but they are ill-suited to monitor systems in which users function in dynamic teams. In this paper, we introduce the community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on the access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to form community structures based on the subjects accessed (e.g., patients’ records viewed by healthcare providers). CADS consists of two components: 1) relational pattern extraction, which derives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users have sufficiently deviated from communities. We further extend CADS into MetaCADS to account for the semantics of subjects (e.g., patients’ diagnoses). To empirically evaluate the framework, we perform an assessment with three months of access logs from a real electronic health record (EHR) system in a large medical center. The results illustrate our models exhibit significant performance gains over state-of-the-art competitors. When the number of illicit users is low, MetaCADS is the best model, but as the number grows, commonly accessed semantics lead to hiding in a crowd, such that CADS is more prudent. PMID:24489520

Lung Nodule Detection via Deep Reinforcement Learning.

PubMed

Ali, Issa; Hart, Gregory R; Gunabushanam, Gowthaman; Liang, Ying; Muhammad, Wazir; Nartowt, Bradley; Kane, Michael; Ma, Xiaomei; Deng, Jun

2018-01-01

Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.

Lung Nodule Detection via Deep Reinforcement Learning

PubMed Central

Ali, Issa; Hart, Gregory R.; Gunabushanam, Gowthaman; Liang, Ying; Muhammad, Wazir; Nartowt, Bradley; Kane, Michael; Ma, Xiaomei; Deng, Jun

2018-01-01

Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures. PMID:29713615

A new CAD approach for improving efficacy of cancer screening

NASA Astrophysics Data System (ADS)

Zheng, Bin; Qian, Wei; Li, Lihua; Pu, Jiantao; Kang, Yan; Lure, Fleming; Tan, Maxine; Qiu, Yuchen

2015-03-01

Since performance and clinical utility of current computer-aided detection (CAD) schemes of detecting and classifying soft tissue lesions (e.g., breast masses and lung nodules) is not satisfactory, many researchers in CAD field call for new CAD research ideas and approaches. The purpose of presenting this opinion paper is to share our vision and stimulate more discussions of how to overcome or compensate the limitation of current lesion-detection based CAD schemes in the CAD research community. Since based on our observation that analyzing global image information plays an important role in radiologists' decision making, we hypothesized that using the targeted quantitative image features computed from global images could also provide highly discriminatory power, which are supplementary to the lesion-based information. To test our hypothesis, we recently performed a number of independent studies. Based on our published preliminary study results, we demonstrated that global mammographic image features and background parenchymal enhancement of breast MR images carried useful information to (1) predict near-term breast cancer risk based on negative screening mammograms, (2) distinguish between true- and false-positive recalls in mammography screening examinations, and (3) classify between malignant and benign breast MR examinations. The global case-based CAD scheme only warns a risk level of the cases without cueing a large number of false-positive lesions. It can also be applied to guide lesion-based CAD cueing to reduce false-positives but enhance clinically relevant true-positive cueing. However, before such a new CAD approach is clinically acceptable, more work is needed to optimize not only the scheme performance but also how to integrate with lesion-based CAD schemes in the clinical practice.

Interfacing WIPL-D with Mechanical CAD Software

NASA Technical Reports Server (NTRS)

Bliznyuk, Nataliya; Janic, Bojan

2007-01-01

of almost any popular CAD format, e.g. IGES, Parasolid, DXF, ACIS etc. The solid models are processed (simplified) and meshed in GiD(R), and then converted into WIPL-D Pro input file by simple Fortran or Matlab code. This algorithm allows the user to control the mesh of imported geometry, and to assign electric pperties to metalic and dielectric surfaces. Implementation of the algorithm is demonstrated by examples obtained from the NASA Discovery mission, Phoenix Lander 2008. Results for radiation pattern of Phoenix Lander UHF relay antenna with effect of Martian surface, both simulated in WIPL-D Pro and measured, are shown for comparison.

Angiographic findings and clinical outcomes in asymptomatic patients with severe obstructive atherosclerosis on computed tomography angiography.

PubMed

Kornowski, Ran; Bachar, Gil N; Dvir, Danny; Fuchs, Shmuel; Atar, Eli

2008-01-01

Cardiac computed tomography angiography is a relatively new imaging modality to detect coronary atherosclerosis. To explore the diagnostic value of CTA in assessing coronary artery disease among asymptomatic patients. In this retrospective single-centered analysis, 622 consecutive patients underwent CTA of coronary arteries between November 2004 and May 2006 at the Mor Institute for Cardiovascular Imaging in Bnei Brak, Israel. All patients were asymptomatic but had at least one risk factor for atherosclerotic CAD. The initial 244 patients were examined with the 16-slice Brilliance CT scanner (Philips, Cleveland, OH, U.S.A.), and in the remaining 378 patients the 64-slice scanner (GE Healthcare, The Netherlands) with dedicated cardiac reconstruction software and electrocardiography triggering was used. Scanning was performed in the cranio-caudal direction. Images reconstructed in different phases of the cardiac cycle using a retrospective ECG-gated reconstruction algorithm were transferred to a dedicated workstation for review by experienced CT radiologists and cardiologists. Of 622 patients, 52 (8.4%) had severe obstructive atherosclerosis (suspected > or = 75% stenosis) according to CTA interpretation. Invasive coronary angiography was performed in 48 patients while 4 patients had no further procedure. A non-significant CAD (e.g., diameter stenosis < 70%) was identified in 6 of 48 patients (12%) by selective coronary angiography. Forty-two patients showed severe CAD with at least one lesion of 70% stenosis. Percutaneous coronary intervention was performed in 35 patients and coronary artery bypass grafting surgery in the other 4 patients. Angioplasty procedures were successful in all 35 patients and stents were utilized in all cases without complications. No further complications occurred among the study cohort undergoing either PCI or surgery. The 6 month survival rate in these patients was 100%. Non-invasive coronary CTA appears to be a reliable technique, with reasonably high accuracy, to detect obstructive atherosclerosis in asymptomatic high risk patients for atherosclerotic CAD.

Diagnostic accuracy of second-generation dual-source computed tomography coronary angiography with iterative reconstructions: a real-world experience.

PubMed

Maffei, E; Martini, C; Rossi, A; Mollet, N; Lario, C; Castiglione Morelli, M; Clemente, A; Gentile, G; Arcadi, T; Seitun, S; Catalano, O; Aldrovandi, A; Cademartiri, F

2012-08-01

The authors evaluated the diagnostic accuracy of second-generation dual-source (DSCT) computed tomography coronary angiography (CTCA) with iterative reconstructions for detecting obstructive coronary artery disease (CAD). Between June 2010 and February 2011, we enrolled 160 patients (85 men; mean age 61.2±11.6 years) with suspected CAD. All patients underwent CTCA and conventional coronary angiography (CCA). For the CTCA scan (Definition Flash, Siemens), we use prospective tube current modulation and 70-100 ml of iodinated contrast material (Iomeprol 400 mgI/ ml, Bracco). Data sets were reconstructed with iterative reconstruction algorithm (IRIS, Siemens). CTCA and CCA reports were used to evaluate accuracy using the threshold for significant stenosis at ≥50% and ≥70%, respectively. No patient was excluded from the analysis. Heart rate was 64.3±11.9 bpm and radiation dose was 7.2±2.1 mSv. Disease prevalence was 30% (48/160). Sensitivity, specificity and positive and negative predictive values of CTCA in detecting significant stenosis were 90.1%, 93.3%, 53.2% and 99.1% (per segment), 97.5%, 91.2%, 61.4% and 99.6% (per vessel) and 100%, 83%, 71.6% and 100% (per patient), respectively. Positive and negative likelihood ratios at the per-patient level were 5.89 and 0.0, respectively. CTCA with second-generation DSCT in the real clinical world shows a diagnostic performance comparable with previously reported validation studies. The excellent negative predictive value and likelihood ratio make CTCA a first-line noninvasive method for diagnosing obstructive CAD.

Comparison of sensitivity and reading time for the use of computer aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader

NASA Astrophysics Data System (ADS)

Beyer, F.; Zierott, L.; Fallenberg, E. M.; Juergens, K.; Stoeckel, J.; Heindel, W.; Wormanns, D.

2006-03-01

Purpose: To compare sensitivity and reading time when using CAD as second reader resp. concurrent reader. Materials and Methods: Fifty chest MDCT scans due to clinical indication were analysed independently by four radiologists two times: First with CAD as concurrent reader (display of CAD results simultaneously to the primary reading by the radiologist); then after a median of 14 weeks with CAD as second reader (CAD results were shown after completion of a reading session without CAD). A prototype version of Siemens LungCAD (Siemens,Malvern,USA) was used. Sensitivities and reading times for detecting nodules >=4mm of concurrent reading, reading without CAD and second reading were recorded. In a consensus conference false positive findings were eliminated. Student's T-Test was used to compare sensitivities and reading times. Results: 108 true positive nodules were found. Mean sensitivity was .68 for reading without CAD, .68 for concurrent reading and .75 for second reading. Differences of sensitivities were significant between concurrent and second reading (p<.001) resp. reading without CAD and second reading (p=.001). Mean reading time for concurrent reading was significant shorter (274s) compared to reading without CAD (294s;p=.04) and second reading (337sp<.001). New work to be presented: To our knowledge this is the first study that compares sensitivities and reading times between use of CAD as concurrent resp. second reader. Conclusion: CAD can either be used to speed up reading of chest CT cases for pulmonary nodules without loss of sensitivity as concurrent reader -OR (and not AND) to increase sensitivity and reading time as second reader.

Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

PubMed

Li, Feng

2015-07-01

This review paper is based on our research experience in the past 30 years. The importance of radiologists' role is discussed in the development or evaluation of new medical images and of computer-aided detection (CAD) schemes in chest radiology. The four main topics include (1) introducing what diseases can be included in a research database for different imaging techniques or CAD systems and what imaging database can be built by radiologists, (2) understanding how radiologists' subjective judgment can be combined with technical objective features to improve CAD performance, (3) sharing our experience in the design of successful observer performance studies, and (4) finally, discussing whether the new images and CAD systems can improve radiologists' diagnostic ability in chest radiology. In conclusion, advanced imaging techniques and detection/classification of CAD systems have a potential clinical impact on improvement of radiologists' diagnostic ability, for both the detection and the differential diagnosis of various lung diseases, in chest radiology.

Computer Aided Detection (CAD) Systems for Mammography and the Use of GRID in Medicine

NASA Astrophysics Data System (ADS)

Lauria, Adele

It is well known that the most effective way to defeat breast cancer is early detection, as surgery and medical therapies are more efficient when the disease is diagnosed at an early stage. The principal diagnostic technique for breast cancer detection is X-ray mammography. Screening programs have been introduced in many European countries to invite women to have periodic radiological breast examinations. In such screenings, radiologists are often required to examine large numbers of mammograms with a double reading, that is, two radiologists examine the images independently and then compare their results. In this way an increment in sensitivity (the rate of correctly identified images with a lesion) of up to 15% is obtained.1,2 In most radiological centres, it is a rarity to find two radiologists to examine each report. In recent years different Computer Aided Detection (CAD) systems have been developed as a support to radiologists working in mammography: one may hope that the "second opinion" provided by CAD might represent a lower cost alternative to improve the diagnosis. At present, four CAD systems have obtained the FDA approval in the USA. † Studies3,4 show an increment in sensitivity when CAD systems are used. Freer and Ulissey in 2001 5 demonstrated that the use of a commercial CAD system (ImageChecker M1000, R2 Technology) increases the number of cancers detected up to 19.5% with little increment in recall rate. Ciatto et al.,5 in a study simulating a double reading with a commercial CAD system (SecondLook‡), showed a moderate increment in sensitivity while reducing specificity (the rate of correctly identified images without a lesion). Notwithstanding these optimistic results, there is an ongoing debate to define the advantages of the use of CAD as second reader: the main limits underlined, e.g., by Nishikawa6 are that retrospective studies are considered much too optimistic and that clinical studies must be performed to demonstrate a statistically significant benefit from the use of CAD.

Detection of Anomalous Insiders in Collaborative Environments via Relational Analysis of Access Logs

PubMed Central

Chen, You; Malin, Bradley

2014-01-01

Collaborative information systems (CIS) are deployed within a diverse array of environments, ranging from the Internet to intelligence agencies to healthcare. It is increasingly the case that such systems are applied to manage sensitive information, making them targets for malicious insiders. While sophisticated security mechanisms have been developed to detect insider threats in various file systems, they are neither designed to model nor to monitor collaborative environments in which users function in dynamic teams with complex behavior. In this paper, we introduce a community-based anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on information recorded in the access logs of collaborative environments. CADS is based on the observation that typical users tend to form community structures, such that users with low a nity to such communities are indicative of anomalous and potentially illicit behavior. The model consists of two primary components: relational pattern extraction and anomaly detection. For relational pattern extraction, CADS infers community structures from CIS access logs, and subsequently derives communities, which serve as the CADS pattern core. CADS then uses a formal statistical model to measure the deviation of users from the inferred communities to predict which users are anomalies. To empirically evaluate the threat detection model, we perform an analysis with six months of access logs from a real electronic health record system in a large medical center, as well as a publicly-available dataset for replication purposes. The results illustrate that CADS can distinguish simulated anomalous users in the context of real user behavior with a high degree of certainty and with significant performance gains in comparison to several competing anomaly detection models. PMID:25485309

Additional diagnostic value of systolic dysfunction induced by dipyridamole stress cardiac magnetic resonance used in detecting coronary artery disease.

PubMed

Husser, Oliver; Bodí, Vicente; Sanchís, Juan; Mainar, Luis; Núñez, Julio; López-Lereu, María P; Monmeneu, José V; Ruiz, Vicente; Rumiz, Eva; Moratal, David; Chorro, Francisco J; Llácer, Angel

2009-04-01

Dipyridamole stress perfusion cardiovascular magnetic resonance (CMR) is used to detect coronary artery disease (CAD). However, few data are available on the diagnostic value of the systolic dysfunction induced by dipyridamole. This study investigated whether the induction of systolic dysfunction supplements the diagnostic information provided by perfusion imaging in the detection of CAD. Overall, 166 patients underwent dipyridamole CMR and quantitative coronary angiography, with CAD being defined as a stenosis > or =70%. Systolic dysfunction at rest, systolic dysfunction with dipyridamole, induced systolic dysfunction, and stress first-pass perfussion deficit (PD) and delayed enhancement were quantified. In the multivariate analysis, PD (hazard ratio [HR]=1.6; 95% confidence interval [CI], 1.33-1.91;P< .0001) and induced systolic dysfunction (OR=1.8; 95% CI, 1.18-2.28; P< .007) were independently associated with CAD and had a sensitivity and specificity of 92% and 62% and 43% and 96%, respectively. Patients were categorized as having no ischemia (Group 1), PD but no induced systolic dysfunction (Group 2), or induced systolic dysfunction irrespective of PD (Group 3). In Group 3, the prevalence of CAD was higher than in Group 1 or 2 (96% vs. 22% and 79%, respectively; P=.001) and the risk of CAD was two-fold higher than in Group 2 (OR=2.34; 95% CI, 1.07-5.13; P=.034). Compared with Group 2, more hypoperfused segments were observed in Group 3 (6.2+/-2.6 vs. 7.4+/-3.4; P=.044), and more diseased vessels (1.4+/-1.0 vs. 1.8+/-0.9; P=.036). Adding induced systolic dysfunction to perfusion and clinical data improved the multivariate model's C-statistic for predicting CAD (0.81 vs. 0.87; P=.02). Combining induced systolic dysfunction with perfusion imaging increases the diagnostic accuracy of detecting CAD and enables patients with severe ischemia and a high probability of CAD to be identified.

Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy.

PubMed

Byars, Sean G; Huang, Qin Qin; Gray, Lesley-Ann; Bakshi, Andrew; Ripatti, Samuli; Abraham, Gad; Stearns, Stephen C; Inouye, Michael

2017-06-01

Traditional genome-wide scans for positive selection have mainly uncovered selective sweeps associated with monogenic traits. While selection on quantitative traits is much more common, very few signals have been detected because of their polygenic nature. We searched for positive selection signals underlying coronary artery disease (CAD) in worldwide populations, using novel approaches to quantify relationships between polygenic selection signals and CAD genetic risk. We identified new candidate adaptive loci that appear to have been directly modified by disease pressures given their significant associations with CAD genetic risk. These candidates were all uniquely and consistently associated with many different male and female reproductive traits suggesting selection may have also targeted these because of their direct effects on fitness. We found that CAD loci are significantly enriched for lifetime reproductive success relative to the rest of the human genome, with evidence that the relationship between CAD and lifetime reproductive success is antagonistic. This supports the presence of antagonistic-pleiotropic tradeoffs on CAD loci and provides a novel explanation for the maintenance and high prevalence of CAD in modern humans. Lastly, we found that positive selection more often targeted CAD gene regulatory variants using HapMap3 lymphoblastoid cell lines, which further highlights the unique biological significance of candidate adaptive loci underlying CAD. Our study provides a novel approach for detecting selection on polygenic traits and evidence that modern human genomes have evolved in response to CAD-induced selection pressures and other early-life traits sharing pleiotropic links with CAD.

Undercut feature recognition for core and cavity generation

NASA Astrophysics Data System (ADS)

Yusof, Mursyidah Md; Salman Abu Mansor, Mohd

2018-01-01

Core and cavity is one of the important components in injection mould where the quality of the final product is mostly dependent on it. In the industry, with years of experience and skill, mould designers commonly use commercial CAD software to design the core and cavity which is time consuming. This paper proposes an algorithm that detect possible undercut features and generate the core and cavity. Two approaches are presented; edge convexity and face connectivity approach. The edge convexity approach is used to recognize undercut features while face connectivity is used to divide the faces into top and bottom region.

Automated detection of analyzable metaphase chromosome cells depicted on scanned digital microscopic images

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Wang, Xingwei; Chen, Xiaodong; Li, Yuhua; Liu, Hong; Li, Shibo; Zheng, Bin

2010-02-01

Visually searching for analyzable metaphase chromosome cells under microscopes is quite time-consuming and difficult. To improve detection efficiency, consistency, and diagnostic accuracy, an automated microscopic image scanning system was developed and tested to directly acquire digital images with sufficient spatial resolution for clinical diagnosis. A computer-aided detection (CAD) scheme was also developed and integrated into the image scanning system to search for and detect the regions of interest (ROI) that contain analyzable metaphase chromosome cells in the large volume of scanned images acquired from one specimen. Thus, the cytogeneticists only need to observe and interpret the limited number of ROIs. In this study, the high-resolution microscopic image scanning and CAD performance was investigated and evaluated using nine sets of images scanned from either bone marrow (three) or blood (six) specimens for diagnosis of leukemia. The automated CAD-selection results were compared with the visual selection. In the experiment, the cytogeneticists first visually searched for the analyzable metaphase chromosome cells from specimens under microscopes. The specimens were also automated scanned and followed by applying the CAD scheme to detect and save ROIs containing analyzable cells while deleting the others. The automated selected ROIs were then examined by a panel of three cytogeneticists. From the scanned images, CAD selected more analyzable cells than initially visual examinations of the cytogeneticists in both blood and bone marrow specimens. In general, CAD had higher performance in analyzing blood specimens. Even in three bone marrow specimens, CAD selected 50, 22, 9 ROIs, respectively. Except matching with the initially visual selection of 9, 7, and 5 analyzable cells in these three specimens, the cytogeneticists also selected 41, 15 and 4 new analyzable cells, which were missed in initially visual searching. This experiment showed the feasibility of applying this CAD-guided high-resolution microscopic image scanning system to prescreen and select ROIs that may contain analyzable metaphase chromosome cells. The success and the further improvement of this automated scanning system may have great impact on the future clinical practice in genetic laboratories to detect and diagnose diseases.

Construction of a Resting High Fidelity ECG "SuperScore" for Management and Screening of Heart Disease

NASA Technical Reports Server (NTRS)

Schlegel, Todd T.; Delgado, Reynolds; Poulin, Greg; Starc, Vito; Arenare, Brian; Rahman, M. A.

2006-01-01

Resting conventional ECG is notoriously insensitive for detecting coronary artery disease (CAD) and only nominally useful in screening for cardiomyopathy (CM). Similarly, conventional exercise stress test ECG is both time- and labor-consuming and its accuracy in identifying CAD is suboptimal for use in population screening. We retrospectively investigated the accuracy of several advanced resting electrocardiographic (ECG) parameters, both alone and in combination, for detecting CAD and cardiomyopathy (CM).

Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

PubMed Central

Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

2016-01-01

Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision. PMID:26885520

Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence.

PubMed

Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

2016-01-01

Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

Accuracy of virtual surgical planning of orthognathic surgery with aid of CAD/CAM fabricated surgical splint-A novel 3D analyzing algorithm.

PubMed

Chin, Shih-Jan; Wilde, Frank; Neuhaus, Michael; Schramm, Alexander; Gellrich, Nils-Claudius; Rana, Majeed

2017-12-01

The benefit of computer-assisted planning in orthognathic surgery has been extensively documented over the last decade. This study aims to evaluate the accuracy of a virtual orthognathic surgical plan by a novel three dimensional (3D) analysis method. Ten patients who required orthognathic surgery were included in this study. A virtual surgical plan was achieved by the combination of a 3D skull model acquired from computed tomography (CT) and surface scanning of the upper and lower dental arch respectively and final occlusal position. Osteotomies and movement of maxilla and mandible were simulated by Dolphin Imaging 11.8 Premium ® (Dolphin Imaging and Management Solutions, Chatsworth, CA). The surgical plan was transferred to surgical splints fabricated by means of Computer Aided Design/Computer Aided Manufacturing (CAD/CAM). Differences of three dimensional measurements between the virtual surgical plan and postoperative results were evaluated. The results from all parameters showed that the virtual surgical plans were successfully transferred by the assistance of CAD/CAM fabricated surgical splint. Wilcoxon's signed rank test showed that no statistically significant deviation between surgical plan and post-operational result could be detected. However, deviation of angle U1 axis-HP and distance of A-CP could not fulfill the clinical success criteria. Virtual surgical planning and CAD/CAM fabricated surgical splint are proven to facilitate treatment planning and offer an accurate surgical result in orthognathic surgery. Copyright © 2017 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Evolving biomarkers improve prediction of long-term mortality in patients with stable coronary artery disease: the BIO-VILCAD score.

PubMed

Kleber, M E; Goliasch, G; Grammer, T B; Pilz, S; Tomaschitz, A; Silbernagel, G; Maurer, G; März, W; Niessner, A

2014-08-01

Algorithms to predict the future long-term risk of patients with stable coronary artery disease (CAD) are rare. The VIenna and Ludwigshafen CAD (VILCAD) risk score was one of the first scores specifically tailored for this clinically important patient population. The aim of this study was to refine risk prediction in stable CAD creating a new prediction model encompassing various pathophysiological pathways. Therefore, we assessed the predictive power of 135 novel biomarkers for long-term mortality in patients with stable CAD. We included 1275 patients with stable CAD from the LUdwigshafen RIsk and Cardiovascular health study with a median follow-up of 9.8 years to investigate whether the predictive power of the VILCAD score could be improved by the addition of novel biomarkers. Additional biomarkers were selected in a bootstrapping procedure based on Cox regression to determine the most informative predictors of mortality. The final multivariable model encompassed nine clinical and biochemical markers: age, sex, left ventricular ejection fraction (LVEF), heart rate, N-terminal pro-brain natriuretic peptide, cystatin C, renin, 25OH-vitamin D3 and haemoglobin A1c. The extended VILCAD biomarker score achieved a significantly improved C-statistic (0.78 vs. 0.73; P = 0.035) and net reclassification index (14.9%; P < 0.001) compared to the original VILCAD score. Omitting LVEF, which might not be readily measureable in clinical practice, slightly reduced the accuracy of the new BIO-VILCAD score but still significantly improved risk classification (net reclassification improvement 12.5%; P < 0.001). The VILCAD biomarker score based on routine parameters complemented by novel biomarkers outperforms previous risk algorithms and allows more accurate classification of patients with stable CAD, enabling physicians to choose more personalized treatment regimens for their patients.

A citizen science approach to optimising computer aided detection (CAD) in mammography

NASA Astrophysics Data System (ADS)

Ionescu, Georgia V.; Harkness, Elaine F.; Hulleman, Johan; Astley, Susan M.

2018-03-01

Computer aided detection (CAD) systems assist medical experts during image interpretation. In mammography, CAD systems prompt suspicious regions which help medical experts to detect early signs of cancer. This is a challenging task and prompts may appear in regions that are actually normal, whilst genuine cancers may be missed. The effect prompting has on readers performance is not fully known. In order to explore the effects of prompting errors, we have created an online game (Bat Hunt), designed for non-experts, that mirrors mammographic CAD. This allows us to explore a wider parameter space. Users are required to detect bats in images of flocks of birds, with image difficulty matched to the proportions of screening mammograms in different BI-RADS density categories. Twelve prompted conditions were investigated, along with unprompted detection. On average, players achieved a sensitivity of 0.33 for unprompted detection, and sensitivities of 0.75, 0.83, and 0.92 respectively for 70%, 80%, and 90% of targets prompted, regardless of CAD specificity. False prompts distract players from finding unprompted targets if they appear in the same image. Player performance decreases when the number of false prompts increases, and increases proportionally with prompting sensitivity. Median lowest d' was for unprompted condition (1.08) and the highest for sensitivity 90% and 0.5 false prompts per image (d'=4.48).

Mass detection with digitized screening mammograms by using Gabor features

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Agyepong, Kwabena

2007-03-01

Breast cancer is the leading cancer among American women. The current lifetime risk of developing breast cancer is 13.4% (one in seven). Mammography is the most effective technology presently available for breast cancer screening. With digital mammograms computer-aided detection (CAD) has proven to be a useful tool for radiologists. In this paper, we focus on mass detection that is a common category of breast cancers relative to calcification and architecture distortion. We propose a new mass detection algorithm utilizing Gabor filters, termed as "Gabor Mass Detection" (GMD). There are three steps in the GMD algorithm, (1) preprocessing, (2) generating alarms and (3) classification (reducing false alarms). Down-sampling, quantization, denoising and enhancement are done in the preprocessing step. Then a total of 30 Gabor filtered images (along 6 bands by 5 orientations) are produced. Alarm segments are generated by thresholding four Gabor images of full orientations (Stage-I classification) with image-dependent thresholds computed via histogram analysis. Next a set of edge histogram descriptors (EHD) are extracted from 24 Gabor images (6 by 4) that will be used for Stage-II classification. After clustering EHD features with fuzzy C-means clustering method, a k-nearest neighbor classifier is used to reduce the number of false alarms. We initially analyzed 431 digitized mammograms (159 normal images vs. 272 cancerous images, from the DDSM project, University of South Florida) with the proposed GMD algorithm. And a ten-fold cross validation was used for testing the GMD algorithm upon the available data. The GMD performance is as follows: sensitivity (true positive rate) = 0.88 at false positives per image (FPI) = 1.25, and the area under the ROC curve = 0.83. The overall performance of the GMD algorithm is satisfactory and the accuracy of locating masses (highlighting the boundaries of suspicious areas) is relatively high. Furthermore, the GMD algorithm can successfully detect early-stage (with small values of Assessment & low Subtlety) malignant masses. In addition, Gabor filtered images are used in both stages of classifications, which greatly simplifies the GMD algorithm.

Increasing cancer detection yield of breast MRI using a new CAD scheme of mammograms

NASA Astrophysics Data System (ADS)

Tan, Maxine; Aghaei, Faranak; Hollingsworth, Alan B.; Stough, Rebecca G.; Liu, Hong; Zheng, Bin

2016-03-01

Although breast MRI is the most sensitive imaging modality to detect early breast cancer, its cancer detection yield in breast cancer screening is quite low (< 3 to 4% even for the small group of high-risk women) to date. The purpose of this preliminary study is to test the potential of developing and applying a new computer-aided detection (CAD) scheme of digital mammograms to identify women at high risk of harboring mammography-occult breast cancers, which can be detected by breast MRI. For this purpose, we retrospectively assembled a dataset involving 30 women who had both mammography and breast MRI screening examinations. All mammograms were interpreted as negative, while 5 cancers were detected using breast MRI. We developed a CAD scheme of mammograms, which include a new quantitative mammographic image feature analysis based risk model, to stratify women into two groups with high and low risk of harboring mammography-occult cancer. Among 30 women, 9 were classified into the high risk group by CAD scheme, which included all 5 women who had cancer detected by breast MRI. All 21 low risk women remained negative on the breast MRI examinations. The cancer detection yield of breast MRI applying to this dataset substantially increased from 16.7% (5/30) to 55.6% (5/9), while eliminating 84% (21/25) unnecessary breast MRI screenings. The study demonstrated the potential of applying a new CAD scheme to significantly increase cancer detection yield of breast MRI, while simultaneously reducing the number of negative MRIs in breast cancer screening.

Management of coronary artery disease

NASA Astrophysics Data System (ADS)

Safri, Z.

2018-03-01

Coronary Artery Disease (CAD) is associated with significant morbidity and mortality, therefore it’s important to early and accurate detection and appropriate management. Diagnosis of CAD include clinical examination, noninvasive techniques such as biochemical testing, a resting ECG, possibly ambulatory ECG monitoring, resting echocardiography, chest X-ray in selected patients; and catheterization. Managements of CAD patients include lifestyle modification, control of CAD risk factors, pharmacologic therapy, and patient education. Revascularization consists of percutaneous coronary angioplasty and coronary artery bypass grafting. Cardiac rehabilitation should be considered in all patients with CAD. This comprehensive review highlights strategies of management in patients with CAD.

Impact of arrhythmia on diagnostic performance of adenosine stress CMR in patients with suspected or known coronary artery disease.

PubMed

Greulich, Simon; Steubing, Hannah; Birkmeier, Stefan; Grün, Stefan; Bentz, Kerstin; Sechtem, Udo; Mahrholdt, Heiko

2015-11-05

The diagnostic performance of adenosine stress cardiovascular magnetic resonance (CMR) in patients with arrhythmias presenting for work-up of suspected or known CAD is largely unknown, since most CMR studies currently available exclude arrhythmic patients from analysis fearing gating problems, or other artifacts will impair image quality. The primary aim of our study was to evaluate the diagnostic performance of adenosine stress CMR for detection of significant coronary stenosis in patients with arrhythmia presenting for 1) work-up of suspected coronary artery disease (CAD), or 2) work-up of ischemia in known CAD. Patients with arrhythmia referred for work-up of suspected CAD or work-up of ischemia in known CAD undergoing adenosine stress CMR were included if they had coronary angiography within four weeks of CMR. One hundred fifty-nine patients were included (n = 64 atrial fibrillation, n = 87 frequent ventricular extrasystoles, n = 8 frequent supraventricular extrasystoles). Of these, n = 72 had suspected CAD, and n = 87 had known CAD. Diagnostic accuracy of the adenosine stress CMR for detection of significant CAD was 73 % for the entire population (sensitivity 72 %, specificity 76 %). Diagnostic accuracy was 75 % (sensitivity 80 %, specificity 74 %) in patients with suspected CAD, and 74 % (sensitivity 71 %, specificity 79 %) in the group with known CAD. For different types of arrhythmia, diagnostic accuracy of CMR was 70 % in the atrial fibrillation group, and 79 % in patients with ventricular extrasystoles. On a per coronary territory analysis, diagnostic accuracy of CMR was 77 % for stenosis of the left and 82 % for stenosis of the right coronary artery. The present data demonstrates good diagnostic performance of adenosine stress CMR for detection of significant coronary stenosis in patients with arrhythmia presenting for work-up of suspected CAD, or work-up of ischemia in known CAD. This holds true for a per patient, as well as for a per coronary territory analysis.

ΤND: a thyroid nodule detection system for analysis of ultrasound images and videos.

PubMed

Keramidas, Eystratios G; Maroulis, Dimitris; Iakovidis, Dimitris K

2012-06-01

In this paper, we present a computer-aided-diagnosis (CAD) system prototype, named TND (Thyroid Nodule Detector), for the detection of nodular tissue in ultrasound (US) thyroid images and videos acquired during thyroid US examinations. The proposed system incorporates an original methodology that involves a novel algorithm for automatic definition of the boundaries of the thyroid gland, and a novel approach for the extraction of noise resilient image features effectively representing the textural and the echogenic properties of the thyroid tissue. Through extensive experimental evaluation on real thyroid US data, its accuracy in thyroid nodule detection has been estimated to exceed 95%. These results attest to the feasibility of the clinical application of TND, for the provision of a second more objective opinion to the radiologists by exploiting image evidences.

Performance Evaluation of Various STL File Mesh Refining Algorithms Applied for FDM-RP Process

NASA Astrophysics Data System (ADS)

Ledalla, Siva Rama Krishna; Tirupathi, Balaji; Sriram, Venkatesh

2018-06-01

Layered manufacturing machines use the stereolithography (STL) file to build parts. When a curved surface is converted from a computer aided design (CAD) file to STL, it results in a geometrical distortion and chordal error. Parts manufactured with this file, might not satisfy geometric dimensioning and tolerance requirements due to approximated geometry. Current algorithms built in CAD packages have export options to globally reduce this distortion, which leads to an increase in the file size and pre-processing time. In this work, different mesh subdivision algorithms are applied on STL file of a complex geometric features using MeshLab software. The mesh subdivision algorithms considered in this work are modified butterfly subdivision technique, loops sub division technique and general triangular midpoint sub division technique. A comparative study is made with respect to volume and the build time using the above techniques. It is found that triangular midpoint sub division algorithm is more suitable for the geometry under consideration. Only the wheel cap part is then manufactured on Stratasys MOJO FDM machine. The surface roughness of the part is measured on Talysurf surface roughness tester.

Lung partitioning for x-ray CAD applications

NASA Astrophysics Data System (ADS)

Annangi, Pavan; Raja, Anand

2011-03-01

Partitioning the inside region of lung into homogeneous regions becomes a crucial step in any computer-aided diagnosis applications based on chest X-ray. The ribs, air pockets and clavicle occupy major space inside the lung as seen in the chest x-ray PA image. Segmenting the ribs and clavicle to partition the lung into homogeneous regions forms a crucial step in any CAD application to better classify abnormalities. In this paper we present two separate algorithms to segment ribs and the clavicle bone in a completely automated way. The posterior ribs are segmented based on Phase congruency features and the clavicle is segmented using Mean curvature features followed by Radon transform. Both the algorithms work on the premise that the presentation of each of these anatomical structures inside the left and right lung has a specific orientation range within which they are confined to. The search space for both the algorithms is limited to the region inside the lung, which is obtained by an automated lung segmentation algorithm that was previously developed in our group. Both the algorithms were tested on 100 images of normal and patients affected with Pneumoconiosis.

Verifying Three-Dimensional Skull Model Reconstruction Using Cranial Index of Symmetry

PubMed Central

Kung, Woon-Man; Chen, Shuo-Tsung; Lin, Chung-Hsiang; Lu, Yu-Mei; Chen, Tzu-Hsuan; Lin, Muh-Shi

2013-01-01

Background Difficulty exists in scalp adaptation for cranioplasty with customized computer-assisted design/manufacturing (CAD/CAM) implant in situations of excessive wound tension and sub-cranioplasty dead space. To solve this clinical problem, the CAD/CAM technique should include algorithms to reconstruct a depressed contour to cover the skull defect. Satisfactory CAM-derived alloplastic implants are based on highly accurate three-dimensional (3-D) CAD modeling. Thus, it is quite important to establish a symmetrically regular CAD/CAM reconstruction prior to depressing the contour. The purpose of this study is to verify the aesthetic outcomes of CAD models with regular contours using cranial index of symmetry (CIS). Materials and methods From January 2011 to June 2012, decompressive craniectomy (DC) was performed for 15 consecutive patients in our institute. 3-D CAD models of skull defects were reconstructed using commercial software. These models were checked in terms of symmetry by CIS scores. Results CIS scores of CAD reconstructions were 99.24±0.004% (range 98.47–99.84). CIS scores of these CAD models were statistically significantly greater than 95%, identical to 99.5%, but lower than 99.6% (p<0.001, p = 0.064, p = 0.021 respectively, Wilcoxon matched pairs signed rank test). These data evidenced the highly accurate symmetry of these CAD models with regular contours. Conclusions CIS calculation is beneficial to assess aesthetic outcomes of CAD-reconstructed skulls in terms of cranial symmetry. This enables further accurate CAD models and CAM cranial implants with depressed contours, which are essential in patients with difficult scalp adaptation. PMID:24204566

Applying a CAD-generated imaging marker to assess short-term breast cancer risk

NASA Astrophysics Data System (ADS)

Mirniaharikandehei, Seyedehnafiseh; Zarafshani, Ali; Heidari, Morteza; Wang, Yunzhi; Aghaei, Faranak; Zheng, Bin

2018-02-01

Although whether using computer-aided detection (CAD) helps improve radiologists' performance in reading and interpreting mammograms is controversy due to higher false-positive detection rates, objective of this study is to investigate and test a new hypothesis that CAD-generated false-positives, in particular, the bilateral summation of false-positives, is a potential imaging marker associated with short-term breast cancer risk. An image dataset involving negative screening mammograms acquired from 1,044 women was retrospectively assembled. Each case involves 4 images of craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breasts. In the next subsequent mammography screening, 402 cases were positive for cancer detected and 642 remained negative. A CAD scheme was applied to process all "prior" negative mammograms. Some features from CAD scheme were extracted, which include detection seeds, the total number of false-positive regions, an average of detection scores and the sum of detection scores in CC and MLO view images. Then the features computed from two bilateral images of left and right breasts from either CC or MLO view were combined. In order to predict the likelihood of each testing case being positive in the next subsequent screening, two logistic regression models were trained and tested using a leave-one-case-out based cross-validation method. Data analysis demonstrated the maximum prediction accuracy with an area under a ROC curve of AUC=0.65+/-0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of [2.95, 6.83]. The results also illustrated an increasing trend in the adjusted odds ratio and risk prediction scores (p<0.01). Thus, the study showed that CAD-generated false-positives might provide a new quantitative imaging marker to help assess short-term breast cancer risk.

Improving the performance of lesion-based computer-aided detection schemes of breast masses using a case-based adaptive cueing method

NASA Astrophysics Data System (ADS)

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Qian, Wei; Zheng, Bin

2016-03-01

Current commercialized CAD schemes have high false-positive (FP) detection rates and also have high correlations in positive lesion detection with radiologists. Thus, we recently investigated a new approach to improve the efficacy of applying CAD to assist radiologists in reading and interpreting screening mammograms. Namely, we developed a new global feature based CAD approach/scheme that can cue the warning sign on the cases with high risk of being positive. In this study, we investigate the possibility of fusing global feature or case-based scores with the local or lesion-based CAD scores using an adaptive cueing method. We hypothesize that the information from the global feature extraction (features extracted from the whole breast regions) are different from and can provide supplementary information to the locally-extracted features (computed from the segmented lesion regions only). On a large and diverse full-field digital mammography (FFDM) testing dataset with 785 cases (347 negative and 438 cancer cases with masses only), we ran our lesion-based and case-based CAD schemes "as is" on the whole dataset. To assess the supplementary information provided by the global features, we used an adaptive cueing method to adaptively adjust the original CAD-generated detection scores (Sorg) of a detected suspicious mass region based on the computed case-based score (Scase) of the case associated with this detected region. Using the adaptive cueing method, better sensitivity results were obtained at lower FP rates (<= 1 FP per image). Namely, increases of sensitivities (in the FROC curves) of up to 6.7% and 8.2% were obtained for the ROI and Case-based results, respectively.

Computer aided detection of brain micro-bleeds in traumatic brain injury

NASA Astrophysics Data System (ADS)

van den Heuvel, T. L. A.; Ghafoorian, M.; van der Eerden, A. W.; Goraj, B. M.; Andriessen, T. M. J. C.; ter Haar Romeny, B. M.; Platel, B.

2015-03-01

Brain micro-bleeds (BMBs) are used as surrogate markers for detecting diffuse axonal injury in traumatic brain injury (TBI) patients. The location and number of BMBs have been shown to influence the long-term outcome of TBI. To further study the importance of BMBs for prognosis, accurate localization and quantification are required. The task of annotating BMBs is laborious, complex and prone to error, resulting in a high inter- and intra-reader variability. In this paper we propose a computer-aided detection (CAD) system to automatically detect BMBs in MRI scans of moderate to severe neuro-trauma patients. Our method consists of four steps. Step one: preprocessing of the data. Both susceptibility (SWI) and T1 weighted MRI scans are used. The images are co-registered, a brain-mask is generated, the bias field is corrected, and the image intensities are normalized. Step two: initial candidates for BMBs are selected as local minima in the processed SWI scans. Step three: feature extraction. BMBs appear as round or ovoid signal hypo-intensities on SWI. Twelve features are computed to capture these properties of a BMB. Step four: Classification. To identify BMBs from the set of local minima using their features, different classifiers are trained on a database of 33 expert annotated scans and 18 healthy subjects with no BMBs. Our system uses a leave-one-out strategy to analyze its performance. With a sensitivity of 90% and 1.3 false positives per BMB, our CAD system shows superior results compared to state-of-the-art BMB detection algorithms (developed for non-trauma patients).

The interplay of attention economics and computer-aided detection marks in screening mammography

NASA Astrophysics Data System (ADS)

Schwartz, Tayler M.; Sridharan, Radhika; Wei, Wei; Lukyanchenko, Olga; Geiser, William; Whitman, Gary J.; Haygood, Tamara Miner

2016-03-01

Introduction: According to attention economists, overabundant information leads to decreased attention for individual pieces of information. Computer-aided detection (CAD) alerts radiologists to findings potentially associated with breast cancer but is notorious for creating an abundance of false-positive marks. We suspected that increased CAD marks do not lengthen mammogram interpretation time, as radiologists will selectively disregard these marks when present in larger numbers. We explore the relevance of attention economics in mammography by examining how the number of CAD marks affects interpretation time. Methods: We performed a retrospective review of bilateral digital screening mammograms obtained between January 1, 2011 and February 28, 2014, using only weekend interpretations to decrease distractions and the likelihood of trainee participation. We stratified data according to reader and used ANOVA to assess the relationship between number of CAD marks and interpretation time. Results: Ten radiologists, with median experience after residency of 12.5 years (range 6 to 24,) interpreted 1849 mammograms. When accounting for number of images, Breast Imaging Reporting and Data System category, and breast density, increasing numbers of CAD marks was correlated with longer interpretation time only for the three radiologists with the fewest years of experience (median 7 years.) Conclusion: For the 7 most experienced readers, increasing CAD marks did not lengthen interpretation time. We surmise that as CAD marks increase, the attention given to individual marks decreases. Experienced radiologists may rapidly dismiss larger numbers of CAD marks as false-positive, having learned that devoting extra attention to such marks does not improve clinical detection.

Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy

PubMed Central

Byars, Sean G.; Gray, Lesley-Ann; Ripatti, Samuli; Stearns, Stephen C.; Inouye, Michael

2017-01-01

Traditional genome-wide scans for positive selection have mainly uncovered selective sweeps associated with monogenic traits. While selection on quantitative traits is much more common, very few signals have been detected because of their polygenic nature. We searched for positive selection signals underlying coronary artery disease (CAD) in worldwide populations, using novel approaches to quantify relationships between polygenic selection signals and CAD genetic risk. We identified new candidate adaptive loci that appear to have been directly modified by disease pressures given their significant associations with CAD genetic risk. These candidates were all uniquely and consistently associated with many different male and female reproductive traits suggesting selection may have also targeted these because of their direct effects on fitness. We found that CAD loci are significantly enriched for lifetime reproductive success relative to the rest of the human genome, with evidence that the relationship between CAD and lifetime reproductive success is antagonistic. This supports the presence of antagonistic-pleiotropic tradeoffs on CAD loci and provides a novel explanation for the maintenance and high prevalence of CAD in modern humans. Lastly, we found that positive selection more often targeted CAD gene regulatory variants using HapMap3 lymphoblastoid cell lines, which further highlights the unique biological significance of candidate adaptive loci underlying CAD. Our study provides a novel approach for detecting selection on polygenic traits and evidence that modern human genomes have evolved in response to CAD-induced selection pressures and other early-life traits sharing pleiotropic links with CAD. PMID:28640878

Dynamic MRI-based computer aided diagnostic systems for early detection of kidney transplant rejection: A survey

NASA Astrophysics Data System (ADS)

Mostapha, Mahmoud; Khalifa, Fahmi; Alansary, Amir; Soliman, Ahmed; Gimel'farb, Georgy; El-Baz, Ayman

2013-10-01

Early detection of renal transplant rejection is important to implement appropriate medical and immune therapy in patients with transplanted kidneys. In literature, a large number of computer-aided diagnostic (CAD) systems using different image modalities, such as ultrasound (US), magnetic resonance imaging (MRI), computed tomography (CT), and radionuclide imaging, have been proposed for early detection of kidney diseases. A typical CAD system for kidney diagnosis consists of a set of processing steps including: motion correction, segmentation of the kidney and/or its internal structures (e.g., cortex, medulla), construction of agent kinetic curves, functional parameter estimation, diagnosis, and assessment of the kidney status. In this paper, we survey the current state-of-the-art CAD systems that have been developed for kidney disease diagnosis using dynamic MRI. In addition, the paper addresses several challenges that researchers face in developing efficient, fast and reliable CAD systems for the early detection of kidney diseases.

VLSI Architectures and CAD

DTIC Science & Technology

1989-04-01

existing types of data compression methods amenable to our needs: Huffman, Arithmetic, BSTW, and Lempel - Ziv . The two algorithms with the most modest...APEX architecture. Recently we bega-, investigating various data compression algorithms with character- istics amenable to hardware implementation...This work has so far yielded a variant of the Lempel - Ziv algorithm that adapts continuously to its input and is appropriate to a hardware implementation

Geometry modeling and grid generation using 3D NURBS control volume

NASA Technical Reports Server (NTRS)

Yu, Tzu-Yi; Soni, Bharat K.; Shih, Ming-Hsin

1995-01-01

The algorithms for volume grid generation using NURBS geometric representation are presented. The parameterization algorithm is enhanced to yield a desired physical distribution on the curve, surface and volume. This approach bridges the gap between CAD surface/volume definition and surface/volume grid generation. Computational examples associated with practical configurations have shown the utilization of these algorithms.

Evaluation of MTANNs for eliminating false-positive with different computer aided pulmonary nodules detection software.

PubMed

Shi, Zhenghao; Ma, Jiejue; Feng, Yaning; He, Lifeng; Suzuki, Kenji

2015-11-01

MTANN (Massive Training Artificial Neural Network) is a promising tool, which applied to eliminate false-positive for thoracic CT in recent years. In order to evaluate whether this method is feasible to eliminate false-positive of different CAD schemes, especially, when it is applied to commercial CAD software, this paper evaluate the performance of the method for eliminating false-positives produced by three different versions of commercial CAD software for lung nodules detection in chest radiographs. Experimental results demonstrate that the approach is useful in reducing FPs for different computer aided lung nodules detection software in chest radiographs.

On the Use of CAD and Cartesian Methods for Aerodynamic Optimization

NASA Technical Reports Server (NTRS)

Nemec, M.; Aftosmis, M. J.; Pulliam, T. H.

2004-01-01

The objective for this paper is to present the development of an optimization capability for Curt3D, a Cartesian inviscid-flow analysis package. We present the construction of a new optimization framework and we focus on the following issues: 1) Component-based geometry parameterization approach using parametric-CAD models and CAPRI. A novel geometry server is introduced that addresses the issue of parallel efficiency while only sparingly consuming CAD resources; 2) The use of genetic and gradient-based algorithms for three-dimensional aerodynamic design problems. The influence of noise on the optimization methods is studied. Our goal is to create a responsive and automated framework that efficiently identifies design modifications that result in substantial performance improvements. In addition, we examine the architectural issues associated with the deployment of a CAD-based approach in a heterogeneous parallel computing environment that contains both CAD workstations and dedicated compute engines. We demonstrate the effectiveness of the framework for a design problem that features topology changes and complex geometry.

Danish study of Non-Invasive testing in Coronary Artery Disease (Dan-NICAD): study protocol for a randomised controlled trial.

PubMed

Nissen, Louise; Winther, Simon; Isaksen, Christin; Ejlersen, June Anita; Brix, Lau; Urbonaviciene, Grazina; Frost, Lars; Madsen, Lene Helleskov; Knudsen, Lars Lyhne; Schmidt, Samuel Emil; Holm, Niels Ramsing; Maeng, Michael; Nyegaard, Mette; Bøtker, Hans Erik; Bøttcher, Morten

2016-05-26

Coronary computed tomography angiography (CCTA) is an established method for ruling out coronary artery disease (CAD). Most patients referred for CCTA do not have CAD and only approximately 20-30 % of patients are subsequently referred to further testing by invasive coronary angiography (ICA) or non-invasive perfusion evaluation due to suspected obstructive CAD. In cases with severe calcifications, a discrepancy between CCTA and ICA often occurs, leading to the well-described, low-diagnostic specificity of CCTA. As ICA is cost consuming and involves a risk of complications, an optimized algorithm would be valuable and could decrease the number of ICAs that do not lead to revascularization. The primary objective of the Dan-NICAD study is to determine the diagnostic accuracy of cardiac magnetic resonance imaging (CMRI) and myocardial perfusion scintigraphy (MPS) as secondary tests after a primary CCTA where CAD could not be ruled out. The secondary objective includes an evaluation of the diagnostic precision of an acoustic technology that analyses the sound of coronary blood flow. It may potentially provide better stratification prior to CCTA than clinical risk stratification scores alone. Dan-NICAD is a multi-centre, randomised, cross-sectional trial, which will include approximately 2,000 patients without known CAD, who were referred to CCTA due to a history of symptoms suggestive of CAD and a low-risk to intermediate-risk profile, as evaluated by a cardiologist. Patient interview, sound recordings, and blood samples are obtained in connection with the CCTA. All patients with suspected obstructive CAD by CCTA are randomised to either stress CMRI or stress MPS, followed by ICA with fractional flow reserve (FFR) measurements. Obstructive CAD is defined as an FFR below 0.80 or as high-grade stenosis (>90 % diameter stenosis) by visual assessment. Diagnostic performance is evaluated as sensitivity, specificity, predictive values, likelihood ratios, and C statistics. Enrolment commenced in September 2014 and is expected to be complete in May 2016. Dan-NICAD is designed to assess whether a secondary perfusion examination after CCTA could safely reduce the number of ICAs where revascularization is not required. The results are expected to add knowledge about the optimal algorithm for diagnosing CAD. Clinicaltrials.gov identifier, NCT02264717 . Registered on 26 September 2014.

Investigation of optimal use of computer-aided detection systems: the role of the "machine" in decision making process.

PubMed

Paquerault, Sophie; Hardy, Paul T; Wersto, Nancy; Chen, John; Smith, Robert C

2010-09-01

The aim of this study was to explore different computerized models (the "machine") as a means to achieve optimal use of computer-aided detection (CAD) systems and to investigate whether these models can play a primary role in clinical decision making and possibly replace a clinician's subjective decision for combining his or her own assessment with that provided by a CAD system. Data previously collected from a fully crossed, multiple-reader, multiple-case observer study with and without CAD by seven observers asked to identify simulated small masses on two separate sets of 100 mammographic images (low-contrast and high-contrast sets; ie, low-contrast and high-contrast simulated masses added to random locations on normal mammograms) were used. This allowed testing two relative sensitivities between the observers and CAD. Seven models that combined detection assessments from CAD standalone, unaided read, and CAD-aided read (second read and concurrent read) were developed using the leave-one-out technique for training and testing. These models were personalized for each observer. Detection performance accuracies were analyzed using the area under a portion of the free-response receiver-operating characteristic curve (AUFC), sensitivity, and number of false-positives per image. For the low-contrast set, the use of computerized models resulted in significantly higher AUFCs compared to the unaided read mode for all readers, whereas the increased AUFCs between CAD-aided (second and concurrent reads; ie, decisions made by the readers) and unaided read modes were statistically significant for a majority, but not all, of the readers (four and five of the seven readers, respectively). For the high-contrast set, there were no significant trends in the AUFCs whether or not a model was used to combine the original reading modes. Similar results were observed when using sensitivity as the figure of merit. However, the average number of false-positives per image resulting from the computerized models remained the same as that obtained from the unaided read modes. Individual computerized models (the machine) that combine image assessments from CAD standalone, unaided read, and CAD-aided read can increase detection performance compared to the reading done by the observer. However, relative sensitivity (ie, the difference in sensitivity between CAD standalone and unaided read) was a critical factor that determined incremental improvement in decision making, whether made by the observer or using computerized models. Published by Elsevier Inc.

Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM.

PubMed

Davari Dolatabadi, Azam; Khadem, Siamak Esmael Zadeh; Asl, Babak Mohammadzadeh

2017-01-01

Currently Coronary Artery Disease (CAD) is one of the most prevalent diseases, and also can lead to death, disability and economic loss in patients who suffer from cardiovascular disease. Diagnostic procedures of this disease by medical teams are typically invasive, although they do not satisfy the required accuracy. In this study, we have proposed a methodology for the automatic diagnosis of normal and Coronary Artery Disease conditions using Heart Rate Variability (HRV) signal extracted from electrocardiogram (ECG). The features are extracted from HRV signal in time, frequency and nonlinear domains. The Principal Component Analysis (PCA) is applied to reduce the dimension of the extracted features in order to reduce computational complexity and to reveal the hidden information underlaid in the data. Finally, Support Vector Machine (SVM) classifier has been utilized to classify two classes of data using the extracted distinguishing features. In this paper, parameters of the SVM have been optimized in order to improve the accuracy. Provided reports in this paper indicate that the detection of CAD class from normal class using the proposed algorithm was performed with accuracy of 99.2%, sensitivity of 98.43%, and specificity of 100%. This study has shown that methods which are based on the feature extraction of the biomedical signals are an appropriate approach to predict the health situation of the patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automated bone age assessment of older children using the radius

NASA Astrophysics Data System (ADS)

Tsao, Sinchai; Gertych, Arkadiusz; Zhang, Aifeng; Liu, Brent J.; Huang, Han K.

2008-03-01

The Digital Hand Atlas in Assessment of Skeletal Development is a large-scale Computer Aided Diagnosis (CAD) project for automating the process of grading Skeletal Development of children from 0-18 years of age. It includes a complete collection of 1,400 normal hand X-rays of children between the ages of 0-18 years of age. Bone Age Assessment is used as an index of skeletal development for detection of growth pathologies that can be related to endocrine, malnutrition and other disease types. Previous work at the Image Processing and Informatics Lab (IPILab) allowed the bone age CAD algorithm to accurately assess bone age of children from 1 to 16 (male) or 14 (female) years of age using the Phalanges as well as the Carpal Bones. At the older ages (16(male) or 14(female) -19 years of age) the Phalanges as well as the Carpal Bones are fully developed and do not provide well-defined features for accurate bone age assessment. Therefore integration of the Radius Bone as a region of interest (ROI) is greatly needed and will significantly improve the ability to accurately assess the bone age of older children. Preliminary studies show that an integrated Bone Age CAD that utilizes the Phalanges, Carpal Bones and Radius forms a robust method for automatic bone age assessment throughout the entire age range (1-19 years of age).

Two-view information fusion for improvement of computer-aided detection (CAD) of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Wei, Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Ge, Jun; Zhang, Yiheng

2006-03-01

We are developing a two-view information fusion method to improve the performance of our CAD system for mass detection. Mass candidates on each mammogram were first detected with our single-view CAD system. Potential object pairs on the two-view mammograms were then identified by using the distance between the object and the nipple. Morphological features, Hessian feature, correlation coefficients between the two paired objects and texture features were used as input to train a similarity classifier that estimated a similarity scores for each pair. Finally, a linear discriminant analysis (LDA) classifier was used to fuse the score from the single-view CAD system and the similarity score. A data set of 475 patients containing 972 mammograms with 475 biopsy-proven masses was used to train and test the CAD system. All cases contained the CC view and the MLO or LM view. We randomly divided the data set into two independent sets of 243 cases and 232 cases. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained from averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 85% and 80% on the test set, the single-view CAD system achieved an FP rate of 2.0, 1.5, and 1.2 FPs/image, respectively. With the two-view fusion system, the FP rates were reduced to 1.7, 1.3, and 1.0 FPs/image, respectively, at the corresponding sensitivities. The improvement was found to be statistically significant (p<0.05) by the AFROC method. Our results indicate that the two-view fusion scheme can improve the performance of mass detection on mammograms.

Pulmonary nodules: effect of adaptive statistical iterative reconstruction (ASIR) technique on performance of a computer-aided detection (CAD) system-comparison of performance between different-dose CT scans.

PubMed

Yanagawa, Masahiro; Honda, Osamu; Kikuyama, Ayano; Gyobu, Tomoko; Sumikawa, Hiromitsu; Koyama, Mitsuhiro; Tomiyama, Noriyuki

2012-10-01

To evaluate the effects of ASIR on CAD system of pulmonary nodules using clinical routine-dose CT and lower-dose CT. Thirty-five patients (body mass index, 22.17 ± 4.37 kg/m(2)) were scanned by multidetector-row CT with tube currents (clinical routine-dose CT, automatically adjusted mA; lower-dose CT, 10 mA) and X-ray voltage (120 kVp). Each 0.625-mm-thick image was reconstructed at 0%-, 50%-, and 100%-ASIR: 0%-ASIR is reconstructed using only the filtered back-projection algorithm (FBP), while 100%-ASIR is reconstructed using the maximum ASIR and 50%-ASIR implies a blending of 50% FBP and ASIR. CAD output was compared retrospectively with the results of the reference standard which was established using a consensus panel of three radiologists. Data were analyzed using Bonferroni/Dunn's method. Radiation dose was calculated by multiplying dose-length product by conversion coefficient of 0.021. The consensus panel found 265 non-calcified nodules ≤ 30 mm (ground-glass opacity [GGO], 103; part-solid, 34; and solid, 128). CAD sensitivity was significantly higher at 100%-ASIR [clinical routine-dose CT, 71% (overall), 49% (GGO); lower-dose CT, 52% (overall), 67% (solid)] than at 0%-ASIR [clinical routine-dose CT, 54% (overall), 25% (GGO); lower-dose CT, 36% (overall), 50% (solid)] (p<0.001). Mean number of false-positive findings per examination was significantly higher at 100%-ASIR (clinical routine-dose CT, 8.5; lower-dose CT, 6.2) than at 0%-ASIR (clinical routine-dose CT, 4.6; lower-dose CT, 3.5; p<0.001). Effective doses were 10.77 ± 3.41 mSv in clinical routine-dose CT and 2.67 ± 0.17 mSv in lower-dose CT. CAD sensitivity at 100%-ASIR on lower-dose CT is almost equal to that at 0%-ASIR on clinical routine-dose CT. ASIR can increase CAD sensitivity despite increased false-positive findings. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

SU-E-T-339: Dosimetric Verification of Acuros XB Dose Calculation Algorithm On An Air Cavity for 6-MV Flattening Filter-Free Beam

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kang, S; Suh, T; Chung, J

Purpose: This study was to verify the accuracy of Acuros XB (AXB) dose calculation algorithm on an air cavity for a single radiation field using 6-MV flattening filter-free (FFF) beam. Methods: A rectangular slab phantom containing an air cavity was made for this study. The CT images of the phantom for dose calculation were scanned with and without film at measurement depths (4.5, 5.5, 6.5 and 7.5 cm). The central axis doses (CADs) and the off-axis doses (OADs) were measured by film and calculated with Analytical Anisotropic Algorithm (AAA) and AXB for field sizes ranging from 2 Χ 2 tomore » 5 Χ 5 cm{sup 2} of 6-MV FFF beams. Both algorithms were divided into AXB-w and AAA -w when included the film in phantom for dose calculation, and AXB-w/o and AAA-w/o in calculation without film. The calculated OADs for both algorithms were compared with the measured OADs and difference values were determined using root means squares error (RMSE) and gamma evaluation. Results: The percentage differences (%Diffs) between the measured and calculated CAD for AXB-w was most agreement than others. Compared to the %Diff with and without film, the %Diffs with film were decreased than without within both algorithms. The %Diffs for both algorithms were reduced with increasing field size and increased relative to the depth increment. RMSEs of CAD for AXB-w were within 10.32% for both inner-profile and penumbra, while the corresponding values of AAA-w appeared to 96.50%. Conclusion: This study demonstrated that the dose calculation with AXB within air cavity shows more accurate than with AAA compared to the measured dose. Furthermore, we found that the AXB-w was superior to AXB-w/o in this region when compared against the measurements.« less

Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.

PubMed

Patel, Bhavika K; Ranjbar, Sara; Wu, Teresa; Pockaj, Barbara A; Li, Jing; Zhang, Nan; Lobbes, Mark; Zhang, Bin; Mitchell, J Ross

2018-01-01

To evaluate whether the use of a computer-aided diagnosis-contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists. This IRB-approved retrospective study analyzed 50 lesions described on CESM from August 2014 to December 2015. Histopathologic analyses, used as the criterion standard, revealed 24 benign and 26 malignant lesions. An expert breast radiologist manually outlined lesion boundaries on the different views. A set of morphologic and textural features were then extracted from the low-energy and recombined images. Machine-learning algorithms with feature selection were used along with statistical analysis to reduce, select, and combine features. Selected features were then used to construct a predictive model using a support vector machine (SVM) classification method in a leave-one-out-cross-validation approach. The classification performance was compared against the diagnostic predictions of 2 breast radiologists with access to the same CESM cases. Based on the SVM classification, CAD-CESM correctly identified 45 of 50 lesions in the cohort, resulting in an overall accuracy of 90%. The detection rate for the malignant group was 88% (3 false-negative cases) and 92% for the benign group (2 false-positive cases). Compared with the model, radiologist 1 had an overall accuracy of 78% and a detection rate of 92% (2 false-negative cases) for the malignant group and 62% (10 false-positive cases) for the benign group. Radiologist 2 had an overall accuracy of 86% and a detection rate of 100% for the malignant group and 71% (8 false-positive cases) for the benign group. The results of our feasibility study suggest that a CAD-CESM tool can provide complementary information to radiologists, mainly by reducing the number of false-positive findings. Copyright © 2017 Elsevier B.V. All rights reserved.

Computer aided detection of surgical retained foreign object for prevention

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hadjiiski, Lubomir, E-mail: lhadjisk@umich.edu; Marentis, Theodore C.; Rondon, Lucas

2015-03-15

Purpose: Surgical retained foreign objects (RFOs) have significant morbidity and mortality. They are associated with approximately $1.5 × 10{sup 9} annually in preventable medical costs. The detection accuracy of radiographs for RFOs is a mediocre 59%. The authors address the RFO problem with two complementary technologies: a three-dimensional (3D) gossypiboma micro tag, the μTag that improves the visibility of RFOs on radiographs, and a computer aided detection (CAD) system that detects the μTag. It is desirable for the CAD system to operate in a high specificity mode in the operating room (OR) and function as a first reader for themore » surgeon. This allows for fast point of care results and seamless workflow integration. The CAD system can also operate in a high sensitivity mode as a second reader for the radiologist to ensure the highest possible detection accuracy. Methods: The 3D geometry of the μTag produces a similar two dimensional (2D) depiction on radiographs regardless of its orientation in the human body and ensures accurate detection by a radiologist and the CAD. The authors created a data set of 1800 cadaver images with the 3D μTag and other common man-made surgical objects positioned randomly. A total of 1061 cadaver images contained a single μTag and the remaining 739 were without μTag. A radiologist marked the location of the μTag using an in-house developed graphical user interface. The data set was partitioned into three independent subsets: a training set, a validation set, and a test set, consisting of 540, 560, and 700 images, respectively. A CAD system with modules that included preprocessing μTag enhancement, labeling, segmentation, feature analysis, classification, and detection was developed. The CAD system was developed using the training and the validation sets. Results: On the training set, the CAD achieved 81.5% sensitivity with 0.014 false positives (FPs) per image in a high specificity mode for the surgeons in the OR and 96.1% sensitivity with 0.81 FPs per image in a high sensitivity mode for the radiologists. On the independent test set, the CAD achieved 79.5% sensitivity with 0.003 FPs per image in a high specificity mode for the surgeons and 90.2% sensitivity with 0.23 FPs per image in a high sensitivity mode for the radiologists. Conclusions: To the best of the authors’ knowledge, this is the first time a 3D μTag is used to produce a recognizable, substantially similar 2D projection on radiographs regardless of orientation in space. It is the first time a CAD system is used to search for man-made objects over anatomic background. The CAD system for the μTags achieved reasonable performance in both the high specificity and the high sensitivity modes.« less

Computer-aided detection of breast masses: Four-view strategy for screening mammography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei Jun; Chan Heangping; Zhou Chuan

2011-04-15

Purpose: To improve the performance of a computer-aided detection (CAD) system for mass detection by using four-view information in screening mammography. Methods: The authors developed a four-view CAD system that emulates radiologists' reading by using the craniocaudal and mediolateral oblique views of the ipsilateral breast to reduce false positives (FPs) and the corresponding views of the contralateral breast to detect asymmetry. The CAD system consists of four major components: (1) Initial detection of breast masses on individual views, (2) information fusion of the ipsilateral views of the breast (referred to as two-view analysis), (3) information fusion of the corresponding viewsmore » of the contralateral breast (referred to as bilateral analysis), and (4) fusion of the four-view information with a decision tree. The authors collected two data sets for training and testing of the CAD system: A mass set containing 389 patients with 389 biopsy-proven masses and a normal set containing 200 normal subjects. All cases had four-view mammograms. The true locations of the masses on the mammograms were identified by an experienced MQSA radiologist. The authors randomly divided the mass set into two independent sets for cross validation training and testing. The overall test performance was assessed by averaging the free response receiver operating characteristic (FROC) curves of the two test subsets. The FP rates during the FROC analysis were estimated by using the normal set only. The jackknife free-response ROC (JAFROC) method was used to estimate the statistical significance of the difference between the test FROC curves obtained with the single-view and the four-view CAD systems. Results: Using the single-view CAD system, the breast-based test sensitivities were 58% and 77% at the FP rates of 0.5 and 1.0 per image, respectively. With the four-view CAD system, the breast-based test sensitivities were improved to 76% and 87% at the corresponding FP rates, respectively. The improvement was found to be statistically significant (p<0.0001) by JAFROC analysis. Conclusions: The four-view information fusion approach that emulates radiologists' reading strategy significantly improves the performance of breast mass detection of the CAD system in comparison with the single-view approach.« less

Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts.

PubMed

Misawa, Masashi; Kudo, Shin-Ei; Mori, Yuichi; Takeda, Kenichi; Maeda, Yasuharu; Kataoka, Shinichi; Nakamura, Hiroki; Kudo, Toyoki; Wakamura, Kunihiko; Hayashi, Takemasa; Katagiri, Atsushi; Baba, Toshiyuki; Ishida, Fumio; Inoue, Haruhiro; Nimura, Yukitaka; Oda, Msahiro; Mori, Kensaku

2017-05-01

Real-time characterization of colorectal lesions during colonoscopy is important for reducing medical costs, given that the need for a pathological diagnosis can be omitted if the accuracy of the diagnostic modality is sufficiently high. However, it is sometimes difficult for community-based gastroenterologists to achieve the required level of diagnostic accuracy. In this regard, we developed a computer-aided diagnosis (CAD) system based on endocytoscopy (EC) to evaluate cellular, glandular, and vessel structure atypia in vivo. The purpose of this study was to compare the diagnostic ability and efficacy of this CAD system with the performances of human expert and trainee endoscopists. We developed a CAD system based on EC with narrow-band imaging that allowed microvascular evaluation without dye (ECV-CAD). The CAD algorithm was programmed based on texture analysis and provided a two-class diagnosis of neoplastic or non-neoplastic, with probabilities. We validated the diagnostic ability of the ECV-CAD system using 173 randomly selected EC images (49 non-neoplasms, 124 neoplasms). The images were evaluated by the CAD and by four expert endoscopists and three trainees. The diagnostic accuracies for distinguishing between neoplasms and non-neoplasms were calculated. ECV-CAD had higher overall diagnostic accuracy than trainees (87.8 vs 63.4%; [Formula: see text]), but similar to experts (87.8 vs 84.2%; [Formula: see text]). With regard to high-confidence cases, the overall accuracy of ECV-CAD was also higher than trainees (93.5 vs 71.7%; [Formula: see text]) and comparable to experts (93.5 vs 90.8%; [Formula: see text]). ECV-CAD showed better diagnostic accuracy than trainee endoscopists and was comparable to that of experts. ECV-CAD could thus be a powerful decision-making tool for less-experienced endoscopists.

Determination of Gentamicin Sulphate Composition and Related Substances in Pharmaceutical Preparations by LC with Charged Aerosol Detection

PubMed Central

Stypulkowska, Karolina; Fijalek, Zbigniew; Sarna, Katarzyna

2010-01-01

A new, simple and repeatable liquid chromatography method with charged aerosol detection (LC-CAD) for the determination of gentamicin sulphate composition and related substances has been developed. Gentamicin lacks of chromophores, therefore its determination is quite problematic. Using a universal CAD enables to achieve good separation without sample derivatization. Mass spectrometry was employed to confirm the LC-CAD peak profile. The proposed method was validated and applied for the determination of gentamicin sulphate composition and related substances in pharmaceutical preparations. PMID:21212825

External validation of Medicare claims codes for digital mammography and computer-aided detection.

PubMed

Fenton, Joshua J; Zhu, Weiwei; Balch, Steven; Smith-Bindman, Rebecca; Lindfors, Karen K; Hubbard, Rebecca A

2012-08-01

While Medicare claims are a potential resource for clinical mammography research or quality monitoring, the validity of key data elements remains uncertain. Claims codes for digital mammography and computer-aided detection (CAD), for example, have not been validated against a credible external reference standard. We matched Medicare mammography claims for women who received bilateral mammograms from 2003 to 2006 to corresponding mammography data from the Breast Cancer Surveillance Consortium (BCSC) registries in four U.S. states (N = 253,727 mammograms received by 120,709 women). We assessed the accuracy of the claims-based classifications of bilateral mammograms as either digital versus film and CAD versus non-CAD relative to a reference standard derived from BCSC data. Claims data correctly classified the large majority of film and digital mammograms (97.2% and 97.3%, respectively), yielding excellent agreement beyond chance (κ = 0.90). Claims data correctly classified the large majority of CAD mammograms (96.6%) but a lower percentage of non-CAD mammograms (86.7%). Agreement beyond chance remained high for CAD classification (κ = 0.83). From 2003 to 2006, the predictive values of claims-based digital and CAD classifications increased as the sample prevalences of each technology increased. Medicare claims data can accurately distinguish film and digital bilateral mammograms and mammograms conducted with and without CAD. The validity of Medicare claims data regarding film versus digital mammography and CAD suggests that these data elements can be useful in research and quality improvement. ©2012 AACR.

Two methods of Haustral fold detection from computed tomographic virtual colonoscopy images

NASA Astrophysics Data System (ADS)

Chowdhury, Ananda S.; Tan, Sovira; Yao, Jianhua; Linguraru, Marius G.; Summers, Ronald M.

2009-02-01

Virtual colonoscopy (VC) has gained popularity as a new colon diagnostic method over the last decade. VC is a new, less invasive alternative to the usually practiced optical colonoscopy for colorectal polyp and cancer screening, the second major cause of cancer related deaths in industrial nations. Haustral (colonic) folds serve as important landmarks for virtual endoscopic navigation in the existing computer-aided-diagnosis (CAD) system. In this paper, we propose and compare two different methods of haustral fold detection from volumetric computed tomographic virtual colonoscopy images. The colon lumen is segmented from the input using modified region growing and fuzzy connectedness. The first method for fold detection uses a level set that evolves on a mesh representation of the colon surface. The colon surface is obtained from the segmented colon lumen using the Marching Cubes algorithm. The second method for fold detection, based on a combination of heat diffusion and fuzzy c-means algorithm, is employed on the segmented colon volume. Folds obtained on the colon volume using this method are then transferred to the corresponding colon surface. After experimentation with different datasets, results are found to be promising. The results also demonstrate that the first method has a tendency of slight under-segmentation while the second method tends to slightly over-segment the folds.

Surface smoothing and template partitioning for cranial implant CAD

NASA Astrophysics Data System (ADS)

Min, Kyoung-june; Dean, David

2005-04-01

Employing patient-specific prefabricated implants can be an effective treatment for large cranial defects (i.e., > 25 cm2). We have previously demonstrated the use of Computer Aided Design (CAD) software that starts with the patient"s 3D head CT-scan. A template is accurately matched to the pre-detected skull defect margin. For unilateral cranial defects the template is derived from a left-to-right mirrored skull image. However, two problems arise: (1) slice edge artifacts generated during isosurface polygonalization are inherited by the final implant; and (2) partitioning (i.e., cookie-cutting) the implant surface from the mirrored skull image usually results in curvature discontinuities across the interface between the patient"s defect and the implant. To solve these problems, we introduce a novel space curve-to-surface partitioning algorithm following a ray-casting surface re-sampling and smoothing procedure. Specifically, the ray-cast re-sampling is followed by bilinear interpolation and low-pass filtering. The resulting surface has a highly regular grid-like topological structure of quadrilaterally arranged triangles. Then, we replace the regions to be partitioned with predefined sets of triangular elements thereby cutting the template surface to accurately fit the defect margin at high resolution and without surface curvature discontinuities. Comparisons of the CAD implants for five patients against the manually generated implant that the patient actually received show an average implant-patient gap of 0.45mm for the former and 2.96mm for the latter. Also, average maximum normalized curvature of interfacing surfaces was found to be smoother, 0.043, for the former than the latter, 0.097. This indicates that the CAD implants would provide a significantly better fit.

Modular Aero-Propulsion System Simulation

NASA Technical Reports Server (NTRS)

Parker, Khary I.; Guo, Ten-Huei

2006-01-01

The Modular Aero-Propulsion System Simulation (MAPSS) is a graphical simulation environment designed for the development of advanced control algorithms and rapid testing of these algorithms on a generic computational model of a turbofan engine and its control system. MAPSS is a nonlinear, non-real-time simulation comprising a Component Level Model (CLM) module and a Controller-and-Actuator Dynamics (CAD) module. The CLM module simulates the dynamics of engine components at a sampling rate of 2,500 Hz. The controller submodule of the CAD module simulates a digital controller, which has a typical update rate of 50 Hz. The sampling rate for the actuators in the CAD module is the same as that of the CLM. MAPSS provides a graphical user interface that affords easy access to engine-operation, engine-health, and control parameters; is used to enter such input model parameters as power lever angle (PLA), Mach number, and altitude; and can be used to change controller and engine parameters. Output variables are selectable by the user. Output data as well as any changes to constants and other parameters can be saved and reloaded into the GUI later.

A walk through the planned CS building. M.S. Thesis

NASA Technical Reports Server (NTRS)

Khorramabadi, Delnaz

1991-01-01

Using the architectural plan views of our future computer science building as test objects, we have completed the first stage of a Building walkthrough system. The inputs to our system are AutoCAD files. An AutoCAD converter translates the geometrical information in these files into a format suitable for 3D rendering. Major model errors, such as incorrect polygon intersections and random face orientations, are detected and fixed automatically. Interactive viewing and editing tools are provided to view the results, to modify and clean the model and to change surface attributes. Our display system provides a simple-to-use user interface for interactive exploration of buildings. Using only the mouse buttons, the user can move inside and outside the building and change floors. Several viewing and rendering options are provided, such as restricting the viewing frustum, avoiding wall collisions, and selecting different rendering algorithms. A plan view of the current floor, with the position of the eye point and viewing direction on it, is displayed at all times. The scene illumination can be manipulated, by interactively controlling intensity values for 5 light sources.

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

Implementation of combined SVM-algorithm and computer-aided perception feedback for pulmonary nodule detection

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; Rannou, Didier; Brennan, Patrick C.

2012-02-01

This pilot study examines the effect of a novel decision support system in medical image interpretation. This system is based on combining image spatial frequency properties and eye-tracking data in order to recognize over and under calling errors. Thus, before it can be implemented as a detection aided schema, training is required during which SVMbased algorithm learns to recognize FP from all reported outcomes, and, FN from all unreported prolonged dwelled regions. Eight radiologists inspected 50 PA chest radiographs with the specific task of identifying lung nodules. Twentyfive cases contained CT proven subtle malignant lesions (5-20mm), but prevalence was not known by the subjects, who took part in two sequential reading sessions, the second, without and with support system feedback. MCMR ROC DBM and JAFROC analyses were conducted and demonstrated significantly higher scores following feedback with p values of 0.04, and 0.03 respectively, highlighting significant improvements in radiology performance once feedback was used. This positive effect on radiologists' performance might have important implications for future CAD-system development.

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

PubMed

Yousefi, Mina; Krzyżak, Adam; Suen, Ching Y

2018-05-01

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images.

PubMed

Raghavendra, U; Gudigar, Anjan; Maithri, M; Gertych, Arkadiusz; Meiburger, Kristen M; Yeong, Chai Hong; Madla, Chakri; Kongmebhol, Pailin; Molinari, Filippo; Ng, Kwan Hoong; Acharya, U Rajendra

2018-04-01

Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be helpful to cross-verify the severity of nodules. This paper proposes a new CAD system to characterize thyroid nodules using optimized multi-level elongated quinary patterns. In this study, higher order spectral (HOS) entropy features extracted from these patterns appropriately distinguished benign and malignant nodules under particle swarm optimization (PSO) and support vector machine (SVM) frameworks. Our CAD algorithm achieved a maximum accuracy of 97.71% and 97.01% in private and public datasets respectively. The evaluation of this CAD system on both private and public datasets confirmed its effectiveness as a secondary tool in assisting radiological findings. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automated detection of diagnostically relevant regions in H&E stained digital pathology slides

NASA Astrophysics Data System (ADS)

Bahlmann, Claus; Patel, Amar; Johnson, Jeffrey; Ni, Jie; Chekkoury, Andrei; Khurd, Parmeshwar; Kamen, Ali; Grady, Leo; Krupinski, Elizabeth; Graham, Anna; Weinstein, Ronald

2012-03-01

We present a computationally efficient method for analyzing H&E stained digital pathology slides with the objective of discriminating diagnostically relevant vs. irrelevant regions. Such technology is useful for several applications: (1) It can speed up computer aided diagnosis (CAD) for histopathology based cancer detection and grading by an order of magnitude through a triage-like preprocessing and pruning. (2) It can improve the response time for an interactive digital pathology workstation (which is usually dealing with several GByte digital pathology slides), e.g., through controlling adaptive compression or prioritization algorithms. (3) It can support the detection and grading workflow for expert pathologists in a semi-automated diagnosis, hereby increasing throughput and accuracy. At the core of the presented method is the statistical characterization of tissue components that are indicative for the pathologist's decision about malignancy vs. benignity, such as, nuclei, tubules, cytoplasm, etc. In order to allow for effective yet computationally efficient processing, we propose visual descriptors that capture the distribution of color intensities observed for nuclei and cytoplasm. Discrimination between statistics of relevant vs. irrelevant regions is learned from annotated data, and inference is performed via linear classification. We validate the proposed method both qualitatively and quantitatively. Experiments show a cross validation error rate of 1.4%. We further show that the proposed method can prune ~90% of the area of pathological slides while maintaining 100% of all relevant information, which allows for a speedup of a factor of 10 for CAD systems.

Computer-aided diagnosis in radiological imaging: current status and future challenges

NASA Astrophysics Data System (ADS)

Doi, Kunio

2009-10-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. Many different types of CAD schemes are being developed for detection and/or characterization of various lesions in medical imaging, including conventional projection radiography, CT, MRI, and ultrasound imaging. Commercial systems for detection of breast lesions on mammograms have been developed and have received FDA approval for clinical use. CAD may be defined as a diagnosis made by a physician who takes into account the computer output as a "second opinion". The purpose of CAD is to improve the quality and productivity of physicians in their interpretation of radiologic images. The quality of their work can be improved in terms of the accuracy and consistency of their radiologic diagnoses. In addition, the productivity of radiologists is expected to be improved by a reduction in the time required for their image readings. The computer output is derived from quantitative analysis of radiologic images by use of various methods and techniques in computer vision, artificial intelligence, and artificial neural networks (ANNs). The computer output may indicate a number of important parameters, for example, the locations of potential lesions such as lung cancer and breast cancer, the likelihood of malignancy of detected lesions, and the likelihood of various diseases based on differential diagnosis in a given image and clinical parameters. In this review article, the basic concept of CAD is first defined, and the current status of CAD research is then described. In addition, the potential of CAD in the future is discussed and predicted.

Wavelet method for CT colonography computer-aided polyp detection.

PubMed

Li, Jiang; Van Uitert, Robert; Yao, Jianhua; Petrick, Nicholas; Franaszek, Marek; Huang, Adam; Summers, Ronald M

2008-08-01

Computed tomographic colonography (CTC) computer aided detection (CAD) is a new method to detect colon polyps. Colonic polyps are abnormal growths that may become cancerous. Detection and removal of colonic polyps, particularly larger ones, has been shown to reduce the incidence of colorectal cancer. While high sensitivities and low false positive rates are consistently achieved for the detection of polyps sized 1 cm or larger, lower sensitivities and higher false positive rates occur when the goal of CAD is to identify "medium"-sized polyps, 6-9 mm in diameter. Such medium-sized polyps may be important for clinical patient management. We have developed a wavelet-based postprocessor to reduce false positives for this polyp size range. We applied the wavelet-based postprocessor to CTC CAD findings from 44 patients in whom 45 polyps with sizes of 6-9 mm were found at segmentally unblinded optical colonoscopy and visible on retrospective review of the CT colonography images. Prior to the application of the wavelet-based postprocessor, the CTC CAD system detected 33 of the polyps (sensitivity 73.33%) with 12.4 false positives per patient, a sensitivity comparable to that of expert radiologists. Fourfold cross validation with 5000 bootstraps showed that the wavelet-based postprocessor could reduce the false positives by 56.61% (p <0.001), to 5.38 per patient (95% confidence interval [4.41, 6.34]), without significant sensitivity degradation (32/45, 71.11%, 95% confidence interval [66.39%, 75.74%], p=0.1713). We conclude that this wavelet-based postprocessor can substantially reduce the false positive rate of our CTC CAD for this important polyp size range.

Molecular cloning and functional analysis of nine cinnamyl alcohol dehydrogenase family members in Populus tomentosa.

PubMed

Chao, Nan; Liu, Shu-Xin; Liu, Bing-Mei; Li, Ning; Jiang, Xiang-Ning; Gai, Ying

2014-11-01

Nine CAD/CAD-like genes in P. tomentosa were classified into four classes based on expression patterns, phylogenetic analysis and biochemical properties with modification for the previous claim of SAD. Cinnamyl alcohol dehydrogenase (CAD) functions in monolignol biosynthesis and plays a critical role in wood development and defense. In this study, we isolated and cloned nine CAD/CAD-like genes in the Populus tomentosa genome. We investigated differential expression using microarray chips and found that PtoCAD1 was highly expressed in bud, root and vascular tissues (xylem and phloem) with the greatest expression in the root. Differential expression in tissues was demonstrated for PtoCAD3, PtoCAD6 and PtoCAD9. Biochemical analysis of purified PtoCADs in vitro indicated PtoCAD1, PtoCAD2 and PtoCAD8 had detectable activity against both coniferaldehyde and sinapaldehyde. PtoCAD1 used both substrates with high efficiency. PtoCAD2 showed no specific requirement for sinapaldehyde in spite of its high identity with so-called PtrSAD (sinapyl alcohol dehydrogenase). In addition, the enzymatic activity of PtoCAD1 and PtoCAD2 was affected by temperature. We classified these nine CAD/CAD-like genes into four classes: class I included PtoCAD1, which was a bone fide CAD with the highest activity; class II included PtoCAD2, -5, -7, -8, which might function in monolignol biosynthesis and defense; class III genes included PtoCAD3, -6, -9, which have a distinct expression pattern; class IV included PtoCAD12, which has a distinct structure. These data suggest divergence of the PtoCADs and its homologs, related to their functions. We propose genes in class II are a subset of CAD genes that evolved before angiosperms appeared. These results suggest CAD/CAD-like genes in classes I and II play a role in monolignol biosynthesis and contribute to our knowledge of lignin biosynthesis in P. tomentosa.

Diagnostic Accuracy of CT Coronary Angiography According to Pretest Probability of Coronary Artery Disease and Severity of Coronary Arterial Calcification: The CorE-64 International, Multicenter Study

PubMed Central

Arbab-Zadeh, Armin; Miller, Julie M; Rochitte, Carlos E; Dewey, Marc; Niinuma, Hiroyuki; Gottlieb, Ilan; Paul, Narinder; Clouse, Melvin E.; Shapiro, Edward P.; Hoe, John; Lardo, Albert C.; Bush, David E.; de Roos, Albert; Cox, Christopher; Brinker, Jeffrey; Lima, Joăo A. C.

2012-01-01

Objectives Assess the impact of patient population characteristics on accuracy by CT angiography (CTA) to detect obstructive coronary artery disease (CAD). Background The ability of CTA to exclude obstructive CAD in patients of different pretest probabilities and in presence of coronary calcification remains uncertain. Methods For the CorE-64 study 371 patients underwent CTA and cardiac catheterization for the detection of obstructive CAD defined as 50% or greater luminal stenosis by quantitative coronary angiography (QCA). This analysis includes 80 initially excluded patients with a calcium score ≥ 600. Area under the receiver-operating-characteristics curve (AUC) was used to evaluate CTA diagnostic accuracy compared to QCA in patients according to calcium score and pretest probability of CAD. Results Analysis of patient-based quantitative CTA accuracy revealed an AUC of 0.93 (95% confidence interval [CI] 0.90-0.95). AUC remained 0.93 (0.90-0.96) after excluding patients with known CAD but decreased to 0.81 (0.71-0.89) in patients with calcium score ≥ 600 (p=0.077). While AUC were similar (0.93, 0.92, and 0.93, respectively) for patients with intermediate, high pretest probability for CAD, and known CAD, negative predictive values were different: 0.90, 0.83, and 0.50, respectively. Negative predictive values decreased from 0.93 to 0.75 for patients with calcium score < or ≥ 100, respectively (p= 0.053). Conclusions Both pretest probability for CAD and coronary calcium scoring should be considered before using CTA for excluding obstructive CAD. CTA is less effective for this purpose in patients with calcium score ≥ 600 and in patients with a high pretest probability for obstructive CAD. PMID:22261160

Computer-aided detection system for chest radiography: reducing report turnaround times of examinations with abnormalities.

PubMed

Kao, E-Fong; Liu, Gin-Chung; Lee, Lo-Yeh; Tsai, Huei-Yi; Jaw, Twei-Shiun

2015-06-01

The ability to give high priority to examinations with pathological findings could be very useful to radiologists with large work lists who wish to first evaluate the most critical studies. A computer-aided detection (CAD) system for identifying chest examinations with abnormalities has therefore been developed. To evaluate the effectiveness of a CAD system on report turnaround times of chest examinations with abnormalities. The CAD system was designed to automatically mark chest examinations with possible abnormalities in the work list of radiologists interpreting chest examinations. The system evaluation was performed in two phases: two radiologists interpreted the chest examinations without CAD in phase 1 and with CAD in phase 2. The time information recorded by the radiology information system was then used to calculate the turnaround times. All chest examinations were reviewed by two other radiologists and were divided into normal and abnormal groups. The turnaround times for the examinations with pathological findings with and without the CAD system assistance were compared. The sensitivity and specificity of the CAD for chest abnormalities were 0.790 and 0.697, respectively, and use of the CAD system decreased the turnaround time for chest examinations with abnormalities by 44%. The turnaround times required for radiologists to identify chest examinations with abnormalities could be reduced by using the CAD system. This system could be useful for radiologists with large work lists who wish to first evaluate the most critical studies. © The Foundation Acta Radiologica 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection

PubMed Central

Su, Hai; Xing, Fuyong; Yang, Lin

2016-01-01

Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96. PMID:26812706

A handheld computer-aided diagnosis system and simulated analysis

NASA Astrophysics Data System (ADS)

Su, Mingjian; Zhang, Xuejun; Liu, Brent; Su, Kening; Louie, Ryan

2016-03-01

This paper describes a Computer Aided Diagnosis (CAD) system based on cellphone and distributed cluster. One of the bottlenecks in building a CAD system for clinical practice is the storage and process of mass pathology samples freely among different devices, and normal pattern matching algorithm on large scale image set is very time consuming. Distributed computation on cluster has demonstrated the ability to relieve this bottleneck. We develop a system enabling the user to compare the mass image to a dataset with feature table by sending datasets to Generic Data Handler Module in Hadoop, where the pattern recognition is undertaken for the detection of skin diseases. A single and combination retrieval algorithm to data pipeline base on Map Reduce framework is used in our system in order to make optimal choice between recognition accuracy and system cost. The profile of lesion area is drawn by doctors manually on the screen, and then uploads this pattern to the server. In our evaluation experiment, an accuracy of 75% diagnosis hit rate is obtained by testing 100 patients with skin illness. Our system has the potential help in building a novel medical image dataset by collecting large amounts of gold standard during medical diagnosis. Once the project is online, the participants are free to join and eventually an abundant sample dataset will soon be gathered enough for learning. These results demonstrate our technology is very promising and expected to be used in clinical practice.

Detection of benign prostatic hyperplasia nodules in T2W MR images using fuzzy decision forest

NASA Astrophysics Data System (ADS)

Lay, Nathan; Freeman, Sabrina; Turkbey, Baris; Summers, Ronald M.

2016-03-01

Prostate cancer is the second leading cause of cancer-related death in men MRI has proven useful for detecting prostate cancer, and CAD may further improve detection. One source of false positives in prostate computer-aided diagnosis (CAD) is the presence of benign prostatic hyperplasia (BPH) nodules. These nodules have a distinct appearance with a pseudo-capsule on T2 weighted MR images but can also resemble cancerous lesions in other sequences such as the ADC or high B-value images. Describing their appearance with hand-crafted heuristics (features) that also exclude the appearance of cancerous lesions is challenging. This work develops a method based on fuzzy decision forests to automatically learn discriminative features for the purpose of BPH nodule detection in T2 weighted images for the purpose of improving prostate CAD systems.

CATO: a CAD tool for intelligent design of optical networks and interconnects

NASA Astrophysics Data System (ADS)

Chlamtac, Imrich; Ciesielski, Maciej; Fumagalli, Andrea F.; Ruszczyk, Chester; Wedzinga, Gosse

1997-10-01

Increasing communication speed requirements have created a great interest in very high speed optical and all-optical networks and interconnects. The design of these optical systems is a highly complex task, requiring the simultaneous optimization of various parts of the system, ranging from optical components' characteristics to access protocol techniques. Currently there are no computer aided design (CAD) tools on the market to support the interrelated design of all parts of optical communication systems, thus the designer has to rely on costly and time consuming testbed evaluations. The objective of the CATO (CAD tool for optical networks and interconnects) project is to develop a prototype of an intelligent CAD tool for the specification, design, simulation and optimization of optical communication networks. CATO allows the user to build an abstract, possible incomplete, model of the system, and determine its expected performance. Based on design constraints provided by the user, CATO will automatically complete an optimum design, using mathematical programming techniques, intelligent search methods and artificial intelligence (AI). Initial design and testing of a CATO prototype (CATO-1) has been completed recently. The objective was to prove the feasibility of combining AI techniques, simulation techniques, an optical device library and a graphical user interface into a flexible CAD tool for obtaining optimal communication network designs in terms of system cost and performance. CATO-1 is an experimental tool for designing packet-switching wavelength division multiplexing all-optical communication systems using a LAN/MAN ring topology as the underlying network. The two specific AI algorithms incorporated are simulated annealing and a genetic algorithm. CATO-1 finds the optimal number of transceivers for each network node, using an objective function that includes the cost of the devices and the overall system performance.

Distributed Human Intelligence for Colonic Polyp Classification in Computer-aided Detection for CT Colonography

PubMed Central

Nguyen, Tan B.; Wang, Shijun; Anugu, Vishal; Rose, Natalie; McKenna, Matthew; Petrick, Nicholas; Burns, Joseph E.

2012-01-01

Purpose: To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography. Materials and Methods: This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed by using CAD software to identify 268 polyp candidates. Twenty knowledge workers (KWs) from a crowdsourcing platform labeled each polyp candidate as a true or false polyp. Two trials involving 228 KWs were conducted to assess reproducibility. Performance was assessed by comparing the area under the receiver operating characteristic curve (AUC) of KWs with the AUC of CAD for polyp classification. Results: The detection-level AUC for KWs was 0.845 ± 0.045 (standard error) in trial 1 and 0.855 ± 0.044 in trial 2. These were not significantly different from the AUC for CAD, which was 0.859 ± 0.043. When polyp candidates were stratified by difficulty, KWs performed better than CAD on easy detections; AUCs were 0.951 ± 0.032 in trial 1, 0.966 ± 0.027 in trial 2, and 0.877 ± 0.048 for CAD (P = .039 for trial 2). KWs who participated in both trials showed a significant improvement in performance going from trial 1 to trial 2; AUCs were 0.759 ± 0.052 in trial 1 and 0.839 ± 0.046 in trial 2 (P = .041). Conclusion: The performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification. © RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11110938/-/DC1 PMID:22274839

N-cadherin{sup +} HSCs in fetal liver exhibit higher long-term bone marrow reconstitution activity than N-cadherin{sup -} HSCs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Toyama, Hirofumi; Arai, Fumio; Hosokawa, Kentaro

Highlights: Black-Right-Pointing-Pointer High N-cad expression was detected in E12.5 mouse FL LT-HSCs (EPCR{sup +} LSK cells). Black-Right-Pointing-Pointer Immunohistochemically, N-cad{sup +} HSCs co-localized with sinusoidal ECs (Lyve-1{sup +} cells) in E12.5 FL, but these gradually detached in E15.5 and E18.5 FL. Black-Right-Pointing-Pointer N-cad{sup +} LSK cells in E12.5 FL exhibited higher LTR activity versus N-cad{sup -} LSK cells, which decreased in E15.5 and E18.5. Black-Right-Pointing-Pointer N-cad expression may confer high LTR activity to HSCs by facilitating interactions with the perisinusoidal niche in FL. -- Abstract: Adult hematopoietic stem cells (HSCs) are maintained in a microenvironment known as the stem cell niche.more » The regulation of HSCs in fetal liver (FL) and their niche, however, remains to be elucidated. In this study, we investigated the role of N-cadherin (N-cad) in the maintenance of HSCs during FL hematopoiesis. By using anti-N-cad antibodies (Abs) produced by our laboratory, we detected high N-cad expression in embryonic day 12.5 (E12.5) mouse FL HSCs, but not in E15.5 and E18.5 FL. Immunofluorescence staining revealed that N-cad{sup +}c-Kit{sup +} and N-cad{sup +} endothelial protein C receptor (EPCR){sup +} HSCs co-localized with Lyve-1{sup +} sinusoidal endothelial cells (ECs) in E12.5 FL and that some of these cells also expressed N-cad. However, N-cad{sup +} HSCs were also observed to detach from the perisinusoidal niche at E15.5 and E18.5, concomitant with a down-regulation of N-cad and an up-regulation of E-cadherin (E-cad) in hepatic cells. Moreover, EPCR{sup +} long-term (LT)-HSCs were enriched in the N-cad{sup +}Lin{sup -}Sca-1{sup +}c-Kit{sup +} (LSK) fraction in E12.5 FL, but not in E15.5 or E18.5 FL. In a long-term reconstitution (LTR) activity assay, higher engraftment associated with N-cad{sup +} LSK cells versus N-cad{sup -} LSK cells in E12.5 FL when transplanted into lethally irradiated recipient mice. However, the higher engraftment of N-cad{sup +} LSK cells decreased subsequently in E15.5 and E18.5 FL. It is possible that N-cad expression conferred higher LTR activity to HSCs by facilitating interactions with the perisinusoidal niche, especially at E12.5. The down-regulation of N-cad during FL hematopoiesis may help us better understand the regulation and mobility of HSCs before migration into BM.« less

A CAD approach to magnetic bearing design

NASA Technical Reports Server (NTRS)

Jeyaseelan, M.; Anand, D. K.; Kirk, J. A.

1988-01-01

A design methodology has been developed at the Magnetic Bearing Research Laboratory for designing magnetic bearings using a CAD approach. This is used in the algorithm of an interactive design software package. The package is a design tool developed to enable the designer to simulate the entire process of design and analysis of the system. Its capabilities include interactive input/modification of geometry, finding any possible saturation at critical sections of the system, and the design and analysis of a control system that stabilizes and maintains magnetic suspension.

South Asian ethnicity and cardiovascular risk: the known, the unknown, and the paradox.

PubMed

Ahmed, Emad; El-Menyar, Ayman

2015-05-01

South Asians (SAs), in their countries or after migration, are at high risk of coronary artery disease (CAD) and mortality compared to other ethnic groups. It has been shown that >90% of CAD global risk could be attributed to 9 modifiable risk factors (RFs) worldwide. However, these conventional RFs may not fully explain this high risk of CAD among SAs. Therefore, attention has been directed toward nonconventional RFs. In this narrative review, we evaluate the conventional and emerging cardiovascular RFs characterizing SAs. These factors may explain the high morbidity and mortality among SAs. Further prospective studies are urgently needed to set algorithms for the optimal management of these RFs in high-risk populations like SAs. © The Author(s) 2014.

Imaging of Spontaneous and Traumatic Cervical Artery Dissection : Comparison of Typical CT Angiographic Features.

PubMed

Sporns, Peter B; Niederstadt, Thomas; Heindel, Walter; Raschke, Michael J; Hartensuer, René; Dittrich, Ralf; Hanning, Uta

2018-01-26

Cervical artery dissection (CAD) is an important etiology of ischemic stroke and early recognition is vital to protect patients from the major complication of cerebral embolization by administration of anticoagulants. The etiology of arterial dissections differ and can be either spontaneous or traumatic. Even though the historical gold standard is still catheter angiography, recent studies suggest a good performance of computed tomography angiography (CTA) for detection of CAD. We conducted this research to evaluate the variety and frequency of possible imaging signs of spontaneous and traumatic CAD and to guide neuroradiologists' decision making. Retrospective review of the database of our multiple injured patients admitted to the Department of Trauma, Hand, and Reconstructive Surgery of the University Hospital Münster in Germany (a level 1 trauma center) for patients with traumatic CAD (tCAD) and of our stroke database (2008-2015) for patients with spontaneous CAD (sCAD) and CT/CTA on initial clinical work-up. All images were evaluated concerning specific and sensitive radiological features for dissection by two experienced neuroradiologists. Imaging features were compared between the two etiologies. This study included 145 patients (99 male, 46 female; 45 ± 18.8 years of age), consisting of 126 dissected arteries with a traumatic and 43 with spontaneous etiology. Intimal flaps were more frequently observed after traumatic etiology (58.1% tCADs, 6.9% sCADs; p < 0.001); additionally, multivessel dissections were much more frequent in trauma patients (3 sCADs, 21 tCADs) and only less than half (42%) of the patients with traumatic dissections showed cervical spine fractures. Neuroradiologists should be aware that intimal flaps and multivessel dissections are more common after a traumatic etiology. In addition, it seems important to conduct a CTA in a trauma setting, even if no cervical spine fracture is detected.

Effectiveness of computer aided detection for solitary pulmonary nodules

NASA Astrophysics Data System (ADS)

Yan, Jiayong; Li, Wenjie; Du, Xiangying; Lu, Huihai; Xu, Jianrong; Xu, Mantao; Rong, Dongdong

2009-02-01

This study is to investigate the incremental effect of using a high performance computer-aided detection (CAD) system in detection of solitary pulmonary nodules in chest radiographs. The Kodak Chest CAD system was evaluated by a panel of six radiologists at different levels of experience. The observer study consisted of two independent phases: readings without CAD and readings with assistance of CAD. The study was conducted over a set of chest radiographs comprising 150 cancer cases and 150 cancer-free cases. The actual sensitivity of the CAD system is 72% with 3.7 false positives per case. Receiver operating characteristic (ROC) analysis was used to assess the overall observer performance. The AUZ (area under ROC curve) showed a significantly improvement (P=0.0001) from 0.844 to 0.884 after using CAD. The ROC analysis was also applied for observer performances on nodules in different sizes and visibilities. The average AUZs are improved from 0.798 to 0.835 (P=0.0003) for 5-10mm nodules, 0.853 to 0.907 (P=0.001) for 10-15mm nodules, 0.864 to 0.897 (P=0.051) for 15-20 mm nodules and 0.859 to 0.896 (P=0.0342) for 20-30mm nodules, respectively. For different visibilities, the average AUZs are improved from 0.886 to 0.915 (P=0.0337), 0.803 to 0.840 (P=0.063), 0.830 to 0.893 (P=0.0001), and 0.813 to 0.847 (P=0.152), for nodules clearly visible, hidden by ribs, partially overlap with ribs, and overlap with other structures, respectively. These results showed that observer performance could be greatly improved when the CAD system is employed as a second reader, especially for small nodules and nodules occluded by ribs.

A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Jo, Byungdu; Choi, Seungyeon; Shin, Jungwook; Kim, Hee-Joung

2017-03-01

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium.

PubMed

Dodd, Lori E; Wagner, Robert F; Armato, Samuel G; McNitt-Gray, Michael F; Beiden, Sergey; Chan, Heang-Ping; Gur, David; McLennan, Geoffrey; Metz, Charles E; Petrick, Nicholas; Sahiner, Berkman; Sayre, Jim

2004-04-01

Cancer of the lung and bronchus is the leading fatal malignancy in the United States. Five-year survival is low, but treatment of early stage disease considerably improves chances of survival. Advances in multidetector-row computed tomography technology provide detection of smaller lung nodules and offer a potentially effective screening tool. The large number of images per exam, however, requires considerable radiologist time for interpretation and is an impediment to clinical throughput. Thus, computer-aided diagnosis (CAD) methods are needed to assist radiologists with their decision making. To promote the development of CAD methods, the National Cancer Institute formed the Lung Image Database Consortium (LIDC). The LIDC is charged with developing the consensus and standards necessary to create an image database of multidetector-row computed tomography lung images as a resource for CAD researchers. To develop such a prospective database, its potential uses must be anticipated. The ultimate applications will influence the information that must be included along with the images, the relevant measures of algorithm performance, and the number of required images. In this article we outline assessment methodologies and statistical issues as they relate to several potential uses of the LIDC database. We review methods for performance assessment and discuss issues of defining "truth" as well as the complications that arise when truth information is not available. We also discuss issues about sizing and populating a database.

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

PubMed Central

Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system. PMID:29687000

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

PubMed

Huang, Qinghua; Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

Automatic segmentation of tumor-laden lung volumes from the LIDC database

NASA Astrophysics Data System (ADS)

O'Dell, Walter G.

2012-03-01

The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling, and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm was developed and trained to on the first 100 datasets of the LIDC image database.

Automated detection of breast cancer in false-negative screening MRI studies from women at increased risk.

PubMed

Gubern-Mérida, Albert; Vreemann, Suzan; Martí, Robert; Melendez, Jaime; Lardenoije, Susanne; Mann, Ritse M; Karssemeijer, Nico; Platel, Bram

2016-02-01

To evaluate the performance of an automated computer-aided detection (CAD) system to detect breast cancers that were overlooked or misinterpreted in a breast MRI screening program for women at increased risk. We identified 40 patients that were diagnosed with breast cancer in MRI and had a prior MRI examination reported as negative available. In these prior examinations, 24 lesions could retrospectively be identified by two breast radiologists in consensus: 11 were scored as visible and 13 as minimally visible. Additionally, 120 normal scans were collected from 120 women without history of breast cancer or breast surgery participating in the same MRI screening program. A fully automated CAD system was applied to this dataset to detect malignant lesions. At 4 false-positives per normal case, the sensitivity for the detection of cancer lesions that were visible or minimally visible in retrospect in prior-negative examinations was 0.71 (95% CI=0.38-1.00) and 0.31 (0.07-0.59), respectively. A substantial proportion of cancers that were misinterpreted or overlooked in an MRI screening program was detected by a CAD system in prior-negative examinations. It has to be clarified with further studies if such a CAD system has an influence on the number of misinterpreted and overlooked cancers in clinical practice when results are given to a radiologist. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Comparison of ultraviolet detection and charged aerosol detection methods for liquid-chromatographic determination of protoescigenin.

PubMed

Filip, Katarzyna; Grynkiewicz, Grzegorz; Gruza, Mariusz; Jatczak, Kamil; Zagrodzki, Bogdan

2014-01-01

Escin, a complex mixture of pentacyclic triterpene saponins obtained from horse chestnut seeds extract (HCSE; Aesculus hippocastanum L.), constitutes a traditional herbal active substance of preparations (drugs) used for a treatment of chronic venous insufficiency and capillary blood vessel leakage. A new approach to exploitation of pharmacological potential of this saponin complex has been recently proposed, in which the β-escin mixture is perceived as a source of a hitherto unavailable raw material, pentacyclic triterpene aglycone-protoescigenin. Although many liquid chromatography methods are described in the literature for saponins determination, analysis of protoescigenin is barely mentioned. In this work, a new ultra-high performance liquid chromatography (UHPLC) method developed for protoescigenin quantification has been described. CAD (charged aerosol detection), as a relatively new detection method based on aerosol charging, has been applied in this method as an alternative to ultraviolet (UV) detection. The influence of individual parameters on CAD response and sensitivity was studied. The detection was performed using CAD and UV (200 nm) simultaneously and the results were compared with reference to linearity, accuracy, precision and limit of detection.

Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm.

PubMed

Beheshti, Iman; Demirel, Hasan; Matsuda, Hiroshi

2017-04-01

We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking and a genetic algorithm to analyze structural magnetic resonance imaging data; using this system, we can predict conversion of mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) at between one and three years before clinical diagnosis. The CAD system was developed in four stages. First, we used a voxel-based morphometry technique to investigate global and local gray matter (GM) atrophy in an AD group compared with healthy controls (HCs). Regions with significant GM volume reduction were segmented as volumes of interest (VOIs). Second, these VOIs were used to extract voxel values from the respective atrophy regions in AD, HC, stable MCI (sMCI) and progressive MCI (pMCI) patient groups. The voxel values were then extracted into a feature vector. Third, at the feature-selection stage, all features were ranked according to their respective t-test scores and a genetic algorithm designed to find the optimal feature subset. The Fisher criterion was used as part of the objective function in the genetic algorithm. Finally, the classification was carried out using a support vector machine (SVM) with 10-fold cross validation. We evaluated the proposed automatic CAD system by applying it to baseline values from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (160 AD, 162 HC, 65 sMCI and 71 pMCI subjects). The experimental results indicated that the proposed system is capable of distinguishing between sMCI and pMCI patients, and would be appropriate for practical use in a clinical setting. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

PubMed Central

El-Baz, Ayman; Beache, Garth M.; Gimel'farb, Georgy; Suzuki, Kenji; Okada, Kazunori; Elnakib, Ahmed; Soliman, Ahmed; Abdollahi, Behnoush

2013-01-01

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems. PMID:23431282

PACS-Based Computer-Aided Detection and Diagnosis

NASA Astrophysics Data System (ADS)

Huang, H. K. (Bernie); Liu, Brent J.; Le, Anh HongTu; Documet, Jorge

The ultimate goal of Picture Archiving and Communication System (PACS)-based Computer-Aided Detection and Diagnosis (CAD) is to integrate CAD results into daily clinical practice so that it becomes a second reader to aid the radiologist's diagnosis. Integration of CAD and Hospital Information System (HIS), Radiology Information System (RIS) or PACS requires certain basic ingredients from Health Level 7 (HL7) standard for textual data, Digital Imaging and Communications in Medicine (DICOM) standard for images, and Integrating the Healthcare Enterprise (IHE) workflow profiles in order to comply with the Health Insurance Portability and Accountability Act (HIPAA) requirements to be a healthcare information system. Among the DICOM standards and IHE workflow profiles, DICOM Structured Reporting (DICOM-SR); and IHE Key Image Note (KIN), Simple Image and Numeric Report (SINR) and Post-processing Work Flow (PWF) are utilized in CAD-HIS/RIS/PACS integration. These topics with examples are presented in this chapter.

Computer aided detection system for lung cancer using computer tomography scans

NASA Astrophysics Data System (ADS)

Mahesh, Shanthi; Rakesh, Spoorthi; Patil, Vidya C.

2018-04-01

Lung Cancer is a disease can be defined as uncontrolled cell growth in tissues of the lung. If we detect the Lung Cancer in its early stage, then that could be the key of its cure. In this work the non-invasive methods are studied for assisting in nodule detection. It supplies a Computer Aided Diagnosis System (CAD) for early detection of lung cancer nodules from the Computer Tomography (CT) images. CAD system is the one which helps to improve the diagnostic performance of radiologists in their image interpretations. The main aim of this technique is to develop a CAD system for finding the lung cancer using the lung CT images and classify the nodule as Benign or Malignant. For classifying cancer cells, SVM classifier is used. Here, image processing techniques have been used to de-noise, to enhance, for segmentation and edge detection of an image is used to extract the area, perimeter and shape of nodule. The core factors of this research are Image quality and accuracy.

An Empirical Evaluation of Five Circular Error Probable Estimation Techniques and a Method for Improving Them

DTIC Science & Technology

1993-03-10

template which runs a Romberg algorithm in the background to numerically integrate the BVN [12:257]. Appendix A als- lists the results from two other...for computing these values: a Taylor series expansion, the Romberg algorithm , and the CBN technique. Appendix A lists CEPpop. values for eleven...determining factor in this selection process. Of the 175 populations ex- amined in the experiment, the MathCAD version of the Romberg algorithm failed

3D Model Generation From the Engineering Drawing

NASA Astrophysics Data System (ADS)

Vaský, Jozef; Eliáš, Michal; Bezák, Pavol; Červeňanská, Zuzana; Izakovič, Ladislav

2010-01-01

The contribution deals with the transformation of engineering drawings in a paper form into a 3D computer representation. A 3D computer model can be further processed in CAD/CAM system, it can be modified, archived, and a technical drawing can be then generated from it as well. The transformation process from paper form to the data one is a complex and difficult one, particularly owing to the different types of drawings, forms of displayed objects and encountered errors and deviations from technical standards. The algorithm for 3D model generating from an orthogonal vector input representing a simplified technical drawing of the rotational part is described in this contribution. The algorithm was experimentally implemented as ObjectARX application in the AutoCAD system and the test sample as the representation of the rotational part was used for verificaton.

Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis

PubMed Central

Mazurowski, Maciej A; Lo, Joseph Y; Harrawood, Brian P; Tourassi, Georgia D

2011-01-01

Development of a computational decision aid for a new medical imaging modality typically is a long and complicated process. It consists of collecting data in the form of images and annotations, development of image processing and pattern recognition algorithms for analysis of the new images and finally testing of the resulting system. Since new imaging modalities are developed more rapidly than ever before, any effort for decreasing the time and cost of this development process could result in maximizing the benefit of the new imaging modality to patients by making the computer aids quickly available to radiologists that interpret the images. In this paper, we make a step in this direction and investigate the possibility of translating the knowledge about the detection problem from one imaging modality to another. Specifically, we present a computer-aided detection (CAD) system for mammographic masses that uses a mutual information-based template matching scheme with intelligently selected templates. We presented principles of template matching with mutual information for mammography before. In this paper, we present an implementation of those principles in a complete computer-aided detection system. The proposed system, through an automatic optimization process, chooses the most useful templates (mammographic regions of interest) using a large database of previously collected and annotated mammograms. Through this process, the knowledge about the task of detecting masses in mammograms is incorporated in the system. Then we evaluate whether our system developed for screen-film mammograms can be successfully applied not only to other mammograms but also to digital breast tomosynthesis (DBT) reconstructed slices without adding any DBT cases for training. Our rationale is that since mutual information is known to be a robust intermodality image similarity measure, it has high potential of transferring knowledge between modalities in the context of the mass detection task. Experimental evaluation of the system on mammograms showed competitive performance compared to other mammography CAD systems recently published in the literature. When the system was applied “as-is” to DBT, its performance was notably worse than that for mammograms. However, with a simple additional preprocessing step, the performance of the system reached levels similar to that obtained for mammograms. In conclusion, the presented CAD system not only performed competitively on screen-film mammograms but it also performed robustly on DBT showing that direct transfer of knowledge across breast imaging modalities for mass detection is in fact possible. PMID:21554985

Determination of the purity of valine by isocratic liquid chromatography coupled with charged aerosol detection (CAD).

PubMed

Lodi, A; Angus, M; Nap, C J; Skellern, G; Nicolas, A

2015-01-01

A liquid chromatography coupled with charged aerosol detection (LC-CAD) procedure; capable of separating and quantifying the most common impurities of valine at levels as low as 0.05 per cent (m/m), has been developed. The procedure is simple (isocratic), rapid, linear, sensitive and repeatable. It employs a widely available and inexpensive stationary phase (C18).

Using Motion Planning to Determine the Existence of an Accessible Route in a CAD Environment

ERIC Educational Resources Information Center

Pan, Xiaoshan; Han, Charles S.; Law, Kincho H.

2010-01-01

We describe an algorithm based on motion-planning techniques to determine the existence of an accessible route through a facility for a wheeled mobility device. The algorithm is based on LaValle's work on rapidly exploring random trees and is enhanced to take into consideration the particularities of the accessible route domain. Specifically, the…

Positron Emission Tomography-Determined Hyperemic Flow, Myocardial Flow Reserve, and Flow Gradient—Quo Vadis?

PubMed Central

Leucker, Thorsten M.; Valenta, Ines; Schindler, Thomas Hellmut

2017-01-01

Positron emission tomography/computed tomography (PET/CT) applied with positron-emitting flow tracers such as 13N-ammonia and 82Rubidium enables the quantification of both myocardial perfusion and myocardial blood flow (MBF) in milliliters per gram per minute for coronary artery disease (CAD) detection and characterization. The detection of a regional myocardial perfusion defect during vasomotor stress commonly identifies the culprit lesion or most severe epicardial narrowing, whereas adding regional hyperemic MBFs, myocardial flow reserve (MFR), and/or longitudinal flow decrease may also signify less severe but flow-limiting stenosis in multivessel CAD. The addition of regional hyperemic flow parameters, therefore, may afford a comprehensive identification and characterization of flow-limiting effects of multivessel CAD. The non-specific origin of decreases in hyperemic MBFs and MFR, however, prompts an evaluation and interpretation of regional flow in the appropriate context with the presence of obstructive CAD. Conversely, initial results of the assessment of a longitudinal hyperemic flow gradient suggest this novel flow parameter to be specifically related to increases in CAD caused epicardial resistance. The concurrent assessment of myocardial perfusion and several hyperemic flow parameters with PET/CT may indeed open novel avenues of precision medicine to guide coronary revascularization procedures that may potentially lead to a further improvement in cardiovascular outcomes in CAD patients. PMID:28770213

N-terminal pro-B-type natriuretic peptide diagnostic algorithm versus American Heart Association algorithm for Kawasaki disease.

PubMed

Dionne, Audrey; Meloche-Dumas, Léamarie; Desjardins, Laurent; Turgeon, Jean; Saint-Cyr, Claire; Autmizguine, Julie; Spigelblatt, Linda; Fournier, Anne; Dahdah, Nagib

2017-03-01

Diagnosis of Kawasaki disease (KD) can be challenging in the absence of a confirmatory test or pathognomonic finding, especially when clinical criteria are incomplete. We recently proposed serum N-terminal pro-B-type natriuretic peptide (NT-proBNP) as an adjunctive diagnostic test. We retrospectively tested a new algorithm to help KD diagnosis based on NT-proBNP, coronary artery dilation (CAD) at onset, and abnormal serum albumin or C-reactive protein (CRP). The goal was to assess the performance of the algorithm and compare its performance with that of the 2004 American Heart Association (AHA)/American Academy of Pediatrics (AAP) algorithm. The algorithm was tested on 124 KD patients with NT-proBNP measured on admission at the present institutions between 2007 and 2013. Age at diagnosis was 3.4 ± 3.0 years, with a median of five diagnostic criteria; and 55 of the 124 patients (44%) had incomplete KD. CA complications occurred in 64 (52%), with aneurysm in 14 (11%). Using this algorithm, 120/124 (97%) were to be treated, based on high NT-proBNP alone for 79 (64%); on onset CAD for 14 (11%); and on high CRP or low albumin for 27 (22%). Using the AHA/AAP algorithm, 22/47 (47%) of the eligible patients with incomplete KD would not have been referred for treatment, compared with 3/55 (5%) with the NT-proBNP algorithm (P < 0.001). This NT-proBNP-based algorithm is efficient to identify and treat patients with KD, including those with incomplete KD. This study paves the way for a prospective validation trial of the algorithm. © 2016 Japan Pediatric Society.

Prevalence of severe subclinical coronary artery disease on cardiac CT and MRI in patients with extra-cardiac arterial disease.

PubMed

den Dekker, M A M; van den Dungen, J J A M; Tielliu, I F J; Tio, R A; Jaspers, M M J J R; Oudkerk, M; Vliegenthart, R

2013-12-01

Patients with extra-cardiac arterial disease (ECAD) are at high risk of coronary artery disease (CAD). Prevalence of silent, significant CAD in patients with stenotic or aneurysmal ECAD was examined. Early detection and treatment may reduce CAD mortality in this high-risk group. ECAD patients without cardiac complaints underwent computed tomography (CT) for calcium scoring, coronary CT angiography (cCTA) if calcium score was 1,000 or under, and adenosine perfusion magnetic resonance imaging (APMR) if there was no left main stenosis. Significant CAD was defined as calcium score over 1,000, cCTA-detected coronary stenosis of at least 50% lumen diameter, and/or APMR-detected inducible myocardial ischemia. In cases of left main stenosis (or equivalent) or myocardial ischemia, patients were referred to a cardiologist. The prevalence of significant CAD was 56.8% (95% CI 47.5 to 66.0). One-hundred and eleven patients were included. Eighty-four patients (76%) had stenotic ECAD, and 27 (24%) had aneurysmal disease. In patients with stenotic ECAD, significant coronary stenosis was present in 32 (38%) and inducible ischemia in eight (12%). Corresponding results in aneurysmal ECAD were eight (30%) and two (11%), respectively (p for difference >.05). Sixteen (19%) patients with stenotic and six (22%) with aneurysmal ECAD were referred to a cardiologist, with subsequent cardiac intervention in seven (44%) and three (50%), respectively (both p >.05). Patients with stenotic or aneurysmal ECAD have a high prevalence of silent, significant CAD. Copyright © 2013 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.

Distortion of CAD-CAM-fabricated implant-fixed titanium and zirconia complete dental prosthesis frameworks.

PubMed

Al-Meraikhi, Hadi; Yilmaz, Burak; McGlumphy, Edwin; Brantley, William A; Johnston, William M

2018-01-01

Computer-aided design and computer-aided manufacturing (CAD-CAM)-fabricated titanium and zirconia implant-supported fixed dental prostheses have become increasingly popular for restoring patients with complete edentulism. However, the distortion level of these frameworks is not well known. The purpose of this in vitro study was to compare the 3-dimensional (3D) distortion of CAD-CAM zirconia and titanium implant-fixed screw-retained complete dental prostheses. A master edentulous model with 4 implants at the positions of the maxillary first molars and canines was used. Multiunit abutments (Nobel Biocare) secured to the model were digitally scanned using scan bodies and a laboratory scanner (S600 ARTI; Zirkonzahn). Titanium (n=5) and zirconia (n=5) frameworks were milled using a CAD-CAM system (Zirkonzahn M1; Zirkonzahn). All frameworks were scanned using an industrial computed tomography (CT) scanner (Nikon/X-Tek XT H 225kV MCT Micro-Focus). The direct CT scans were reconstructed to generate standard tessellation language (STL) files. To calculate the 3D distortion of the frameworks, STL files of the CT scans were aligned to the CAD model using a sum of the least squares best-fit algorithm. Surface comparison points were placed on the CAD model on the midfacial aspect of all teeth. The 3D distortion of each direct scan to the CAD model was calculated. In addition, color maps of the scan-to-CAD comparison were constructed using a ±0.500 mm color scale range. Both materials exhibited distortion; however, no significant difference was found in the amount of distortion from the CAD model between the materials (P=.747). Absolute values of deviations from the CAD model were evident in the x and y plane and less so in the z direction. Zirconia and titanium frameworks showed similar 3D distortion compared with the CAD model for the tested CAD-CAM and implant systems. The distortion was more pronounced in the horizontal and sagittal plane than in the vertical plane. Copyright © 2017 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Canine adenovirus type 1 in a fennec fox (Vulpes zerda).

PubMed

Choi, Jeong-Won; Lee, Hyun-Kyoung; Kim, Seong-Hee; Kim, Yeon-Hee; Lee, Kyoung-Ki; Lee, Myoung-Heon; Oem, Jae-Ku

2014-12-01

A 10-mo-old female fennec fox (Vulpes zerda) with drooling suddenly died and was examined postmortem. Histologic examination of different tissue samples was performed. Vacuolar degeneration and diffuse fatty change were observed in the liver. Several diagnostic methods were used to screen for canine parvovirus, canine distemper virus, canine influenza virus, canine coronavirus, canine parainfluenza virus, and canine adenovirus (CAdV). Only CAdV type 1 (CAdV-1) was detected in several organs (liver, lung, brain, kidney, spleen, and heart), and other viruses were not found. CAdV-1 was confirmed by virus isolation and nucleotide sequencing.

Low Complexity Models to improve Incomplete Sensitivities for Shape Optimization

NASA Astrophysics Data System (ADS)

Stanciu, Mugurel; Mohammadi, Bijan; Moreau, Stéphane

2003-01-01

The present global platform for simulation and design of multi-model configurations treat shape optimization problems in aerodynamics. Flow solvers are coupled with optimization algorithms based on CAD-free and CAD-connected frameworks. Newton methods together with incomplete expressions of gradients are used. Such incomplete sensitivities are improved using reduced models based on physical assumptions. The validity and the application of this approach in real-life problems are presented. The numerical examples concern shape optimization for an airfoil, a business jet and a car engine cooling axial fan.

Learning-based image preprocessing for robust computer-aided detection

NASA Astrophysics Data System (ADS)

Raghupathi, Laks; Devarakota, Pandu R.; Wolf, Matthias

2013-03-01

Recent studies have shown that low dose computed tomography (LDCT) can be an effective screening tool to reduce lung cancer mortality. Computer-aided detection (CAD) would be a beneficial second reader for radiologists in such cases. Studies demonstrate that while iterative reconstructions (IR) improve LDCT diagnostic quality, it however degrades CAD performance significantly (increased false positives) when applied directly. For improving CAD performance, solutions such as retraining with newer data or applying a standard preprocessing technique may not be suffice due to high prevalence of CT scanners and non-uniform acquisition protocols. Here, we present a learning-based framework that can adaptively transform a wide variety of input data to boost an existing CAD performance. This not only enhances their robustness but also their applicability in clinical workflows. Our solution consists of applying a suitable pre-processing filter automatically on the given image based on its characteristics. This requires the preparation of ground truth (GT) of choosing an appropriate filter resulting in improved CAD performance. Accordingly, we propose an efficient consolidation process with a novel metric. Using key anatomical landmarks, we then derive consistent feature descriptors for the classification scheme that then uses a priority mechanism to automatically choose an optimal preprocessing filter. We demonstrate CAD prototype∗ performance improvement using hospital-scale datasets acquired from North America, Europe and Asia. Though we demonstrated our results for a lung nodule CAD, this scheme is straightforward to extend to other post-processing tools dedicated to other organs and modalities.

Non-invasive assessment of low- and intermediate-risk patients with chest pain

PubMed Central

Balfour, Pelbreton C.; Gonzalez, Jorge A.; Kramer, Christopher M.

2016-01-01

Coronary artery disease (CAD) remains a significant global public health burden despite advancements in prevention and therapeutic strategies. Common non-invasive imaging modalities, anatomic and functional, are available for the assessment of patients with stable chest pain. Exercise electrocardiography is a long-standing method for evaluation for CAD and remains the initial test for the majority of patients who can exercise adequately with a baseline interpretable electrocardiogram. The addition of cardiac imaging to exercise testing provides incremental benefit for accurate diagnosis for CAD and is particularly useful in patients who are unable to exercise adequately and/or have uninterpretable electrocardiograms. Radionuclide myocardial perfusion imaging and echocardiography with exercise or pharmacological stress provide high sensitivity and specificity in the detection and further risk stratification of patients with CAD. Recently, coronary computed tomography angiography has demonstrated its growing role to rule out significant CAD given its high negative predictive value. Although less available, stress cardiac magnetic resonance provides a comprehensive assessment of cardiac structure and function and provides a high diagnostic accuracy in the detection of CAD. The utilization of non-invasive testing is complex due to various advantages and limitations, particularly in the assessment of low- and intermediate-risk patients with chest pain, where no single study is suitable for all patients. This review will describe currently available non-invasive modalities, along with current evidence-based guidelines and appropriate use criteria in the assessment of low- and intermediate-risk patients with suspected, stable CAD. PMID:27717538

SPECT myocardial perfusion imaging as an adjunct to coronary calcium score for the detection of hemodynamically significant coronary artery stenosis

PubMed Central

2012-01-01

Background Coronary artery calcifications (CAC) are markers of coronary atherosclerosis, but do not correlate well with stenosis severity. This study intended to evaluate clinical situations where a combined approach of coronary calcium scoring (CS) and nuclear stress test (SPECT-MPI) is useful for the detection of relevant CAD. Methods Patients with clinical indication for invasive coronary angiography (ICA) were included into our study during 08/2005-09/2008. At first all patients underwent CS procedure as part of the study protocol performed by either using a multidetector computed tomography (CT) scanner or a dual-source CT imager. CAC were automatically defined by dedicated software and the Agatston score was semi-automatically calculated. A stress-rest SPECT-MPI study was performed afterwards and scintigraphic images were evaluated quantitatively. Then all patients underwent ICA. Thereby significant CAD was defined as luminal stenosis ≥75% in quantitative coronary analysis (QCA) in ≥1 epicardial vessel. To compare data lacking Gaussian distribution an unpaired Wilcoxon-Test (Mann–Whitney) was used. Otherwise a Students t-test for unpaired samples was applied. Calculations were considered to be significant at a p-value of <0.05. Results We consecutively included 351 symptomatic patients (mean age: 61.2±12.3 years; range: 18–94 years; male: n=240) with a mean Agatston score of 258.5±512.2 (range: 0–4214). ICA verified exclusion of significant CAD in 66/67 (98.5%) patients without CAC. CAC was detected in remaining 284 patients. In 132/284 patients (46.5%) with CS>0 significant CAD was confirmed by ICA, and excluded in 152/284 (53.5%) patients. Sensitivity for CAD detection by CS alone was calculated as 99.2%, specificity was 30.3%, and negative predictive value was 98.5%. An additional SPECT in patients with CS>0 increased specificity to 80.9% while reducing sensitivity to 87.9%. Diagnostic accuracy was 84.2%. Conclusions In patients without CS=0 significant CAD can be excluded with a high negative predictive value by CS alone. An additional SPECT-MPI in those patients with CS>0 leads to a high diagnostic accuracy for the detection of CAD while reducing the number of patients needing invasive diagnostic procedure. PMID:23206557

[Effect of forced E-cadherin expression on adhesion and proliferation of human breast carcinoma cells].

PubMed

Yang, Li-Juan; Liu, Yu-Qin; Gu, Bei; Bian, Xiao-Cui; Feng, Hai-Liang; Yang, Zhen-Li; Liu, Yan-Yan

2010-12-01

To investigate the role that E-cadherin (E-cad) plays on cell adhesion and proliferation of human breast carcinoma. E-cad expression vector was transfected into an E-cad-negative human breast carcinoma MDA-MB-231 cells. G418 was used to screen positive clones. E-cad, β-catenin (β-cat) and cyclin D1 expressions of these clones were confirmed by Western blot. Their cell-cell and cell-matrix adhesion abilities were detected. E-cad/β-catenin interaction was confirmed by immunoprecipitation. Cell proliferation was evaluated by MTT. Cell apoptosis was analyzed by flow cytometry. Direct two-step immunocytochemistry was used to detect the localization of β-cat. E-cad(+) cell strains Ecad-231-7 and Ecad-231-9 were established. When cultured in ultra-low-binding dishes Ecad-231 cells grow in suspension while Ecad-231-7 and Ecad-231-9 cells grow in large clamps. When co-cultured with HCT116 cells, the average adhesion rates at 30 min are 39.0%, 60.0% and 59.5% for MDA-MB-231, Ecad-231-7 and Ecad-231-9 respectively. The average detachment rates by EDTA for 5 min are 37.4%, 4.2% and 7.4% respectively. So E-cad expression enhanced hemotypic and heterotypic cell-cell adhesion and cell-matrix adhesion. Forced exogenously expressed E-cad could combine with endogenous β-cat, whereas down stream cyclin D1 expression was significantly decreased, as evidenced by Western blot. The rates of cell apoptosis of MDA-MB-231, Ecad-231-7 and Ecad-231-9 were 1.8%, 2.0% and 2.1%. Expression of E-cad had no obvious effect on the apoptosis of tumor cells with regular culture. β-cat increased in the cytoplasma. Two monoclonal tumor cell strains (Ecad-231-7 and Ecad-231-9) stably expressing E-cad were successfully established. E-cad could enhance adhesion and inhibit proliferation of human breast carcinoma cells through a pathway involving β-cat and cyclin D1.

Computer-Aided Diagnostic (CAD) Scheme by Use of Contralateral Subtraction Technique

NASA Astrophysics Data System (ADS)

Nagashima, Hiroyuki; Harakawa, Tetsumi

We developed a computer-aided diagnostic (CAD) scheme for detection of subtle image findings of acute cerebral infarction in brain computed tomography (CT) by using a contralateral subtraction technique. In our computerized scheme, the lateral inclination of image was first corrected automatically by rotating and shifting. The contralateral subtraction image was then derived by subtraction of reversed image from original image. Initial candidates for acute cerebral infarctions were identified using the multiple-thresholding and image filtering techniques. As the 1st step for removing false positive candidates, fourteen image features were extracted in each of the initial candidates. Halfway candidates were detected by applying the rule-based test with these image features. At the 2nd step, five image features were extracted using the overlapping scale with halfway candidates in interest slice and upper/lower slice image. Finally, acute cerebral infarction candidates were detected by applying the rule-based test with five image features. The sensitivity in the detection for 74 training cases was 97.4% with 3.7 false positives per image. The performance of CAD scheme for 44 testing cases had an approximate result to training cases. Our CAD scheme using the contralateral subtraction technique can reveal suspected image findings of acute cerebral infarctions in CT images.

Moats and Drawbridges: An Isolation Primitive for Reconfigurable Hardware Based Systems

DTIC Science & Technology

2007-05-01

these systems, and after being run through an optimizing CAD tool the resulting circuit is a single entangled mess of gates and wires. To prevent the...translates MATLAB [48] algorithms into HDL, logic synthesis translates this HDL into a netlist, a synthesis tool uses a place-and-route algorithm to...Core Soft Core µ Soft P Core µP Core Hard Soft Algorithms MATLAB gcc ExecutableC Code HDL C Code Bitstream Place and Route NetlistLogic Synthesis EDK µP

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

2017-09-27

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Additive value of 3T cardiovascular magnetic resonance coronary angiography for detecting coronary artery disease.

PubMed

Zhang, Lijun; Song, Xiantao; Dong, Li; Li, Jianan; Dou, Ruiyu; Fan, Zhanming; An, Jing; Li, Debiao

2018-04-30

The purpose of the work was to evaluate the incremental diagnostic value of free-breathing, contrast-enhanced, whole-heart, 3 T cardiovascular magnetic resonance coronary angiography (CE-MRCA) to stress/rest myocardial perfusion imaging (MPI) and late gadolinium enhancement (LGE) imaging for detecting coronary artery disease (CAD). Fifty-one patients with suspected CAD underwent a comprehensive cardiovascular magnetic resonance (CMR) examination (CE-MRCA, MPI, and LGE). The additive diagnostic value of MRCA to MPI and LGE was evaluated using invasive x-ray coronary angiography (XA) as the standard for defining functionally significant CAD (≥ 50% stenosis in vessels > 2 mm in diameter). 90.2% (46/51) patients (54.0 ± 11.5 years; 71.7% men) completed CE-MRCA successfully. On per-patient basis, compared to MPI/LGE alone or MPI alone, the addition of MRCA resulted in higher sensitivity (100% vs. 76.5%, p < 0.01), no change in specificity (58.3% vs. 66.7%, p = 0.6), and higher accuracy (89.1% vs 73.9%, p < 0.01) for CAD detection (prevalence = 73.9%). Compared to LGE alone, the addition of CE-MRCA resulted in higher sensitivity (97.1% vs. 41.2%, p < 0.01), inferior specificity (83.3% vs. 91.7%, p = 0.02), and higher diagnostic accuracy (93.5% vs. 54.3%, p < 0.01). The inclusion of successful free-breathing, whole-heart, 3 T CE-MRCA significantly improved the sensitivity and diagnostic accuracy as compared to MPI and LGE alone for CAD detection.

Computer-aided diagnosis of liver tumors on computed tomography images.

PubMed

Chang, Chin-Chen; Chen, Hong-Hao; Chang, Yeun-Chung; Yang, Ming-Yang; Lo, Chung-Ming; Ko, Wei-Chun; Lee, Yee-Fan; Liu, Kao-Lang; Chang, Ruey-Feng

2017-07-01

Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images. A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation. The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713. Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system. Copyright © 2017 Elsevier B.V. All rights reserved.

The interplay of exercise heart rate and blood pressure as a predictor of coronary artery disease and arterial hypertension.

PubMed

Michaelides, Andreas P; Liakos, Charalampos I; Vyssoulis, Gregory P; Chatzistamatiou, Evangelos I; Markou, Maria I; Tzamou, Vanessa; Stefanadis, Christodoulos I

2013-03-01

Delayed blood pressure (BP) and heart rate (HR) decline at recovery post-exercise are independent predictors of incident coronary artery disease (CAD). Delayed BP recovery and exaggerated BP response to exercise are independent predictors of future arterial hypertension (AH). This study sought to examine whether the combination of two exercise parameters provides additional prognostic value than each variable alone. A total of 830 non-CAD patients (374 normotensive) were followed for new-onset CAD and/or AH for 5 years after diagnostic exercise testing (ET). At the end of follow-up, patients without overt CAD underwent a second ET. Stress imaging modalities and coronary angiography, where appropriate, ruled out CAD. New-onset CAD was detected in 110 participants (13.3%) whereas AH was detected in 41 former normotensives (11.0%). The adjusted (for confounders) relative risk (RR) of CAD in abnormal BP and HR recovery patients was 1.95 (95% confidence interval [CI], 1.28-2.98; P=.011) compared with delayed BP and normal HR recovery patients and 1.71 (95% CI, 1.08-2.75; P=.014) compared with normal BP and delayed HR recovery patients. The adjusted RR of AH in normotensives with abnormal BP recovery and response was 2.18 (95% CI, 1.03-4.72; P=.047) compared with delayed BP recovery and normal BP response patients and 2.48 (95% CI, 1.14-4.97; P=.038) compared with normal BP recovery and exaggerated BP response individuals. In conclusion, the combination of two independent exercise predictors is an even stronger CAD/AH predictor than its components. © 2012 Wiley Periodicals, Inc.

B-Spline Filtering for Automatic Detection of Calcification Lesions in Mammograms

NASA Astrophysics Data System (ADS)

Bueno, G.; Sánchez, S.; Ruiz, M.

2006-10-01

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease.

PubMed

Emoto, Takuo; Yamashita, Tomoya; Kobayashi, Toshio; Sasaki, Naoto; Hirota, Yushi; Hayashi, Tomohiro; So, Anna; Kasahara, Kazuyuki; Yodoi, Keiko; Matsumoto, Takuya; Mizoguchi, Taiji; Ogawa, Wataru; Hirata, Ken-Ichi

2017-01-01

The association between atherosclerosis and gut microbiota has been attracting increased attention. We previously demonstrated a possible link between gut microbiota and coronary artery disease. Our aim of this study was to clarify the gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism (T-RFLP). This study included 39 coronary artery disease (CAD) patients and 30 age- and sex- matched no-CAD controls (Ctrls) with coronary risk factors. Bacterial DNA was extracted from their fecal samples and analyzed by T-RFLP and data mining analysis using the classification and regression algorithm. Five additional CAD patients were newly recruited to confirm the reliability of this analysis. Data mining analysis could divide the composition of gut microbiota into 2 characteristic nodes. The CAD group was classified into 4 CAD pattern nodes (35/39 = 90 %), while the Ctrl group was classified into 3 Ctrl pattern nodes (28/30 = 93 %). Five additional CAD samples were applied to the same dividing model, which could validate the accuracy to predict the risk of CAD by data mining analysis. We could demonstrate that operational taxonomic unit 853 (OTU853), OTU657, and OTU990 were determined important both by the data mining method and by the usual statistical comparison. We classified the gut microbiota profiles in coronary artery disease patients using data mining analysis of T-RFLP data and demonstrated the possibility that gut microbiota is a diagnostic marker of suffering from CAD.

The computation of all plane/surface intersections for CAD/CAM applications

NASA Technical Reports Server (NTRS)

Hoitsma, D. H., Jr.; Roche, M.

1984-01-01

The problem of the computation and display of all intersections of a given plane with a rational bicubic surface patch for use on an interactive CAD/CAM system is examined. The general problem of calculating all intersections of a plane and a surface consisting of rational bicubic patches is reduced to the case of a single generic patch by applying a rejection algorithm which excludes all patches that do not intersect the plane. For each pertinent patch the algorithm presented computed the intersection curves by locating an initial point on each curve, and computes successive points on the curve using a tolerance step equation. A single cubic equation solver is used to compute the initial curve points lying on the boundary of a surface patch, and the method of resultants as applied to curve theory is used to determine critical points which, in turn, are used to locate initial points that lie on intersection curves which are in the interior of the patch. Examples are given to illustrate the ability of this algorithm to produce all intersection curves.

Auto-recognition of surfaces and auto-generation of material removal volume for finishing process

NASA Astrophysics Data System (ADS)

Kataraki, Pramod S.; Salman Abu Mansor, Mohd

2018-03-01

Auto-recognition of a surface and auto-generation of material removal volumes for the so recognised surfaces has become a need to achieve successful downstream manufacturing activities like automated process planning and scheduling. Few researchers have contributed to generation of material removal volume for a product but resulted in material removal volume discontinuity between two adjacent material removal volumes generated from two adjacent faces that form convex geometry. The need for limitation free material removal volume generation was attempted and an algorithm that automatically recognises computer aided design (CAD) model’s surface and also auto-generate material removal volume for finishing process of the recognised surfaces was developed. The surfaces of CAD model are successfully recognised by the developed algorithm and required material removal volume is obtained. The material removal volume discontinuity limitation that occurred in fewer studies is eliminated.

Evaluation of computer-aided detection of lesions in mammograms obtained with a digital phase-contrast mammography system.

PubMed

Tanaka, Toyohiko; Nitta, Norihisa; Ohta, Shinichi; Kobayashi, Tsuyoshi; Kano, Akiko; Tsuchiya, Keiko; Murakami, Yoko; Kitahara, Sawako; Wakamiya, Makoto; Furukawa, Akira; Takahashi, Masashi; Murata, Kiyoshi

2009-12-01

A computer-aided detection (CAD) system was evaluated for its ability to detect microcalcifications and masses on images obtained with a digital phase-contrast mammography (PCM) system, a system characterised by the sharp images provided by phase contrast and by the high resolution of 25-μm-pixel mammograms. Fifty abnormal and 50 normal mammograms were collected from about 3,500 mammograms and printed on film for reading on a light box. Seven qualified radiologists participated in an observer study based on receiver operating characteristic (ROC) analysis. The average of the areas under ROC curve (AUC) values for the ROC analysis with and without CAD were 0.927 and 0.897 respectively (P = 0.015). The AUC values improved from 0.840 to 0.888 for microcalcifications (P = 0.034) and from 0.947 to 0.962 for masses (P = 0.025) respectively. The application of CAD to the PCM system is a promising approach for the detection of breast cancer in its early stages.

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.

PubMed

Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A; Wei, Jun; Cha, Kenny

2016-12-01

Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bueno, G.; Ruiz, M.; Sanchez, S

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Strain-Encoded Cardiac Magnetic Resonance Imaging as an Adjunct for Dobutamine Stress Testing. Incremental Value to Conventional Wall Motion Analysis

PubMed Central

Korosoglou, Grigorios; Lossnitzer, Dirk; Schellberg, Dieter; Lewien, Antje; Wochele, Angela; Schaeufele, Tim; Neizel, Mirja; Steen, Henning; Giannitsis, Evangelos; Katus, Hugo A.; Osman, Nael F.

2009-01-01

Background High-dose dobutamine stress magnetic resonance imaging (DS-MRI) is safe and feasible for the diagnosis of coronary artery disease (CAD) in humans. However, the assessment of cine scans relies on the visual interpretation of regional wall motion, which is subjective. Recently, Strain-Encoded MRI (SENC) has been proposed for the direct color-coded visualization of myocardial strain. The purpose of our study was to compare the diagnostic value of SENC to that provided by conventional wall motion analysis for the detection of inducible ischemia during DS-MRI. Methods and Results Stress induced ischemia was assessed by wall motion analysis and by SENC in 101 patients with suspected or known CAD and in 17 healthy volunteers who underwent DS-MRI in a clinical 1.5T scanner. Quantitative coronary angiography deemed as the standard reference for the presence or absence of significant CAD (≥50% diameter stenosis). On a coronary vessel level, SENC detected inducible ischemia in 86/101 versus 71/101 diseased coronary vessels (p<0.01 versus cine), and showed normal strain response in 189/202 versus 194/202 vessels with <50% stenosis (p=NS versus cine). On a patient level, SENC detected inducible ischemia in 63/64 versus 55/64 patients with CAD (p<0.05 versus cine), and showed normal strain response in 32/37 versus 34/37 patients without CAD (p=NS versus cine).Quantification analysis demonstrated a significant correlation between strain rate reserve (SRreserve) and coronary artery stenosis severity (r²=0.56, p<0.001), and a cut-off value of SRreserve=1.64 deemed as a highly accurate marker for the detection of stenosis≥50% (AUC=0.96, SE=0.01, 95% CI = 0.94–0.98, p<0.001). Conclusions The direct color-coded visualization of strain on MR-images is a useful adjunct for DS-MRI, which provides incremental value for the detection of CAD compared to conventional wall motion readings on cine images. PMID:19808579

Strain-encoded cardiac MRI as an adjunct for dobutamine stress testing: incremental value to conventional wall motion analysis.

PubMed

Korosoglou, Grigorios; Lossnitzer, Dirk; Schellberg, Dieter; Lewien, Antje; Wochele, Angela; Schaeufele, Tim; Neizel, Mirja; Steen, Henning; Giannitsis, Evangelos; Katus, Hugo A; Osman, Nael F

2009-03-01

High-dose dobutamine stress MRI is safe and feasible for the diagnosis of coronary artery disease (CAD) in humans. However, the assessment of cine scans relies on the visual interpretation of regional wall motion, which is subjective. Recently, strain-encoded MRI (SENC) has been proposed for the direct color-coded visualization of myocardial strain. The purpose of our study was to compare the diagnostic value of SENC with that provided by conventional wall motion analysis for the detection of inducible ischemia during dobutamine stress MRI. Stress-induced ischemia was assessed by wall motion analysis and by SENC in 101 patients with suspected or known CAD and in 17 healthy volunteers who underwent dobutamine stress MRI in a clinical 1.5-T scanner. Quantitative coronary angiography deemed as the standard reference for the presence or absence of significant CAD (> or =50% diameter stenosis). On a coronary vessel level, SENC detected inducible ischemia in 86 of 101 versus 71 of 101 diseased coronary vessels (P<0.01 versus cine) and showed normal strain response in 189 of 202 versus 194 of 202 vessels with <50% stenosis (P=NS versus cine). On a patient level, SENC detected inducible ischemia in 63 of 64 versus 55 of 64 patients with CAD (P<0.05 versus cine) and showed normal strain response in 32 of 37 versus 34 of 37 patients without CAD (P=NS versus cine). Quantification analysis demonstrated a significant correlation between strain rate reserve and coronary artery stenosis severity (r(2)=0.56, P<0.001), and a cutoff value of strain rate reserve of 1.64 was deemed as a highly accurate marker for the detection of > or =50% stenosis (area under the curve, 0.96; SE, 0.01; 95% CI, 0.94 to 0.98; P<0.001). The direct color-coded visualization of strain on MR images is a useful adjunct for dobutamine stress MRI, which provides incremental value for the detection of CAD compared with conventional wall motion readings on cine images.

Comparison of exercise electrocardiography and stress perfusion CMR for the detection of coronary artery disease in women

PubMed Central

2012-01-01

Background Exercise electrocardiography (ECG) is frequently used in the work-up of patients with suspected coronary artery disease (CAD), however the accuracy is reduced in women. Cardiovascular magnetic resonance (CMR) stress testing can accurately diagnose CAD in women. To date, a direct comparison of CMR to ECG has not been performed. Methods and results We prospectively enrolled 88 consecutive women with chest pain or other symptoms suggestive of CAD. Patients underwent a comprehensive clinical evaluation, exercise ECG, a CMR stress test including perfusion and infarct imaging, and x-ray coronary angiography (CA) within 24 hours. CAD was defined as stenosis ≥70% on quantitative analysis of CA. Exercise ECG, CMR and CA was completed in 68 females (age 66.4 ± 8.8 years, number of CAD risk factors 3.5 ± 1.4). The prevalence of CAD on CA was 29%. The Duke treadmill score (DTS) in the entire group was −3.0 ± 5.4 and was similar in those with and without CAD (−4.5 ± 5.8 and −2.4 ± 5.1; P = 0.12). Sensitivity, specificity and accuracy for CAD diagnosis was higher for CMR compared with exercise ECG (sensitivities 85% and 50%, P = 0.02, specificities 94% and 73%, P = 0.01, and accuracies 91% and 66%, P = 0.0007, respectively). Even after applying the DTS the accuracy of CMR was higher compared to exercise ECG (area under ROC curve 0.94 ± 0.03 vs 0.56 ± 0.07; P = 0.0001). Conclusions In women with intermediate-to-high risk for CAD who are able to exercise and have interpretable resting ECG, CMR stress perfusion imaging has higher accuracy for the detection of relevant obstruction of the epicardial coronaries when directly compared to exercise ECG. PMID:22697372

A new semiquantitative method for evaluation of metastasis progression.

PubMed

Volarevic, A; Ljujic, B; Volarevic, V; Milovanovic, M; Kanjevac, T; Lukic, A; Arsenijevic, N

2012-01-01

Although recent technical advancements are directed toward developing novel assays and methods for detection of micro and macro metastasis, there are still no reports of reliable, simple to use imaging software that could be used for the detection and quantification of metastasis in tissue sections. We herein report a new semiquantitative method for evaluation of metastasis progression in a well established 4T1 orthotopic mouse model of breast cancer metastasis. The new semiquantitative method presented here was implemented by using the Autodesk AutoCAD 2012 program, a computer-aided design program used primarily for preparing technical drawings in 2 dimensions. By using the Autodesk AutoCAD 2012 software- aided graphical evaluation we managed to detect each metastatic lesion and we precisely calculated the average percentage of lung and liver tissue parenchyma with metastasis in 4T1 tumor-bearing mice. The data were highly specific and relevant to descriptive histological analysis, confirming reliability and accuracy of the AutoCAD 2012 software as new method for quantification of metastatic lesions. The new semiquantitative method using AutoCAD 2012 software provides a novel approach for the estimation of metastatic progression in histological tissue sections.

Automatic rectum limit detection by anatomical markers correlation.

PubMed

Namías, R; D'Amato, J P; del Fresno, M; Vénere, M

2014-06-01

Several diseases take place at the end of the digestive system. Many of them can be diagnosed by means of different medical imaging modalities together with computer aided detection (CAD) systems. These CAD systems mainly focus on the complete segmentation of the digestive tube. However, the detection of limits between different sections could provide important information to these systems. In this paper we present an automatic method for detecting the rectum and sigmoid colon limit using a novel global curvature analysis over the centerline of the segmented digestive tube in different imaging modalities. The results are compared with the gold standard rectum upper limit through a validation scheme comprising two different anatomical markers: the third sacral vertebra and the average rectum length. Experimental results in both magnetic resonance imaging (MRI) and computed tomography colonography (CTC) acquisitions show the efficacy of the proposed strategy in automatic detection of rectum limits. The method is intended for application to the rectum segmentation in MRI for geometrical modeling and as contextual information source in virtual colonoscopies and CAD systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging.

PubMed

Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Theotokas, Ioannis; Zoumpoulis, Pavlos; Loupas, Thanasis; Hazle, John D; Kagadis, George C

2016-03-01

Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval. The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.

The genetic basis for survivorship in coronary artery disease

PubMed Central

Dungan, Jennifer R.; Hauser, Elizabeth R.; Qin, Xuejun; Kraus, William E.

2013-01-01

Survivorship is a trait characterized by endurance and virility in the face of hardship. It is largely considered a psychosocial attribute developed during fatal conditions, rather than a biological trait for robustness in the context of complex, age-dependent diseases like coronary artery disease (CAD). The purpose of this paper is to present the novel phenotype, survivorship in CAD as an observed survival advantage concurrent with clinically significant CAD. We present a model for characterizing survivorship in CAD and its relationships with overlapping time- and clinically-related phenotypes. We offer an optimal measurement interval for investigating survivorship in CAD. We hypothesize genetic contributions to this construct and review the literature for evidence of genetic contribution to overlapping phenotypes in support of our hypothesis. We also present preliminary evidence of genetic effects on survival in people with clinically significant CAD from a primary case-control study of symptomatic coronary disease. Identifying gene variants that confer improved survival in the context of clinically appreciable CAD may improve our understanding of cardioprotective mechanisms acting at the gene level and potentially impact patients clinically in the future. Further, characterizing other survival-variant genetic effects may improve signal-to-noise ratio in detecting gene associations for CAD. PMID:24143143

A new screening pathway for identifying asymptomatic patients using dental panoramic radiographs

NASA Astrophysics Data System (ADS)

Hayashi, Tatsuro; Matsumoto, Takuya; Sawagashira, Tsuyoshi; Tagami, Motoki; Katsumata, Akitoshi; Hayashi, Yoshinori; Muramatsu, Chisako; Zhou, Xiangrong; Iida, Yukihiro; Matsuoka, Masato; Katagi, Kiyoji; Fujita, Hiroshi

2012-03-01

To identify asymptomatic patients is the challenging task and the essential first step in diagnosis. Findings of dental panoramic radiographs include not only dental conditions but also radiographic signs that are suggestive of possible systemic diseases such as osteoporosis, arteriosclerosis, and maxillary sinusitis. Detection of such signs on panoramic radiographs has a potential to provide supplemental benefits for patients. However, it is not easy for general dental practitioners to pay careful attention to such signs. We addressed the development of a computer-aided detection (CAD) system that detects radiographic signs of pathology on panoramic images, and the design of the framework of new screening pathway by cooperation of dentists and our CAD system. The performance evaluation of our CAD system showed the sensitivity and specificity in the identification of osteoporotic patients were 92.6 % and 100 %, respectively, and those of the maxillary sinus abnormality were 89.6 % and 73.6 %, respectively. The detection rate of carotid artery calcifications that suggests the need for further medical evaluation was approximately 93.6 % with 4.4 false-positives per image. To validate the utility of the new screening pathway, preliminary clinical trials by using our CAD system were conducted. To date, 223 panoramic images were processed and 4 asymptomatic patients with suspected osteoporosis, 7 asymptomatic patients with suspected calcifications, and 40 asymptomatic patients with suspected maxillary sinusitis were detected in our initial trial. It was suggested that our new screening pathway could be useful to identify asymptomatic patients with systemic diseases.

Value of coronary computed tomography as a prognostic tool.

PubMed

Contractor, Tahmeed; Parekh, Maansi; Ahmed, Shameer; Martinez, Matthew W

2012-08-01

Coronary computed tomography angiography (CCTA) has become an important part of our armamentarium for noninvasive diagnosis of coronary artery disease (CAD). Emerging technologies have produced lower radiation dose, improved spatial and temporal resolution, as well as information about coronary physiology. Although the prognostic role of coronary artery calcium scoring is known, similar evidence for CCTA has only recently emerged. Initial, small studies in various patient populations have indicated that CCTA-identified CAD may have a prognostic value. These findings were confirmed in a recent analysis of the international, prospective Coronary CT Angiography Evaluation For Clinical Outcomes: An International Multicenter (CONFIRM) registry. An incremental increase in mortality was found with a worse severity of CAD on a per-patient, per-vessel, and per-segment basis. In addition, age-, sex-, and ethnicity-based differences in mortality were also found. Whether changing our management algorithms based on these findings will affect outcomes is unclear. Large prospective studies utilizing targeted management strategies for obstructive and nonobstructive CAD are required to incorporate these recent findings into our daily practice. © 2012 Wiley Periodicals, Inc.

The frequency of early colorectal cancer derived from sessile serrated adenoma/polyps among 1858 serrated polyps from a single institution.

PubMed

Chino, A; Yamamoto, N; Kato, Y; Morishige, K; Ishikawa, H; Kishihara, T; Fujisaki, J; Ishikawa, Y; Tamegai, Y; Igarashi, M

2016-02-01

Sessile serrated adenoma/polyps (SSAPs) are suspected to have a high malignant potential, although few reports have evaluated the incidence of carcinomas derived from SSAPs using the new classification for serrated polyps (SPs). The aim of study was to compare the frequency of cancer coexisting with the various SP subtypes including mixed polyps (MIXs) and conventional adenomas (CADs). A total of 18,667 CADs were identified between April 2005 and December 2011, and 1858 SPs (re-classified as SSAP, hyperplastic polyp (HP), traditional serrated adenoma (TSA), or MIX) were removed via snare polypectomy, endoscopic mucosal resection, or endoscopic sub-mucosal dissection. Among 1160 HP lesions, 1 (0.1%) coexisting sub-mucosal invasive carcinoma (T1) was detected. Among 430 SSAP lesions, 3 (0.7%) high-grade dysplasia (HGD/Tis) and 1 (0.2%) T1 were detected. All of the lesions were detected in the proximal colon, with a mean tumor diameter of 18 mm (SD 9 mm). Among 212 TSA lesions, 3 (1%) HGD/Tis were detected but no T1 cancer. Among 56 MIX lesions, 9 (16%) HGD/Tis and 1 (2%) T1 cancers were detected, and among 18,677 CAD lesions, 964 (5%) HGD/Tis and 166 (1%) T1 cancers were identified. Among the resected lesions that were detected during endoscopic examination, a smaller proportion (1%) of SSAPs harbored HGD or coexisting cancer, compared to CAD or MIX lesions. Therefore, more attention should be paid to accurately identifying lesions endoscopically for intentional resection and the surveillance of each SP subtype.

An automated approach to improve efficacy in detecting residual malignant cancer cell for facilitating prognostic assessment of leukemia: an initial study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Lu, Xianglan; Tan, Maxine; Li, Shibo; Liu, Hong; Zheng, Bin

2015-03-01

The purpose of this study is to investigate the feasibility of applying automatic interphase FISH cells analysis method for detecting the residual malignancy of post chemotherapy leukemia patients. In the experiment, two clinical specimens with translocation between chromosome No. 9 and 22 or No. 11 and 14 were selected from the patients underwent leukemia diagnosis and treatment. The entire slide of each specimen was first digitalized by a commercial fluorescent microscope using a 40× objective lens. Then, the scanned images were processed by a computer-aided detecting (CAD) scheme to identify the analyzable FISH cells, which is accomplished by applying a series of features including the region size, Brenner gradient and maximum intensity. For each identified cell, the scheme detected and counted the number of the FISH signal dots inside the nucleus, using the adaptive threshold of the region size and distance of the labeled FISH dots. The results showed that the new CAD scheme detected 8093 and 6675 suspicious regions of interest (ROI) in two specimens, among which 4546 and 3807 ROI contain analyzable interphase FISH cell. In these analyzable ROIs, CAD selected 334 and 405 residual malignant cancer cells, which is substantially more than those visually detected in a cytogenetic laboratory of our medical center (334 vs. 122, 405 vs. 160). This investigation indicates that an automatic interphase FISH cell scanning and CAD method has the potential to improve the accuracy and efficiency of the prognostic assessment for leukemia and other genetic related cancer patients in the future.

The effects of slice thickness and radiation dose level variations on computer-aided diagnosis (CAD) nodule detection performance in pediatric chest CT scans

NASA Astrophysics Data System (ADS)

Emaminejad, Nastaran; Lo, Pechin; Ghahremani, Shahnaz; Kim, Grace H.; Brown, Matthew S.; McNitt-Gray, Michael F.

2017-03-01

For pediatric oncology patients, CT scans are performed to assess treatment response and disease progression. CAD may be used to detect lung nodules which would reflect metastatic disease. The purpose of this study was to investigate the effects of reducing radiation dose and varying slice thickness on CAD performance in the detection of solid lung nodules in pediatric patients. The dataset consisted of CT scans of 58 pediatric chest cases, from which 7 cases had lung nodules detected by radiologist, and a total of 28 nodules were marked. For each case, the original raw data (sinogram data) was collected and a noise addition model was used to simulate reduced-dose scans of 50%, 25% and 10% of the original dose. In addition, the original and reduced-dose raw data were reconstructed at slice thicknesses of 1.5 and 3 mm using a medium sharp (B45) kernel; the result was eight datasets (4 dose levels x 2 thicknesses) for each case An in-house CAD tool was applied on all reconstructed scans, and results were compared with the radiologist's markings. Patient level mean sensitivities at 3mm thickness were 24%, 26%, 25%, 27%, and at 1.5 mm thickness were 23%, 29%, 35%, 36% for 10%, 25%, 50%, and 100% dose level, respectively. Mean FP numbers were 1.5, 0.9, 0.8, 0.7 at 3 mm and 11.4, 3.5, 2.8, 2.8 at 1.5 mm thickness for 10%, 25%, 50%, and 100% dose level respectively. CAD sensitivity did not change with dose level for 3mm thickness, but did change with dose for 1.5 mm. False Positives increased at low dose levels where noise values were high.

Analyzing ROC curves using the effective set-size model

NASA Astrophysics Data System (ADS)

Samuelson, Frank W.; Abbey, Craig K.; He, Xin

2018-03-01

The Effective Set-Size model has been used to describe uncertainty in various signal detection experiments. The model regards images as if they were an effective number (M*) of searchable locations, where the observer treats each location as a location-known-exactly detection task with signals having average detectability d'. The model assumes a rational observer behaves as if he searches an effective number of independent locations and follows signal detection theory at each location. Thus the location-known-exactly detectability (d') and the effective number of independent locations M* fully characterize search performance. In this model the image rating in a single-response task is assumed to be the maximum response that the observer would assign to these many locations. The model has been used by a number of other researchers, and is well corroborated. We examine this model as a way of differentiating imaging tasks that radiologists perform. Tasks involving more searching or location uncertainty may have higher estimated M* values. In this work we applied the Effective Set-Size model to a number of medical imaging data sets. The data sets include radiologists reading screening and diagnostic mammography with and without computer-aided diagnosis (CAD), and breast tomosynthesis. We developed an algorithm to fit the model parameters using two-sample maximum-likelihood ordinal regression, similar to the classic bi-normal model. The resulting model ROC curves are rational and fit the observed data well. We find that the distributions of M* and d' differ significantly among these data sets, and differ between pairs of imaging systems within studies. For example, on average tomosynthesis increased readers' d' values, while CAD reduced the M* parameters. We demonstrate that the model parameters M* and d' are correlated. We conclude that the Effective Set-Size model may be a useful way of differentiating location uncertainty from the diagnostic uncertainty in medical imaging tasks.

Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Mencattini, Arianna; Casti, Paola; Martinelli, Eugenio; di Natale, Corrado; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

2018-02-01

This paper proposes a method to reduce the number of false-positives (FP) in a computer-aided detection (CAD) scheme for automated detection of architectural distortion (AD) in digital mammography. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automated detection of AD in breast images. The usual approach is automatically detect possible sites of AD in a mammographic image (segmentation step) and then use a classifier to eliminate the false-positives and identify the suspicious regions (classification step). This paper focus on the optimization of the segmentation step to reduce the number of FPs that is used as input to the classifier. The proposal is to use statistical measurements to score the segmented regions and then apply a threshold to select a small quantity of regions that should be submitted to the classification step, improving the detection performance of a CAD scheme. We evaluated 12 image features to score and select suspicious regions of 74 clinical Full-Field Digital Mammography (FFDM). All images in this dataset contained at least one region with AD previously marked by an expert radiologist. The results showed that the proposed method can reduce the false positives of the segmentation step of the CAD scheme from 43.4 false positives (FP) per image to 34.5 FP per image, without increasing the number of false negatives.

3D Silicon Coincidence Avalanche Detector (3D-SiCAD) for charged particle detection

NASA Astrophysics Data System (ADS)

Vignetti, M. M.; Calmon, F.; Pittet, P.; Pares, G.; Cellier, R.; Quiquerez, L.; Chaves de Albuquerque, T.; Bechetoille, E.; Testa, E.; Lopez, J.-P.; Dauvergne, D.; Savoy-Navarro, A.

2018-02-01

Single-Photon Avalanche Diodes (SPADs) are p-n junctions operated in Geiger Mode by applying a reverse bias above the breakdown voltage. SPADs have the advantage of featuring single photon sensitivity with timing resolution in the picoseconds range. Nevertheless, their relatively high Dark Count Rate (DCR) is a major issue for charged particle detection, especially when it is much higher than the incoming particle rate. To tackle this issue, we have developed a 3D Silicon Coincidence Avalanche Detector (3D-SiCAD). This novel device implements two vertically aligned SPADs featuring on-chip electronics for the detection of coincident avalanche events occurring on both SPADs. Such a coincidence detection mode allows an efficient discrimination of events related to an incoming charged particle (producing a quasi-simultaneous activation of both SPADs) from dark counts occurring independently on each SPAD. A 3D-SiCAD detector prototype has been fabricated in CMOS technology adopting a 3D flip-chip integration technique, and the main results of its characterization are reported in this work. The particle detection efficiency and noise rejection capability for this novel device have been evaluated by means of a β- strontium-90 radioactive source. Moreover the impact of the main operating parameters (i.e. the hold-off time, the coincidence window duration, the SPAD excess bias voltage) over the particle detection efficiency has been studied. Measurements have been performed with different β- particles rates and show that a 3D-SiCAD device outperforms single SPAD detectors: the former is indeed capable to detect particle rates much lower than the individual DCR observed in a single SPAD-based detectors (i.e. 2 to 3 orders of magnitudes lower).

Computer aided detection of clusters of microcalcifications on full field digital mammograms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ge Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.

2006-08-15

We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual microcalcifications; regional clustering; and FP reduction for clustered microcalcifications. At the stage of FP reduction for individual microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered microcalcifications, morphological features and features derived from themore » artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was used to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.« less

Computer-aided detection of acute pulmonary embolism with 64-slice multi-detector row computed tomography: impact of the scanning conditions and overall image quality in the detection of peripheral clots.

PubMed

Dewailly, Marion; Rémy-Jardin, Martine; Duhamel, Alain; Faivre, Jean-Baptiste; Pontana, François; Deken, Valérie; Bakai, Anne-Marie; Remy, Jacques

2010-01-01

To evaluate the performance of a computer-aided detection (CAD) system for diagnosing peripheral acute pulmonary embolism (PE) with a 64-slice multi-detector row computed tomography (CT). Two radiologists investigated the accuracy of a software aimed at detecting peripheral clots (PECAD prototype, version 7; Siemens Medical Systems, Forchheim, Germany) by applying this tool for the analysis of the pulmonary arterial bed of 74 CT angiograms obtained with 64-slice dual-source CT (Definition; Siemens Medical Systems). These cases were retrospectively selected from a database of CT studies performed on the same CT unit, with a similar collimation (64 x 0.6 mm) and similar injection protocols. Patient selection was based on a variety of (1) scanning conditions, namely, nongated (n = 30), electrocardiography-gated (n = 30), and dual-energy CT angiograms (n = 14), and (2) image quality (IQ), namely, scans of excellent IQ (n = 53) and lower IQ due to lower levels of arterial enhancement and/or presence of noise (n = 21). The standard of truth was based on the 2 radiologists' consensus reading and the results of CAD. The software detected 80 of 93 peripheral clots present in the 21 patients (42 segmental and 38 subsegmental clots). The overall sensitivity (95% confidence interval) of the CAD tool was 86% (77%-92%) for detecting peripheral clots, 78% (64.5%-88%) at the segmental level and 97% (85.5%-99.9%) at the subsegmental level. Assuming normal vascular anatomy with 20 segmental and 40 subsegmental arteries, overall specificity and positive and negative predictive values (95% confidence interval) of the software were 91.8% (91%-92.6%), 18.4% (15%-22.4%), and 99.7% (99.5%-99.8%), respectively. A mean of 5.4 false positives was found per patient (total, 354 false positives), mainly linked to the presence of perivascular connective tissue (n = 119; 34%) and perivascular airspace consolidation (n = 97; 27%). The sensitivities (95% confidence interval) for the CAD tool were 91% (69.8%-99.3%) for dual-energy, 87% (59.3%-93.2%) for electrocardiography-gated, and 87% (73.5%-95.3%) for nongated scans (P > 0.05). No significant difference was found in the sensitivity of the CAD software when comparing the scans according to the scanning conditions and image quality. The evaluated CAD software has a good sensitivity in detecting peripheral PE, which is not influenced by the scanning conditions or the overall image quality.

Integrated geometry and grid generation system for complex configurations

NASA Technical Reports Server (NTRS)

Akdag, Vedat; Wulf, Armin

1992-01-01

A grid generation system was developed that enables grid generation for complex configurations. The system called ICEM/CFD is described and its role in computational fluid dynamics (CFD) applications is presented. The capabilities of the system include full computer aided design (CAD), grid generation on the actual CAD geometry definition using robust surface projection algorithms, interfacing easily with known CAD packages through common file formats for geometry transfer, grid quality evaluation of the volume grid, coupling boundary condition set-up for block faces with grid topology generation, multi-block grid generation with or without point continuity and block to block interface requirement, and generating grid files directly compatible with known flow solvers. The interactive and integrated approach to the problem of computational grid generation not only substantially reduces manpower time but also increases the flexibility of later grid modifications and enhancements which is required in an environment where CFD is integrated into a product design cycle.

Gadolinium Enhanced MR Coronary Vessel Wall Imaging at 3.0 Tesla.

PubMed

Kelle, Sebastian; Schlendorf, Kelly; Hirsch, Glenn A; Gerstenblith, Gary; Fleck, Eckart; Weiss, Robert G; Stuber, Matthias

2010-10-11

Purpose. We evaluated the influence of the time between low-dose gadolinium (Gd) contrast administration and coronary vessel wall enhancement (LGE) detected by 3T magnetic resonance imaging (MRI) in healthy subjects and patients with coronary artery disease (CAD). Materials and Methods. Four healthy subjects (4 men, mean age 29 ± 3 years and eleven CAD patients (6 women, mean age 61 ± 10 years) were studied on a commercial 3.0 Tesla (T) whole-body MR imaging system (Achieva 3.0 T; Philips, Best, The Netherlands). T1-weighted inversion-recovery coronary magnetic resonance imaging (MRI) was repeated up to 75 minutes after administration of low-dose Gadolinium (Gd) (0.1 mmol/kg Gd-DTPA). Results. LGE was seen in none of the healthy subjects, however in all of the CAD patients. In CAD patients, fifty-six of 62 (90.3%) segments showed LGE of the coronary artery vessel wall at time-interval 1 after contrast. At time-interval 2, 34 of 42 (81.0%) and at time-interval 3, 29 of 39 evaluable segments (74.4%) were enhanced. Conclusion. In this work, we demonstrate LGE of the coronary artery vessel wall using 3.0 T MRI after a single, low-dose Gd contrast injection in CAD patients but not in healthy subjects. In the majority of the evaluated coronary segments in CAD patients, LGE of the coronary vessel wall was already detectable 30-45 minutes after administration of the contrast agent.

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

PubMed Central

Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Wei, Jun; Cha, Kenny

2016-01-01

Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. PMID:27908154

Role of nuclear cardiology in evaluating the total ischemic burden in coronary artery disease

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beller, G.A.

1987-03-09

Goals of exercise radionuclide imaging are to: enhance sensitivity, specificity and predictive value of coronary artery disease (CAD) detection; noninvasively assess extent and severity of functionally significant CAD; determine prognosis so that specific therapeutic strategies can be more rationally implemented; detect silent ischemia in asymptomatic subjects or in patients with known CAD with a higher degree of specificity than can be accomplished by electrocardiogram stress testing alone; evaluate the response to therapeutic interventions aimed at enhancing coronary blood flow. Two major radionuclide techniques are currently used in evaluating the total ischemic burden in patients with CAD. These are myocardial perfusionmore » imaging with either thallium-201 or rubidium-82, and radionuclide angiography performed after administration of technetium-99m. Areas of diminished thallium-201 activity on early postexercise images are abnormal and represent either areas of stress-induced ischemia or myocardial scar. To differentiate between the two, delayed images are obtained to determine if the initial postexercise defect either persists or demonstrates redistribution. Defects demonstrating redistribution represent transient ischemia, whereas areas of previous infarction or scar usually appear as persistent defects. Patients with left main or 3-vessel CAD usually show multiple thallium-201 redistribution defects in more than 1 vascular supply region, a phenomenon often associated with abnormal lung thallium-201 uptake.« less

Coronary Artery Disease and Reticular Macular Disease, a Subphenotype of Early Age-Related Macular Degeneration.

PubMed

Cymerman, Rachel M; Skolnick, Adam H; Cole, William J; Nabati, Camellia; Curcio, Christine A; Smith, R Theodore

2016-11-01

Reticular macular disease (RMD) is the highest risk form of early age-related macular degeneration and also specifically confers decreased longevity. However, because RMD requires advanced retinal imaging for adequate detection of its characteristic subretinal drusenoid deposits (SDD), it has not yet been completely studied with respect to coronary artery disease (CAD), the leading cause of death in the developed world. Because CAD appears in middle age, our purpose was to screen patients aged 45-80 years, documented either with or without CAD, to determine if CAD is associated with RMD. A prospective cohort study of patients with documented CAD status and no known retinal disease in a clinical practice setting at one institution. Subjects and Controls: A number of 76 eyes from 38 consecutive patients (23 with documented CAD, 15 controls documented without CAD; 47.4% female; mean age 66.7 years). Patients were imaged with near-infrared reflectance/spectral domain optical coherence tomography and assessed in masked fashion by two graders for the presence of SDD lesions of RMD and soft drusen. Presence or absence of RMD/SDD and soft drusen. RMD was more frequent in patients with CAD versus those without (Relative Risk [RR] = 2.1, CI = 1.08-3.95, P = 0.03). There was no association of CAD with soft drusen. A specific relationship between CAD and RMD suggests common systemic causes for both and warrants further study.

Aeroelastic Deflection of NURBS Geometry

NASA Technical Reports Server (NTRS)

Samareh, Jamshid A.

1998-01-01

The purpose of this paper is to present an algorithm for using NonUniform Rational B-Spline (NURBS) representation in an aeroelastic loop. The algorithm is based on creating a least-squares NURBS surface representing the aeroelastic defection. The resulting NURBS surfaces are used to update either the original Computer- Aided Design (CAD) model, Computational Structural Mechanics (CSM) grid or the Computational Fluid Dynamics (CFD) grid. Results are presented for a generic High-Speed Civil Transport (HSCT).

Exercise Electrocardiogram Neither Predicts Nor Excludes Coronary Artery Disease in Women with Low to Intermediate Risk.

PubMed

Knol, Remco J J; Kan, Huub; Wondergem, Maurits; Cornel, Jan H; Umans, Victor A W M; van der Ploeg, Tjeerd; van der Zant, Friso M

2018-04-01

The value of exercise electrocardiogram (ExECG) in symptomatic female patients with low to intermediate risk for significant coronary artery disease (CAD) has been under debate for many years, and nondiagnostic or even erroneous test results are frequently encountered. Cardiac-CT may be more appropriate to exclude CAD in women. This study compares the results of ExECGs with those of cardiac-CTs, performed within a time frame of 1 month in an all-comers female chest pain population. Five hundred fifty-one consecutive female patients from a patient registry were included. ExECGs were negative in 324 (59%), positive in 14 (3%), and nondiagnostic in 213 (39%) patients. CAD was revealed by cardiac-CT in 57% of the women with negative ExECG. No signs of CAD were present on cardiac-CT in 64% of the women with a positive ExECG. Cardiac-CT showed presence of CAD in 268/551 (49%) patients, of whom 56/268 (21%) was diagnosed with ≥50% stenosis. The ExECG of the latter group was negative in 26 (46%), inconclusive in 29 (52%), and positive in 1 (2%). Considering ≥50% stenosis at cardiac-CT as the reference, sensitivity, specificity, PPV, and NPV of ExECG for the present population were 3.7%, 95.7%, 7.1%, and 91.7%, respectively. Similar diagnostic performance was calculated when considering ≥70% stenosis at cardiac-CT as the reference. ExECG failed to detect CAD in more than half of this cohort and in almost half of women with >50% stenosis at cardiac-CT. Importantly, no CAD was detected by cardiac-CT in 64% of women with a positive ExECG. ExECG is therefore questionable as a diagnostic strategy in women with low-to-intermediate risk of CAD, although prospective studies are warranted to determine whether replacing ExECG by cardiac-CT provides better prognoses.

Algorithms to eliminate the influence of non-uniform intensity distributions on wavefront reconstruction by quadri-wave lateral shearing interferometers

NASA Astrophysics Data System (ADS)

Chen, Xiao-jun; Dong, Li-zhi; Wang, Shuai; Yang, Ping; Xu, Bing

2017-11-01

In quadri-wave lateral shearing interferometry (QWLSI), when the intensity distribution of the incident light wave is non-uniform, part of the information of the intensity distribution will couple with the wavefront derivatives to cause wavefront reconstruction errors. In this paper, we propose two algorithms to reduce the influence of a non-uniform intensity distribution on wavefront reconstruction. Our simulation results demonstrate that the reconstructed amplitude distribution (RAD) algorithm can effectively reduce the influence of the intensity distribution on the wavefront reconstruction and that the collected amplitude distribution (CAD) algorithm can almost eliminate it.

Excess coronary artery disease risk in South Asian immigrants: Can dysfunctional high-density lipoprotein explain increased risk?

PubMed Central

Dodani, Sunita

2008-01-01

Background: Coronary artery disease (CAD) is the leading cause of mortality and morbidity in the United States (US), and South Asian immigrants (SAIs) have a higher risk of CAD compared to Caucasians. Traditional risk factors may not completely explain high risk, and some of the unknown risk factors need to be explored. This short review is mainly focused on the possible role of dysfunctional high-density lipoprotein (HDL) in causing CAD and presents an overview of available literature on dysfunctional HDL. Discussion: The conventional risk factors, insulin resistance parameters, and metabolic syndrome, although important in predicting CAD risk, may not sufficiently predict risk in SAIs. HDL has antioxidant, antiinflammatory, and antithrombotic properties that contribute to its function as an antiatherogenic agent. Recent Caucasian studies have shown HDL is not only ineffective as an antioxidant but, paradoxically, appears to be prooxidant, and has been found to be associated with CAD. Several causes have been hypothesized for HDL to become dysfunctional, including Apo lipoprotein A-I (Apo A-I) polymorphisms. New risk factors and markers like dysfunctional HDL and genetic polymorphisms may be associated with CAD. Conclusions: More research is required in SAIs to explore associations with CAD and to enhance early detection and prevention of CAD in this high risk group. PMID:19183743

A ROC-based feature selection method for computer-aided detection and diagnosis

NASA Astrophysics Data System (ADS)

Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

2014-03-01

Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

Quantitative imaging biomarkers for dural sinus patterns in idiopathic intracranial hypertension.

PubMed

Zur, Dinah; Anconina, Reut; Kesler, Anat; Lublinsky, Svetlana; Toledano, Ronen; Shelef, Ilan

2017-02-01

To quantitatively characterize transverse dural sinuses (TS) on magnetic resonance venography (MRV) in patients with idiopathic intracranial hypertension (IIH), compared to healthy controls, using a computer assisted detection (CAD) method. We retrospectively analyzed MRV studies of 38 IIH patients and 30 controls, matched by age and gender. Data analysis was performed using a specially developed Matlab algorithm for vessel cross-sectional analysis. The cross-sectional area and shape measurements were evaluated in patients and controls. Mean, minimal, and maximal cross-sectional areas as well as volumetric parameters of the right and left transverse sinuses were significantly smaller in IIH patients than in controls ( p  < .005 for all). Idiopathic intracranial hypertension patients showed a narrowed segment in both TS, clustering near the junction with the sigmoid sinus. In 36% (right TS) and 43% (left TS), the stenosis extended to >50% of the entire length of the TS, i.e. the TS was hypoplastic. Narrower vessels tended to have a more triangular shape than did wider vessels. Using CAD we precisely quantified TS stenosis and its severity in IIH patients by cross-sectional and volumetric analysis. This method can be used as an exact tool for investigating mechanisms of IIH development and response to treatment.

Optic disk localization by a robust fusion method

NASA Astrophysics Data System (ADS)

Zhang, Jielin; Yin, Fengshou; Wong, Damon W. K.; Liu, Jiang; Baskaran, Mani; Cheng, Ching-Yu; Wong, Tien Yin

2013-02-01

The optic disk localization plays an important role in developing computer-aided diagnosis (CAD) systems for ocular diseases such as glaucoma, diabetic retinopathy and age-related macula degeneration. In this paper, we propose an intelligent fusion of methods for the localization of the optic disk in retinal fundus images. Three different approaches are developed to detect the location of the optic disk separately. The first method is the maximum vessel crossing method, which finds the region with the most number of blood vessel crossing points. The second one is the multichannel thresholding method, targeting the area with the highest intensity. The final method searches the vertical and horizontal region-of-interest separately on the basis of blood vessel structure and neighborhood entropy profile. Finally, these three methods are combined using an intelligent fusion method to improve the overall accuracy. The proposed algorithm was tested on the STARE database and the ORIGAlight database, each consisting of images with various pathologies. The preliminary result on the STARE database can achieve 81.5%, while a higher result of 99% can be obtained for the ORIGAlight database. The proposed method outperforms each individual approach and state-of-the-art method which utilizes an intensity-based approach. The result demonstrates a high potential for this method to be used in retinal CAD systems.

Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk

NASA Astrophysics Data System (ADS)

Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Patel, Bhavika; Heidari, Morteza; Liu, Hong; Zheng, Bin

2018-05-01

This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. In the next subsequent mammography screening, 402 women were diagnosed with breast cancer and 642 remained negative. An existing CAD scheme was applied ‘as is’ to process each image. From CAD-generated results, four detection features including the total number of (1) initial detection seeds and (2) the final detected false-positive regions, (3) average and (4) sum of detection scores, were computed from each image. Then, by combining the features computed from two bilateral images of left and right breasts from either craniocaudal or mediolateral oblique view, two logistic regression models were trained and tested using a leave-one-case-out cross-validation method to predict the likelihood of each testing case being positive in the next subsequent screening. The new prediction model yielded the maximum prediction accuracy with an area under a ROC curve of AUC  =  0.65  ±  0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of (2.95, 6.83). The results also showed an increasing trend in the adjusted odds ratio and risk prediction scores (p  <  0.01). Thus, this study demonstrated that CAD-generated false-positives might include valuable information, which needs to be further explored for identifying and/or developing more effective imaging markers for predicting short-term breast cancer risk.

Is Contrast Enhanced Ultrasonography a useful tool in a beginner's hand? How much can a Computer Assisted Diagnosis prototype help in characterizing the malignancy of focal liver lesions?

PubMed

Moga, Tudor Voicu; Popescu, Alina; Sporea, Ioan; Danila, Mirela; David, Ciprian; Gui, Vasile; Iacob, Nicoleta; Miclaus, Gratian; Sirli, Roxana

2017-08-23

Contrast enhanced ultrasound (CEUS) improved the characterization of focal liver lesions (FLLs), but is an operatordependent method. The goal of this paper was to test a computer assisted diagnosis (CAD) prototype and to see its benefit in assisting a beginner in the evaluation of FLLs. Our cohort included 97 good quality CEUS videos[34% hepatocellular carcinomas (HCC), 12.3% hypervascular metastases (HiperM), 11.3% hypovascular metastases (HipoM), 24.7% hemangiomas (HMG), 17.5% focal nodular hyperplasia (FNH)] that were used to develop a CAD prototype based on an algorithm that tested a binary decision based classifier. Two young medical doctors (1 year CEUS experience), two experts and the CAD prototype, reevaluated 50 FLLs CEUS videos (diagnosis of benign vs. malignant) first blinded to clinical data, in order to evaluate the diagnostic gap beginner vs. expert. The CAD classifier managed a 75.2% overall (benign vs. malignant) correct classification rate. The overall classification rates for the evaluators, before and after clinical data were: first beginner-78%; 94%; second beginner-82%; 96%; first expert-94%; 100%; second expert-96%; 98%. For both beginners, the malignant vs. benign diagnosis significantly improved after knowing the clinical data (p=0.005; p=0,008). The expert was better than the beginner (p=0.04) and better than the CAD (p=0.001). CAD in addition to the beginner can reach the expert diagnosis. The most frequent lesions misdiagnosed at CEUS were FNH and HCC. The CAD prototype is a good comparing tool for a beginner operator that can be developed to assist the diagnosis. In order to increase the classification rate, the CAD system for FLL in CEUS must integrate the clinical data.

Diagnostic yield and accuracy of coronary CT angiography after abnormal nuclear myocardial perfusion imaging.

PubMed

Meinel, Felix G; Schoepf, U Joseph; Townsend, Jacob C; Flowers, Brian A; Geyer, Lucas L; Ebersberger, Ullrich; Krazinski, Aleksander W; Kunz, Wolfgang G; Thierfelder, Kolja M; Baker, Deborah W; Khan, Ashan M; Fernandes, Valerian L; O'Brien, Terrence X

2018-06-15

We aimed to determine the diagnostic yield and accuracy of coronary CT angiography (CCTA) in patients referred for invasive coronary angiography (ICA) based on clinical concern for coronary artery disease (CAD) and an abnormal nuclear stress myocardial perfusion imaging (MPI) study. We enrolled 100 patients (84 male, mean age 59.6 ± 8.9 years) with an abnormal MPI study and subsequent referral for ICA. Each patient underwent CCTA prior to ICA. We analyzed the prevalence of potentially obstructive CAD (≥50% stenosis) on CCTA and calculated the diagnostic accuracy of ≥50% stenosis on CCTA for the detection of clinically significant CAD on ICA (defined as any ≥70% stenosis or ≥50% left main stenosis). On CCTA, 54 patients had at least one ≥50% stenosis. With ICA, 45 patients demonstrated clinically significant CAD. A positive CCTA had 100% sensitivity and 84% specificity with a 100% negative predictive value and 83% positive predictive value for clinically significant CAD on a per patient basis in MPI positive symptomatic patients. In conclusion, almost half (48%) of patients with suspected CAD and an abnormal MPI study demonstrate no obstructive CAD on CCTA.

Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suzuki, Kenji; Yoshida, Hiroyuki; Naeppi, Janne

2006-10-15

One of the limitations of the current computer-aided detection (CAD) of polyps in CT colonography (CTC) is a relatively large number of false-positive (FP) detections. Rectal tubes (RTs) are one of the typical sources of FPs because a portion of a RT, especially a portion of a bulbous tip, often exhibits a cap-like shape that closely mimics the appearance of a small polyp. Radiologists can easily recognize and dismiss RT-induced FPs; thus, they may lose their confidence in CAD as an effective tool if the CAD scheme generates such ''obvious'' FPs due to RTs consistently. In addition, RT-induced FPs maymore » distract radiologists from less common true positives in the rectum. Therefore, removal RT-induced FPs as well as other types of FPs is desirable while maintaining a high sensitivity in the detection of polyps. We developed a three-dimensional (3D) massive-training artificial neural network (MTANN) for distinction between polyps and RTs in 3D CTC volumetric data. The 3D MTANN is a supervised volume-processing technique which is trained with input CTC volumes and the corresponding ''teaching'' volumes. The teaching volume for a polyp contains a 3D Gaussian distribution, and that for a RT contains zeros for enhancement of polyps and suppression of RTs, respectively. For distinction between polyps and nonpolyps including RTs, a 3D scoring method based on a 3D Gaussian weighting function is applied to the output of the trained 3D MTANN. Our database consisted of CTC examinations of 73 patients, scanned in both supine and prone positions (146 CTC data sets in total), with optical colonoscopy as a reference standard for the presence of polyps. Fifteen patients had 28 polyps, 15 of which were 5-9 mm and 13 were 10-25 mm in size. These CTC cases were subjected to our previously reported CAD scheme that included centerline-based segmentation of the colon, shape-based detection of polyps, and reduction of FPs by use of a Bayesian neural network based on geometric and texture features. Application of this CAD scheme yielded 96.4% (27/28) by-polyp sensitivity with 3.1 (224/73) FPs per patient, among which 20 FPs were caused by RTs. To eliminate the FPs due to RTs and possibly other normal structures, we trained a 3D MTANN with ten representative polyps and ten RTs, and applied the trained 3D MTANN to the above CAD true- and false-positive detections. In the output volumes of the 3D MTANN, polyps were represented by distributions of bright voxels, whereas RTs and other normal structures partly similar to RTs appeared as darker voxels, indicating the ability of the 3D MTANN to suppress RTs as well as other normal structures effectively. Application of the 3D MTANN to the CAD detections showed that the 3D MTANN eliminated all RT-induced 20 FPs, as well as 53 FPs due to other causes, without removal of any true positives. Overall, the 3D MTANN was able to reduce the FP rate of the CAD scheme from 3.1 to 2.1 FPs per patient (33% reduction), while the original by-polyp sensitivity of 96.4% was maintained.« less

CAD of control systems: Application of nonlinear programming to a linear quadratic formulation

NASA Technical Reports Server (NTRS)

Fleming, P.

1983-01-01

The familiar suboptimal regulator design approach is recast as a constrained optimization problem and incorporated in a Computer Aided Design (CAD) package where both design objective and constraints are quadratic cost functions. This formulation permits the separate consideration of, for example, model following errors, sensitivity measures and control energy as objectives to be minimized or limits to be observed. Efficient techniques for computing the interrelated cost functions and their gradients are utilized in conjunction with a nonlinear programming algorithm. The effectiveness of the approach and the degree of insight into the problem which it affords is illustrated in a helicopter regulation design example.

Cost evaluation of cardiovascular magnetic resonance versus coronary angiography for the diagnostic work-up of coronary artery disease: application of the European Cardiovascular Magnetic Resonance registry data to the German, United Kingdom, Swiss, and United States health care systems.

PubMed

Moschetti, Karine; Muzzarelli, Stefano; Pinget, Christophe; Wagner, Anja; Pilz, Günther; Wasserfallen, Jean-Blaise; Schulz-Menger, Jeanette; Nothnagel, Detle; Dill, Torsten; Frank, Herbert; Lombardi, Massimo; Bruder, Oliver; Mahrholdt, Heiko; Schwitter, Jürg

2012-06-14

Cardiovascular magnetic resonance (CMR) has favorable characteristics for diagnostic evaluation and risk stratification of patients with known or suspected CAD. CMR utilization in CAD detection is growing fast. However, data on its cost-effectiveness are scarce. The goal of this study is to compare the costs of two strategies for detection of significant coronary artery stenoses in patients with suspected coronary artery disease (CAD): 1) Performing CMR first to assess myocardial ischemia and/or infarct scar before referring positive patients (defined as presence of ischemia and/or infarct scar to coronary angiography (CXA) versus 2) a hypothetical CXA performed in all patients as a single test to detect CAD. A subgroup of the European CMR pilot registry was used including 2,717 consecutive patients who underwent stress-CMR. From these patients, 21% were positive for CAD (ischemia and/or infarct scar), 73% negative, and 6% uncertain and underwent additional testing. The diagnostic costs were evaluated using invoicing costs of each test performed. Costs analysis was performed from a health care payer perspective in German, United Kingdom, Swiss, and United States health care settings. In the public sectors of the German, United Kingdom, and Swiss health care systems, cost savings from the CMR-driven strategy were 50%, 25% and 23%, respectively, versus outpatient CXA. If CXA was carried out as an inpatient procedure, cost savings were 46%, 50% and 48%, respectively. In the United States context, cost savings were 51% when compared with inpatient CXA, but higher for CMR by 8% versus outpatient CXA. This analysis suggests that from an economic perspective, the use of CMR should be encouraged as a management option for patients with suspected CAD.

A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).

PubMed

Suzuki, Kenji

2009-09-21

Computer-aided diagnosis (CAD) has been an active area of study in medical image analysis. A filter for the enhancement of lesions plays an important role for improving the sensitivity and specificity in CAD schemes. The filter enhances objects similar to a model employed in the filter; e.g. a blob-enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model; e.g. a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with internal inhomogeneities such as a nodule with spiculations and ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for the enhancement of actual lesions (as opposed to a lesion model) by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With a database of 69 lung cancers, nodule candidate detection by the MTANN filter achieved a 97% sensitivity with 6.7 false positives (FPs) per section, whereas nodule candidate detection by a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section. Classification-MTANNs were applied for further reduction of the FPs. The classification-MTANNs removed 60% of the FPs with a loss of one true positive; thus, it achieved a 96% sensitivity with 2.7 FPs per section. Overall, with our CAD scheme based on the MTANN filter and classification-MTANNs, an 84% sensitivity with 0.5 FPs per section was achieved.

Costs and clinical outcomes in individuals without known coronary artery disease undergoing coronary computed tomographic angiography from an analysis of Medicare category III transaction codes.

PubMed

Min, James K; Shaw, Leslee J; Berman, Daniel S; Gilmore, Amanda; Kang, Ning

2008-09-15

Multidetector coronary computed tomographic angiography (CCTA) demonstrates high accuracy for the detection and exclusion of coronary artery disease (CAD) and predicts adverse prognosis. To date, opportunity costs relating the clinical and economic outcomes of CCTA compared with other methods of diagnosing CAD, such as myocardial perfusion single-photon emission computed tomography (SPECT), remain unknown. An observational, multicenter, patient-level analysis of patients without known CAD who underwent CCTA or SPECT was performed. Patients who underwent CCTA (n = 1,938) were matched to those who underwent SPECT (n = 7,752) on 8 demographic and clinical characteristics and 2 summary measures of cardiac medications and co-morbidities and were evaluated for 9-month expenditures and clinical outcomes. Adjusted total health care and CAD expenditures were 27% (p <0.001) and 33% (p <0.001) lower, respectively, for patients who underwent CCTA compared with those who underwent SPECT, by an average of $467 (95% confidence interval $99 to $984) for CAD expenditures per patient. Despite lower total health care expenditures for CCTA, no differences were observed for rates of adverse cardiovascular events, including CAD hospitalizations (4.2% vs 4.1%, p = NS), CAD outpatient visits (17.4% vs 13.3%, p = NS), myocardial infarction (0.4% vs 0.6%, p = NS), and new-onset angina (3.0% vs 3.5%, p = NS). Patients without known CAD who underwent CCTA, compared with matched patients who underwent SPECT, incurred lower overall health care and CAD expenditures while experiencing similarly low rates of CAD hospitalization, outpatient visits, myocardial infarction, and angina. In conclusion, these data suggest that CCTA may be a cost-efficient alternative to SPECT for the initial coronary evaluation of patients without known CAD.

Fracture and Fatigue Resistance of Cemented versus Fused CAD-on Veneers over Customized Zirconia Implant Abutments.

PubMed

Nossair, Shereen Ahmed; Aboushelib, Moustafa N; Morsi, Tarek Salah

2015-01-05

To evaluate the fracture mechanics of cemented versus fused CAD-on veneers on customized zirconia implant abutments. Forty-five identical customized CAD/CAM zirconia implant abutments (0.5 mm thick) were prepared and seated on short titanium implant abutments (Ti base). A second scan was made to fabricate 45 CAD-on veneers (IPS Empress CAD, A2). Fifteen CAD-on veneers were cemented on the zirconia abutments (Panavia F2.0). Another 15 were fused to the zirconia abutments using low-fusing glass, while manually layered veneers served as control (n = 15). The restorations were subjected to artificial aging (3.2 million cycles between 5 and 10 kg in a water bath at 37°C) before being axially loaded to failure. Fractured specimens were examined using scanning electron microscopy to detect fracture origin, location, and size of critical crack. Stress at failure was calculated using fractography principles (alpha = 0.05). Cemented CAD-on restorations demonstrated significantly higher (F = 72, p < 0.001) fracture load compared to fused CAD-on and manually layered restorations. Fractographic analysis of fractured specimens indicated that cemented CAD-on veneers failed due to radial cracks originating from the veneer/resin interface. Branching of the critical crack was observed in the bulk of the veneer. Fused CAD-on veneers demonstrated cohesive fracture originating at the thickest part of the veneer ceramic, while manually layered veneers failed due to interfacial fracture at the zirconia/veneer interface. Within the limitations of this study, cemented CAD-on veneers on customized zirconia implant abutments demonstrated higher fracture than fused and manually layered veneers. © 2014 by the American College of Prosthodontists.

Quantitative assessment of myocardial blood flow in coronary artery disease by cardiovascular magnetic resonance: comparison of Fermi and distributed parameter modeling against invasive methods.

PubMed

Papanastasiou, Giorgos; Williams, Michelle C; Dweck, Marc R; Alam, Shirjel; Cooper, Annette; Mirsadraee, Saeed; Newby, David E; Semple, Scott I

2016-09-13

Mathematical modeling of perfusion cardiovascular magnetic resonance (CMR) data allows absolute quantification of myocardial blood flow and can potentially improve the diagnosis and prognostication of obstructive coronary artery disease (CAD), against the current clinical standard of visual assessments. This study compares the diagnostic performance of distributed parameter modeling (DP) against the standard Fermi model, for the detection of obstructive CAD, in per vessel against per patient analysis. A pilot cohort of 28 subjects (24 included in the final analysis) with known or suspected CAD underwent adenosine stress-rest perfusion CMR at 3T. Data were analysed using Fermi and DP modeling against invasive coronary angiography and fractional flow reserve, acquired in all subjects. Obstructive CAD was defined as luminal stenosis of ≥70 % alone, or luminal stenosis ≥50 % and fractional flow reserve ≤0.80. On ROC analysis, DP modeling outperformed the standard Fermi model, in per vessel and per patient analysis. In per patient analysis, DP modeling-derived myocardial blood flow at stress demonstrated the highest sensitivity and specificity (0.96, 0.92) in detecting obstructive CAD, against Fermi modeling (0.78, 0.88) and visual assessments (0.79, 0.88), respectively. DP modeling demonstrated consistently increased diagnostic performance against Fermi modeling and showed that it may have merit for stratifying patients with at least one vessel with obstructive CAD. Clinicaltrials.gov NCT01368237 Registered 6 of June 2011. URL: https://clinicaltrials.gov/ct2/show/NCT01368237.

Prevalence and extent of obstructive coronary artery disease among patients undergoing elective coronary catheterization in New York State and Ontario.

PubMed

Ko, Dennis T; Tu, Jack V; Austin, Peter C; Wijeysundera, Harindra C; Samadashvili, Zaza; Guo, Helen; Cantor, Warren J; Hannan, Edward L

2013-07-10

Prior studies have shown that physicians in New York State (New York) perform twice as many cardiac catheterizations per capita as those in Ontario for stable patients. However, the role of patient selection in these findings and their implications for detection of obstructive coronary artery disease (CAD) are largely unknown. To evaluate the extent of obstructive CAD and to compare the probability of detecting obstructive CAD for patients undergoing cardiac catheterization. An observational study was conducted involving patients without a history of cardiac disease who underwent elective cardiac catheterization between October 1, 2008, and September 30, 2011. Obstructive CAD was defined as diameter stenosis of 50% or more in the left main coronary artery or stenosis of 70% or more in a major epicardial vessel. Observed rates and predicted probabilities of obstructive CAD. Predicted probabilities were estimated using logistic regression models. A total of 18,114 patients from New York and 54,933 from Ontario were included. The observed rate of obstructive CAD was significantly lower in New York at 30.4% (95% CI, 29.7%-31.0%) than in Ontario at 44.8% (95% CI, 44.4%-45.3%; P < .001). The percentage of patients with left main or 3-vessel CAD was also significantly lower in New York than in Ontario (7.0% [95% CI, 6.6%-7.3%] vs 13.0% [95% CI, 12.8%-13.3%]; P < .001). In New York, a substantially higher percentage of patients with low predicted probability of obstructive CAD underwent cardiac catheterization; for example, only 19.3% (95% CI, 18.7%-19.9%) of patients undergoing cardiac catheterization in New York had a greater than 50% predicted probability of having obstructive CAD than those in Ontario at 41% (95% CI, 40.6%-41.4%; P < .001). At 30 days, crude mortality for patients undergoing cardiac catheterization was slightly higher in New York at 0.65% (90 of 13,824; 95% CI, 0.51%-0.78%) than in Ontario at 0.38% (153 of 40,794; 95% CI, 0.32%-0.43%; P < .001). In Ontario compared with New York State, patients undergoing elective cardiac catheterization were significantly more likely to have obstructive CAD. This appears to be related to a higher percentage of patients in New York with low predicted probability of CAD undergoing cardiac catheterization.

Hyperhomocysteinaemia, methylenetetrahydrofolate reductase polymorphism and risk of coronary artery disease.

PubMed

Kerkeni, Mohsen; Addad, Faouzi; Chauffert, Maryline; Myara, Anne; Gerhardt, Marie; Chevenne, Didier; Trivin, François; Farhat, Mohamed Ben; Miled, Abdelhedi; Maaroufi, Khira

2006-05-01

Hyperhomocysteinaemia is an independent, graded risk factor for coronary artery disease (CAD). The methylenetetrahydrofolate reductase (MTHFR) polymorphism is associated with hyperhomcysteinaemia and may therefore influence individual susceptibility to CAD. We have investigated this risk factor in a Tunisian Arab population. Polymerase chain reaction-restriction fragment length polymorphism analysis was used to detect the C677T and A1298C variants of the MTHFR gene in 100 patients with CAD and 120 healthy controls. The severity of CAD was expressed as the number of affected vessels. Plasma total homocysteine (tHcy) concentration was determined using a direct chemiluminescence assay. MTHFR CC, CT and TT genotype frequencies in the CAD group were significantly different from those observed in the control group (49%, 35% and 16% versus 48.3%, 45.8% and 5.8%, respectively; P = 0.031). However, MTHFR AA, AC and CC genotypes frequencies in the CAD group were not significantly different from the control group ( P = 0.568). Patients with CAD showed higher plasma tHcy concentrations than patients without CAD (15.86 +/- 8.63 micromol/L versus 11.90 +/- 3.25 micromol/L, P < 0.001). There was no association between the MTHFR polymorphisms and the number of stenosed vessels. Patients with the MTHFR TT genotype had higher plasma tHcy, serum creatinine, cholesterol and triglyceride concentrations than patients with the MTHFR CC genotype. The C677T polymorphism of the MTHFR gene is associated with hyperhomocysteinaemia, lipid dysregulation and the presence of CAD in this Tunisian Arab population.

Endothelial Cell-Derived Microparticles from Patients with Obstructive Sleep Apnea Hypoxia Syndrome and Coronary Artery Disease Increase Aortic Endothelial Cell Dysfunction.

PubMed

Jia, Lixin; Fan, Jingyao; Cui, Wei; Liu, Sa; Li, Na; Lau, Wayne Bond; Ma, Xinliang; Du, Jie; Nie, Shaoping; Wei, Yongxiang

2017-01-01

Obstructive sleep apnea hypoxia syndrome (OSAHS) is an independent risk factor for coronary artery disease (CAD). Treatment of OSAHS improves clinical outcome in some CAD patients, but the relationship between OSAHS and CAD is complex. Microparticles (MPs) are shed by the plasma membrane by either physiologic or pathologic stimulation. In the current study, we investigated the role of MPs in the context of OSAHS. 54 patients with both suspected coronary artery stenosis and OSAHS were recruited and underwent both coronary arteriography and polysomnography. Circulating MPs were isolated and analyzed by flow cytometry. CAD+OSAHS patients exhibited greater levels of total MPs (Annexin V+), erythrocyte-derived MPs (CD235+ Annexin V+), platelet-derived MPs (CD41+ Annexin V+), and leukocyte-derived MPs (CD45+ Annexin V+) compared to CAD alone patients or control. CAD+OSAHS patients expressed the greatest level of endothelial-derived MPs of all cellular origin types (CD144+ Annexin V +). Treatment of human aortic endothelial cells (HAECs) with MPs isolated from CAD+OSAHS patients markedly increased HAEC permeability (as detected by FITC-dextran), and significantly upregulated mRNA levels of ICAM-1, VCAM-1, and MCP-1. OSAHS+CAD patients harbor increased levels of MPs, particularly the endothelial cell-derived subtype. When administered to HAECs, OSAHS+CAD patients MPs increase endothelial cell permeability and dysfunction. © 2017 The Author(s). Published by S. Karger AG, Basel.

Bi-model processing for early detection of breast tumor in CAD system

NASA Astrophysics Data System (ADS)

Mughal, Bushra; Sharif, Muhammad; Muhammad, Nazeer

2017-06-01

Early screening of skeptical masses in mammograms may reduce mortality rate among women. This rate can be further reduced upon developing the computer-aided diagnosis system with decrease in false assumptions in medical informatics. This method highlights the early tumor detection in digitized mammograms. For improving the performance of this system, a novel bi-model processing algorithm is introduced. It divides the region of interest into two parts, the first one is called pre-segmented region (breast parenchyma) and other is the post-segmented region (suspicious region). This system follows the scheme of the preprocessing technique of contrast enhancement that can be utilized to segment and extract the desired feature of the given mammogram. In the next phase, a hybrid feature block is presented to show the effective performance of computer-aided diagnosis. In order to assess the effectiveness of the proposed method, a database provided by the society of mammographic images is tested. Our experimental outcomes on this database exhibit the usefulness and robustness of the proposed method.

High-speed autoverifying technology for printed wiring boards

NASA Astrophysics Data System (ADS)

Ando, Moritoshi; Oka, Hiroshi; Okada, Hideo; Sakashita, Yorihiro; Shibutani, Nobumi

1996-10-01

We have developed an automated pattern verification technique. The output of an automated optical inspection system contains many false alarms. Verification is needed to distinguish between minor irregularities and serious defects. In the past, this verification was usually done manually, which led to unsatisfactory product quality. The goal of our new automated verification system is to detect pattern features on surface mount technology boards. In our system, we employ a new illumination method, which uses multiple colors and multiple direction illumination. Images are captured with a CCD camera. We have developed a new algorithm that uses CAD data for both pattern matching and pattern structure determination. This helps to search for patterns around a defect and to examine defect definition rules. These are processed with a high speed workstation and a hard-wired circuits. The system can verify a defect within 1.5 seconds. The verification system was tested in a factory. It verified 1,500 defective samples and detected all significant defects with only a 0.1 percent of error rate (false alarm).

Region-growing approach to detect microcalcifications in digital mammograms

NASA Astrophysics Data System (ADS)

Shin, Jin-Wook; Chae, Soo-Ik; Sook, Yoon M.; Park, Dong-Sun

2001-09-01

Detecting early symptoms of breast cancer is very important to enhance the possibility of cure. There have been active researches to develop computer-aided diagnosis(CAD) systems detecting early symptoms of breast cancer in digital mammograms. An expert or a CAD system can recognize the early symptoms based on microcalcifications appeared in digital mammographic images. Microcalcifications have higher gray value than surrounding regions, so these can be detected by expanding a region from a local maximum. However the resultant image contains unnecessary elements such as noise, holes and valleys. Mathematical morphology is a good solution to delete regions that are affected by the unnecessary elements. In this paper, we present a method that effectively detects microcalcifications in digital mammograms using a combination of local maximum operation and the region-growing operation.

Sensitivity and specificity of a CAD solution for lung nodule detection on chest radiograph with CTA correlation.

PubMed

Moore, William; Ripton-Snyder, Jennifer; Wu, George; Hendler, Craig

2011-06-01

The objective of this research was to determine the sensitivity and specificity of a commercially available computer-aided detection (CAD) system for detection of lung nodule on posterior-anterior (PA) chest radiograph in a varied patient population who are referred to computed tomographic angiogram (CTA) of the chest as a reference standard. Patients who had a PA chest radiograph with concomitant CTA of the chest were included in this retrospective study. The PA chest radiograph was analyzed by a CAD device, and results were recorded. A qualitative assessment of the CAD results was performed using a 5-point Likert scale. The CTA was then reviewed to determine if there were correlative nodules. The presence of a correlative nodule between 0.5 cm and 1.5 cm was considered a positive result. The baseline sensitivity of the system was determined to be 0.707 (95% CI = 0.52-0.86), with a specificity of 0.50 (95% CI = 0.38-0.76). Positive predictive value was 0.30 (95% CI = 0.24-0.49), with a negative predictive value of 0.858 (95% CI = 0.82-0.95), and accuracy of 0.555 (95% CI = 0.40-0.66). When excluding nodules that were qualitatively determined by a thoracic radiologist to be false positives, the specificity was 0.781 (95% CI = 0.764-0.839), the positive predictive value was 0.564 (95% CI = 0.491-0.654), the negative predictive value was 0.829 (95% CI = 0.819-0.878), and the accuracy was 0.737 (95% CI = 0.721-0.801). The use of CAD for lung nodule detection on chest radiograph, when used in conjunction with an experienced radiologist, has a very good sensitivity, specificity, and accuracy.

Effect of eicosapentaenoic acid/docosahexaenoic acid on coronary high-intensity plaques detected with non-contrast T1-weighted imaging (the AQUAMARINE EPA/DHA study): study protocol for a randomized controlled trial.

PubMed

Nakao, Kazuhiro; Noguchi, Teruo; Asaumi, Yasuhide; Morita, Yoshiaki; Kanaya, Tomoaki; Fujino, Masashi; Hosoda, Hayato; Yoneda, Shuichi; Kawakami, Shoji; Nagai, Toshiyuki; Nishihira, Kensaku; Nakashima, Takahiro; Kumasaka, Reon; Arakawa, Tetsuo; Otsuka, Fumiyuki; Nakanishi, Michio; Kataoka, Yu; Tahara, Yoshio; Goto, Yoichi; Yamamoto, Haruko; Hamasaki, Toshimitsu; Yasuda, Satoshi

2018-01-08

Despite the success of HMG-CoA reductase inhibitor (statin) therapy in reducing atherosclerotic cardiovascular events, a residual risk for cardiovascular events in patients with coronary artery disease (CAD) remains. Long-chain n-3 polyunsaturated fatty acids (LC n-3 PUFAs), especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are promising anti-atherosclerosis agents that might reduce the residual CAD risk. Non-contrast T1-weighted imaging (T1WI) with cardiac magnetic resonance (CMR) less invasively identifies high-risk coronary plaques as high-intensity signals. These high-intensity plaques (HIPs) are quantitatively assessed using the plaque-to-myocardium signal intensity ratio (PMR). Our goal is to assess the effect of EPA/DHA on coronary HIPs detected with T1WI in patients with CAD on statin treatment. This prospective, controlled, randomized, open-label study examines the effect of 12 months of EPA/DHA therapy and statin treatment on PMR of HIPs detected with CMR and computed tomography angiography (CTA) in patients with CAD. The primary endpoint is the change in PMR after EPA/DHA treatment. Secondary endpoints include changes in Hounsfield units, plaque volume, vessel area, and plaque area measured using CTA. Subjects are randomly assigned to either of three groups: the 2 g/day EPA/DHA group, the 4 g/day EPA/DHA group, or the no-treatment group. This trial will help assess whether EPA/DHA has an anti-atherosclerotic effect using PMR of HIPs detected by CMR. The trial outcomes will provide novel insights into the effect of EPA/DHA on high-risk coronary plaques and may provide new strategies for lowering the residual risk in patients with CAD on statin therapy. The University Hospital Medical Information Network (UMIN) Clinical Trials Registry, ID: UMIN000015316 . Registered on 2 October 2014.

Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population.

PubMed

Acharya, U Rajendra; Sree, S Vinitha; Krishnan, M Muthu Rama; Molinari, Filippo; Zieleźnik, Witold; Bardales, Ricardo H; Witkowska, Agnieszka; Suri, Jasjit S

2014-02-01

Computer-aided diagnostic (CAD) techniques aid physicians in better diagnosis of diseases by extracting objective and accurate diagnostic information from medical data. Hashimoto thyroiditis is the most common type of inflammation of the thyroid gland. The inflammation changes the structure of the thyroid tissue, and these changes are reflected as echogenic changes on ultrasound images. In this work, we propose a novel CAD system (a class of systems called ThyroScan) that extracts textural features from a thyroid sonogram and uses them to aid in the detection of Hashimoto thyroiditis. In this paradigm, we extracted grayscale features based on stationary wavelet transform from 232 normal and 294 Hashimoto thyroiditis-affected thyroid ultrasound images obtained from a Polish population. Significant features were selected using a Student t test. The resulting feature vectors were used to build and evaluate the following 4 classifiers using a 10-fold stratified cross-validation technique: support vector machine, decision tree, fuzzy classifier, and K-nearest neighbor. Using 7 significant features that characterized the textural changes in the images, the fuzzy classifier had the highest classification accuracy of 84.6%, sensitivity of 82.8%, specificity of 87.0%, and a positive predictive value of 88.9%. The proposed ThyroScan CAD system uses novel features to noninvasively detect the presence of Hashimoto thyroiditis on ultrasound images. Compared to manual interpretations of ultrasound images, the CAD system offers a more objective interpretation of the nature of the thyroid. The preliminary results presented in this work indicate the possibility of using such a CAD system in a clinical setting after evaluating it with larger databases in multicenter clinical trials.

Real time myocardial contrast echocardiography during supine bicycle stress and continuous infusion of contrast agent. Cutoff values for myocardial contrast replenishment discriminating abnormal myocardial perfusion.

PubMed

Miszalski-Jamka, Tomasz; Kuntz-Hehner, Stefanie; Schmidt, Harald; Hammerstingl, Christoph; Tiemann, Klaus; Ghanem, Alexander; Troatz, Clemens; Lüderitz, Berndt; Omran, Heyder

2007-07-01

Myocardial contrast echocardiography (MCE) is a new imaging modality for diagnosing coronary artery disease (CAD). The aim of our study was to evaluate feasibility of qualitative myocardial contrast replenishment (RP) assessment during supine bicycle stress MCE and find out cutoff values for such analysis, which could allow accurate detection of CAD. Forty-four consecutive patients, scheduled for coronary angiography (CA) underwent supine bicycle stress two-dimensional echocardiography (2DE). During the same session, MCE was performed at peak stress and post stress. Ultrasound contrast agent (SonoVue) was administered in continuous mode using an infusion pump (BR-INF 100, Bracco Research). Seventeen-segment model of left ventricle was used in analysis. MCE was assessed off-line in terms of myocardial contrast opacification and RP. RP was evaluated on the basis of the number of cardiac cycles required to refill the segment with contrast after its prior destruction with high-power frames. Determination of cutoff values for RP assessment was performed by means of reference intervals and receiver operating characteristic analysis. Quantitative CA was carried out using CAAS system. MCE could be assessed in 42 patients. CA revealed CAD in 25 patients. Calculated cutoff values for RP-analysis (peak-stress RP >3 cardiac cycles and difference between peak stress and post stress RP >0 cardiac cycles) provided sensitive (88%) and accurate (88%) detection of CAD. Sensitivity and accuracy of 2DE were 76% and 79%, respectively. Qualitative RP-analysis based on the number of cardiac cycles required to refill myocardium with contrast is feasible during supine bicycle stress MCE and enables accurate detection of CAD.

Prevalence of Putative Virulence Genes in Campylobacter and Arcobacter Species Isolated from Poultry and Poultry By-Products in Tunisia.

PubMed

Jribi, Hela; Sellami, Hanen; Hassena, Amal Ben; Gdoura, Radhouane

2017-10-01

Campylobacter and Arcobacter spp. are common causes of gastroenteritis in humans; these infections are commonly due to undercooked poultry. However, their virulence mechanism is still poorly understood. The aim of this study was to evaluate the presence of genotypic virulence markers in Campylobacter and Arcobacter species using PCR. The prevalence of virulence and cytolethal distending toxin (CDT) genes was estimated in 71 Campylobacteraceae isolates. PCR was used to detect the presence of virulence genes (iam, cadF, virB1, flaA, cdtA, cdtB, and cdtC) using specific primers for a total of 45 Campylobacter isolates, including 37 C. jejuni and 8 C. coli. All the Campylobacter isolates were positive for the cadF gene. The plasmid gene virB11 was not detected in any strain. The invasion associated marker was not detected in C. jejuni. Lower detection rates were observed for flaA, cdtA, cdtB, and cdtC. The presence of nine putative Arcobacter virulence genes (cadF, ciaB, cj1349, mviN, pldA, tlyA, irgA, hecA, and hecB) was checked in a set of 22 Arcobacter butzleri and 4 Arcobacter cryaerophilus isolates. The pldA and mviN genes were predominant (88.64%). Lower detection rates were observed for tlyA (84.76%), ciaB (84.61%), cadF and cj1349 (76.92%), IrgA and hecA (61.53%), and hecB (57.69%). The findings revealed that a majority of the Campylobacteraceae strains have these putative virulence genes that may lead to pathogenic effects in humans.

Diagnostic and clinical benefit of combined coronary calcium and perfusion assessment in patients undergoing PET/CT myocardial perfusion stress imaging.

PubMed

Bybee, Kevin A; Lee, John; Markiewicz, Richard; Longmore, Ryan; McGhie, A Iain; O'Keefe, James H; Hsu, Bai-Ling; Kennedy, Kevin; Thompson, Randall C; Bateman, Timothy M

2010-04-01

A limitation of stress myocardial perfusion imaging (MPI) is the inability to detect non-obstructive coronary artery disease (CAD). One advantage of MPI with a hybrid CT device is the ability to obtain same-setting measurement of the coronary artery calcium score (CACS). Utilizing our single-center nuclear database, we identified 760 consecutive patients with: (1) no CAD history; (2) a normal clinically indicated Rb-82 PET/CT stress perfusion study; and (3) a same-setting CAC scan. 487 of 760 patients (64.1%) had subclinical CAD based on an abnormal CACS. Of those with CAC, the CACS was > or =100, > or =400, and > or =1000 in 47.0%, 22.4%, and 8.4% of patients, respectively. Less than half of the patients with CAC were receiving aspirin or statin medications prior to PET/CT imaging. Patients with CAC were more likely to be initiated or optimized on proven medical therapy for CAD immediately following PET/CT MPI compared to those without CAC. Subclinical CAD is common in patients without known CAD and normal myocardial perfusion assessed by hybrid PET/CT imaging. Identification of CAC influences subsequent physician prescribing patterns such that those with CAC are more likely to be treated with proven medical therapy for the treatment of CAD.

Red Xylem and Higher Lignin Extractability by Down-Regulating a Cinnamyl Alcohol Dehydrogenase in Poplar.

PubMed

Baucher, M.; Chabbert, B.; Pilate, G.; Van Doorsselaere, J.; Tollier, M. T.; Petit-Conil, M.; Cornu, D.; Monties, B.; Van Montagu, M.; Inze, D.; Jouanin, L.; Boerjan, W.

1996-12-01

Cinnamyl alcohol dehydrogenase (CAD) catalyzes the last step in the biosynthesis of the lignin precursors, the monolignols. We have down-regulated CAD in transgenic poplar (Populus tremula X Populus alba) by both antisense and co-suppression strategies. Several antisense and sense CAD transgenic poplars had an approximately 70% reduced CAD activity that was associated with a red coloration of the xylem tissue. Neither the lignin amount nor the lignin monomeric composition (syringyl/guaiacyl) were significantly modified. However, phloroglucinol-HCl staining was different in the down-regulated CAD plants, suggesting changes in the number of aldehyde units in the lignin. Furthermore, the reactivity of the cell wall toward alkali treatment was altered: a lower amount of lignin was found in the insoluble, saponified residue and more lignin could be precipitated from the soluble alkali fraction. Moreover, large amounts of phenolic compounds, vanillin and especially syringaldehyde, were detected in the soluble alkali fraction of the CAD down-regulated poplars. Alkaline pulping experiments on 3-month-old trees showed a reduction of the kappa number without affecting the degree of cellulose degradation. These results indicate that reducing the CAD activity in trees might be a valuable strategy to optimize certain processes of the wood industry, especially those of the pulp and paper industry.

Virulence gene profiles of Arcobacter species isolated from animals, foods of animal origin, and humans in Andhra Pradesh, India.

PubMed

Sekhar, M Soma; Tumati, S R; Chinnam, B K; Kothapalli, V S; Sharif, N Mohammad

2017-06-01

This study aimed to detect putative virulence genes in Arcobacter species of animal and human origin. A total of 41 Arcobacter isolates (16 Arcobacter butzleri , 13 Arcobacter cryaerophilus , and 12 Arcobacter skirrowii ) isolated from diverse sources such as fecal swabs of livestock (21), raw foods of animal origin (13), and human stool samples (7) were subjected to a set of six uniplex polymerase chain reaction assays targeting Arcobacter putative virulence genes ( ciaB , pldA , tlyA , mviN , cadF , and cj1349 ). All the six virulence genes were detected among all the 16 A. butzleri isolates. Among the 13 A. cryaerophilus isolates, cadF, ciaB , cj1349, mviN , pldA , and tlyA genes were detected in 61.5, 84.6, 76.9, 76.9, 61.5, and 61.5% of isolates, respectively. Among the 12 A. skirrowii isolates, cadF, ciaB , cj1349, mviN , pldA , and tlyA genes were detected in 50.0, 91.6, 83.3, 66.6, 50, and 50% of isolates, respectively. Putative virulence genes were detected in majority of the Arcobacter isolates examined. The results signify the potential of Arcobacter species as an emerging foodborne pathogen.

Automated Quantification of Pneumothorax in CT

PubMed Central

Do, Synho; Salvaggio, Kristen; Gupta, Supriya; Kalra, Mannudeep; Ali, Nabeel U.; Pien, Homer

2012-01-01

An automated, computer-aided diagnosis (CAD) algorithm for the quantification of pneumothoraces from Multidetector Computed Tomography (MDCT) images has been developed. Algorithm performance was evaluated through comparison to manual segmentation by expert radiologists. A combination of two-dimensional and three-dimensional processing techniques was incorporated to reduce required processing time by two-thirds (as compared to similar techniques). Volumetric measurements on relative pneumothorax size were obtained and the overall performance of the automated method shows an average error of just below 1%. PMID:23082091

Coronary artery atherosclerosis and risk stratification in young adults with an intermediate pretest likelihood detected by multidetector computed tomography.

PubMed

Hou, Zhi-hui; Lu, Bin; Gao, Yang; Yu, Fang-fang; Cao, Hui-li; Jiang, Shi-liang; Roy, Sion K; Budoff, Matthew J

2012-11-01

To document the prevalence of coronary artery disease (CAD) and major adverse cardiac events (MACE) in patients younger than 45 years of age with intermediate pretest likelihood of CAD, and to determine whether coronary computed tomography angiography (cCTA) is useful for risk stratification of this cohort. We followed 452 intermediate pretest likelihood (according to Diamond and Forrester) outpatients who were suspected of CAD and underwent cCTA. They were all younger than 45 years old. The endpoint was MACE, defined as composite cardiac death, nonfatal myocardial infarction, or coronary revascularization. Follow-up was completed in 427 patients (94.5%) with a median follow-up period of 1081 days. No plaque was noted in 357 (83.6%) patients. Nonsignificant CAD was noted in 33 (7.7%) individuals and 37 (8.7%) patients with significant CAD. At the end of the follow-up period, 12 (2.8%) patients experienced MACE. The annualized event rate was 0.2% in patients with no plaque, 2.0% in patients with nonsignificant CAD, and 7.3% in patients with significant CAD. Hypertension, smoking, and significant CAD in cCTA were significant predictors of MACE in univariate analysis. Moreover, cCTA remained a predictor (P < .001) of events after multivariate correction (hazard ratio: 8.345, 95% CI: 3.438-17.823, P < .001). The prevalence of CAD and MACE in young adults with an intermediate pretest likelihood of CAD was considerable. cCTA is effective in restratifying patients into either a low or high posttest risk group. These results further emphasize the usefulness of cCTA in this cohort. Copyright © 2012 AUR. Published by Elsevier Inc. All rights reserved.

The BRL-CAD Package: An Overview

DTIC Science & Technology

2013-04-01

many different display devices to be supported. The types of primatives supported include: arbitrary boxes of up to eight verticies, ellipsoids...file size. Many algorithms simply run until all of the data is gone, and some don’t even care about scan lines at aiL 5.2. Format Conversion Several

A deep-learning based automatic pulmonary nodule detection system

NASA Astrophysics Data System (ADS)

Zhao, Yiyuan; Zhao, Liang; Yan, Zhennan; Wolf, Matthias; Zhan, Yiqiang

2018-02-01

Lung cancer is the deadliest cancer worldwide. Early detection of lung cancer is a promising way to lower the risk of dying. Accurate pulmonary nodule detection in computed tomography (CT) images is crucial for early diagnosis of lung cancer. The development of computer-aided detection (CAD) system of pulmonary nodules contributes to making the CT analysis more accurate and with more efficiency. Recent studies from other groups have been focusing on lung cancer diagnosis CAD system by detecting medium to large nodules. However, to fully investigate the relevance between nodule features and cancer diagnosis, a CAD that is capable of detecting nodules with all sizes is needed. In this paper, we present a deep-learning based automatic all size pulmonary nodule detection system by cascading two artificial neural networks. We firstly use a U-net like 3D network to generate nodule candidates from CT images. Then, we use another 3D neural network to refine the locations of the nodule candidates generated from the previous subsystem. With the second sub-system, we bring the nodule candidates closer to the center of the ground truth nodule locations. We evaluate our system on a public CT dataset provided by the Lung Nodule Analysis (LUNA) 2016 grand challenge. The performance on the testing dataset shows that our system achieves 90% sensitivity with an average of 4 false positives per scan. This indicates that our system can be an aid for automatic nodule detection, which is beneficial for lung cancer diagnosis.

Research on the key technologies of 3D spatial data organization and management for virtual building environments

NASA Astrophysics Data System (ADS)

Gong, Jun; Zhu, Qing

2006-10-01

As the special case of VGE in the fields of AEC (architecture, engineering and construction), Virtual Building Environment (VBE) has been broadly concerned. Highly complex, large-scale 3d spatial data is main bottleneck of VBE applications, so 3d spatial data organization and management certainly becomes the core technology for VBE. This paper puts forward 3d spatial data model for VBE, and the performance to implement it is very high. Inherent storage method of CAD data makes data redundant, and doesn't concern efficient visualization, which is a practical bottleneck to integrate CAD model, so An Efficient Method to Integrate CAD Model Data is put forward. Moreover, Since the 3d spatial indices based on R-tree are usually limited by their weakness of low efficiency due to the severe overlap of sibling nodes and the uneven size of nodes, a new node-choosing algorithm of R-tree are proposed.

Short-Term Outcomes of Screening Mammography Using Computer-Aided Detection

PubMed Central

Fenton, Joshua J.; Xing, Guibo; Elmore, Joann G.; Bang, Heejung; Chen, Steven L.; Lindfors, Karen K.; Baldwin, Laura-Mae

2013-01-01

Background Computer-aided detection (CAD) has rapidly diffused into screening mammography practice despite limited and conflicting data on its clinical effect. Objective To determine associations between CAD use during screening mammography and the incidence of ductal carcinoma in situ (DCIS) and invasive breast cancer, invasive cancer stage, and diagnostic testing. Design Retrospective cohort study. Setting Medicare program. Participants Women aged 67 to 89 years having screening mammography between 2001 and 2006 in U.S. SEER (Surveillance, Epidemiology and End Results) regions (409 459 mammograms from 163 099 women). Measurements Incident DCIS and invasive breast cancer within 1 year after mammography, invasive cancer stage, and diagnostic testing within 90 days after screening among women without breast cancer. Results From 2001 to 2006, CAD prevalence increased from 3.6% to 60.5%. Use of CAD was associated with greater DCIS incidence (adjusted odds ratio [OR], 1.17 [95% CI, 1.11 to 1.23]) but no difference in invasive breast cancer incidence (adjusted OR, 1.00 [CI, 0.97 to 1.03]). Among women with invasive cancer, CAD was associated with greater likelihood of stage I to II versus III to IV cancer (adjusted OR, 1.27 [CI, 1.14 to 1.41]). In women without breast cancer, CAD was associated with increased odds of diagnostic mammography (adjusted OR, 1.28 [CI, 1.27 to 1.29]), breast ultrasonography (adjusted OR, 1.07 [CI, 1.06 to 1.09]), and breast biopsy (adjusted OR, 1.10 [CI, 1.08 to 1.12]). Limitation Short follow-up for cancer stage, potential unmeasured confounding, and uncertain generalizability to younger women. Conclusion Use of CAD during screening mammography among Medicare enrollees is associated with increased DCIS incidence, the diagnosis of invasive breast cancer at earlier stages, and increased diagnostic testing among women without breast cancer. Primary Funding Source Center for Healthcare Policy and Research, University of California, Davis. PMID:23588746

An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information

NASA Astrophysics Data System (ADS)

Melendez, Jaime; Sánchez, Clara I.; Philipsen, Rick H. H. M.; Maduskar, Pragnya; Dawson, Rodney; Theron, Grant; Dheda, Keertan; van Ginneken, Bram

2016-04-01

Lack of human resources and radiological interpretation expertise impair tuberculosis (TB) screening programmes in TB-endemic countries. Computer-aided detection (CAD) constitutes a viable alternative for chest radiograph (CXR) reading. However, no automated techniques that exploit the additional clinical information typically available during screening exist. To address this issue and optimally exploit this information, a machine learning-based combination framework is introduced. We have evaluated this framework on a database containing 392 patient records from suspected TB subjects prospectively recruited in Cape Town, South Africa. Each record comprised a CAD score, automatically computed from a CXR, and 12 clinical features. Comparisons with strategies relying on either CAD scores or clinical information alone were performed. Our results indicate that the combination framework outperforms the individual strategies in terms of the area under the receiving operating characteristic curve (0.84 versus 0.78 and 0.72), specificity at 95% sensitivity (49% versus 24% and 31%) and negative predictive value (98% versus 95% and 96%). Thus, it is believed that combining CAD and clinical information to estimate the risk of active disease is a promising tool for TB screening.

Effect of radiation dose reduction and iterative reconstruction on computer-aided detection of pulmonary nodules: Intra-individual comparison.

PubMed

Den Harder, Annemarie M; Willemink, Martin J; van Hamersvelt, Robbert W; Vonken, Evert-Jan P A; Milles, Julien; Schilham, Arnold M R; Lammers, Jan-Willem; de Jong, Pim A; Leiner, Tim; Budde, Ricardo P J

2016-02-01

To evaluate the effect of radiation dose reduction and iterative reconstruction (IR) on the performance of computer-aided detection (CAD) for pulmonary nodules. In this prospective study twenty-five patients were included who were scanned for pulmonary nodule follow-up. Image acquisition was performed at routine dose and three reduced dose levels in a single session by decreasing mAs-values with 45%, 60% and 75%. Tube voltage was fixed at 120 kVp for patients ≥ 80 kg and 100 kVp for patients < 80 kg. Data were reconstructed with filtered back projection (FBP), iDose(4) (levels 1,4,6) and IMR (levels 1-3). All noncalcified solid pulmonary nodules ≥ 4 mm identified by two radiologists in consensus served as the reference standard. Subsequently, nodule volume was measured with CAD software and compared to the reference consensus. The numbers of true-positives, false-positives and missed pulmonary nodules were evaluated as well as the sensitivity. Median effective radiation dose was 2.2 mSv at routine dose and 1.2, 0.9 and 0.6 mSv at respectively 45%, 60% and 75% reduced dose. A total of 28 pulmonary nodules were included. With FBP at routine dose, 89% (25/28) of the nodules were correctly identified by CAD. This was similar at reduced dose levels with FBP, iDose(4) and IMR. CAD resulted in a median number of false-positives findings of 11 per scan with FBP at routine dose (93% of the CAD marks) increasing to 15 per scan with iDose(4) (95% of the CAD marks) and 26 per scan (96% of the CAD marks) with IMR at the lowest dose level. CAD can identify pulmonary nodules at submillisievert dose levels with FBP, hybrid and model-based IR. However, the number of false-positive findings increased using hybrid and especially model-based IR at submillisievert dose while dose reduction did not affect the number of false-positives with FBP. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Nonobstructive Coronary Artery Disease by Coronary CT Angiography Improves Risk Stratification and Allocation of Statin Therapy.

PubMed

Emami, Hamed; Takx, Richard A P; Mayrhofer, Thomas; Janjua, Sumbal; Park, Jakob; Pursnani, Amit; Tawakol, Ahmed; Lu, Michael T; Ferencik, Maros; Hoffmann, Udo

2017-09-01

This study sought to determine prognostic value of nonobstructive coronary artery disease (CAD) for atherosclerotic cardiovascular disease (ASCVD) events and to determine whether incorporation of this information into the pooled cohort equation reclassifies recommendations for statin therapy as defined by the 2013 guidelines for cholesterol management of the American College of Cardiology and American Heart Association (ACC/AHA). Detection of nonobstructive CAD by coronary computed tomography angiography may improve risk stratification and permit individualized and more appropriate allocation of statin therapy. This study determined the pooled hazard ratio of nonobstructive CAD for ASCVD events from published studies and incorporated this information into the ACC/AHA pooled cohort equation. The study calculated revised sex- and ethnicity-based 10-year ASCVD risk and determined boundaries corresponding to the original 7.5% risk for ASCVD events. It also assessed reclassification for statin eligibility by incorporating the results from meta-analysis to individual patients from a separate cohort. This study included 2 studies (2,295 subjects; 66% male; prevalence of nonobstructive CAD, 47%; median follow-up, 49 months; 67 ASCVD events). The hazard ratio of nonobstructive CAD for ASCVD events was 3.2 (95% confidence interval: 1.5 to 6.7). Incorporation of this information into the pooled cohort equation resulted in reclassification toward statin eligibility in individuals with nonobstructive CAD, with an original ASCVD score of 3.0% and 5.9% or higher in African-American women and men and a score of 4.4% and 4.6% or higher in Caucasian women and men, respectively. The absence of nonobstructive CAD resulted in reclassification toward statin ineligibility if the original ASCVD score was as 10.0% and 17.9% or lower in African-American women and men and 13.7% and 14.3% or lower in Caucasian women and men, respectively. Reclassification is observed in 14% of patients. Detection of nonobstructive CAD by coronary computed tomography angiography improves risk stratification and permits individualized and more appropriate allocation of statin therapy across sex and ethnicity groups. Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Interpretation Time Using a Concurrent-Read Computer-Aided Detection System for Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breast Tissue.

PubMed

Jiang, Yulei; Inciardi, Marc F; Edwards, Alexandra V; Papaioannou, John

2018-05-24

The purpose of this study was to compare diagnostic accuracy and interpretation time of screening automated breast ultrasound (ABUS) for women with dense breast tissue without and with use of a recently U.S. Food and Drug Administration-approved computer-aided detection (CAD) system for concurrent read. In a retrospective observer performance study, 18 radiologists interpreted a cancer-enriched set (i.e., cancer prevalence higher than in the original screening cohort) of 185 screening ABUS studies (52 with and 133 without breast cancer). These studies were from a large cohort of ABUS screened patients interpreted as BI-RADS density C or D. Each reader interpreted each case twice in a counterbalanced study, once without the CAD system and once with it, separated by 4 weeks. For each case, each reader identified abnormal findings and reported BI-RADS assessment category and level of suspicion for breast cancer. Interpretation time was recorded. Level of suspicion data were compared to evaluate diagnostic accuracy by means of the Dorfman-Berbaum-Metz method of jackknife with ANOVA ROC analysis. Interpretation times were compared by ANOVA. The ROC AUC was 0.848 with the CAD system, compared with 0.828 without it, for a difference of 0.020 (95% CI, -0.011 to 0.051) and was statistically noninferior to the AUC without the CAD system with respect to a margin of -0.05 (p = 0.000086). The mean interpretation time was 3 minutes 33 seconds per case without the CAD system and 2 minutes 24 seconds with it, for a difference of 1 minute 9 seconds saved (95% CI, 44-93 seconds; p = 0.000014), or a reduction in interpretation time to 67% of the time without the CAD system. Use of the concurrent-read CAD system for interpretation of screening ABUS studies of women with dense breast tissue who do not have symptoms is expected to make interpretation significantly faster and produce noninferior diagnostic accuracy compared with interpretation without the CAD system.

CT versus MR Techniques in the Detection of Cervical Artery Dissection.

PubMed

Hanning, Uta; Sporns, Peter B; Schmiedel, Meilin; Ringelstein, Erich B; Heindel, Walter; Wiendl, Heinz; Niederstadt, Thomas; Dittrich, Ralf

2017-11-01

Spontaneous cervical artery dissection (sCAD) is an important etiology of juvenile stroke. The gold standard for the diagnosis of sCAD is convential angiography. However, magnetic resonance imaging (MRI)/MR angiography (MRA) and computed tomography (CT)/CT angiography (CTA) are frequently used alternatives. New developments such as multislice CT/CTA have enabled routine acquisition of thinner sections with rapid imaging times. The goal of this study was to compare the capability of recent developed 128-slice CT/CTA to MRI/MRA to detect radiologic features of sCAD. Retrospective review of patients with suspected sCAD (n = 188) in a database of our Stroke center (2008-2014), who underwent CT/CTA and MRI/MRA on initial clinical work-up. A control group of 26 patients was added. All Images were evaluated concerning specific and sensitive radiological features for dissection by two experienced neuroradiologists. Imaging features were compared between the two modalities. Forty patients with 43 dissected arteries received both modalities (29 internal carotid arteries [ICAs] and 14 vertebral arteries [VAs]). All CADs were identified in CT/CTA and MRI/MRA. The features intimal flap, stenosis, and lumen irregularity appeared in both modalities. One high-grade stenosis was identified by CT/CTA that was expected occluded on MRI/MRA. Two MRI/MRA-confirmed pseudoaneurysms were missed by CT/CTA. None of the controls evidenced specific imaging signs for dissection. CT/CTA is a reliable and better available alternative to MRI/MRA for diagnosis of sCAD. CT/CTA should be used to complement MRI/MRA in cases where MRI/MRA suggests occlusion. Copyright © 2017 by the American Society of Neuroimaging.

Computed tomography versus invasive coronary angiography: design and methods of the pragmatic randomised multicentre DISCHARGE trial.

PubMed

Napp, Adriane E; Haase, Robert; Laule, Michael; Schuetz, Georg M; Rief, Matthias; Dreger, Henryk; Feuchtner, Gudrun; Friedrich, Guy; Špaček, Miloslav; Suchánek, Vojtěch; Fuglsang Kofoed, Klaus; Engstroem, Thomas; Schroeder, Stephen; Drosch, Tanja; Gutberlet, Matthias; Woinke, Michael; Maurovich-Horvat, Pál; Merkely, Béla; Donnelly, Patrick; Ball, Peter; Dodd, Jonathan D; Quinn, Martin; Saba, Luca; Porcu, Maurizio; Francone, Marco; Mancone, Massimo; Erglis, Andrejs; Zvaigzne, Ligita; Jankauskas, Antanas; Sakalyte, Gintare; Harań, Tomasz; Ilnicka-Suckiel, Malgorzata; Bettencourt, Nuno; Gama-Ribeiro, Vasco; Condrea, Sebastian; Benedek, Imre; Čemerlić Adjić, Nada; Adjić, Oto; Rodriguez-Palomares, José; Garcia Del Blanco, Bruno; Roditi, Giles; Berry, Colin; Davis, Gershan; Thwaite, Erica; Knuuti, Juhani; Pietilä, Mikko; Kępka, Cezary; Kruk, Mariusz; Vidakovic, Radosav; Neskovic, Aleksandar N; Díez, Ignacio; Lecumberri, Iñigo; Geleijns, Jacob; Kubiak, Christine; Strenge-Hesse, Anke; Do, The-Hoang; Frömel, Felix; Gutiérrez-Ibarluzea, Iñaki; Benguria-Arrate, Gaizka; Keiding, Hans; Katzer, Christoph; Müller-Nordhorn, Jacqueline; Rieckmann, Nina; Walther, Mario; Schlattmann, Peter; Dewey, Marc

2017-07-01

More than 3.5 million invasive coronary angiographies (ICA) are performed in Europe annually. Approximately 2 million of these invasive procedures might be reduced by noninvasive tests because no coronary intervention is performed. Computed tomography (CT) is the most accurate noninvasive test for detection and exclusion of coronary artery disease (CAD). To investigate the comparative effectiveness of CT and ICA, we designed the European pragmatic multicentre DISCHARGE trial funded by the 7th Framework Programme of the European Union (EC-GA 603266). In this trial, patients with a low-to-intermediate pretest probability (10-60 %) of suspected CAD and a clinical indication for ICA because of stable chest pain will be randomised in a 1-to-1 ratio to CT or ICA. CT and ICA findings guide subsequent management decisions by the local heart teams according to current evidence and European guidelines. Major adverse cardiovascular events (MACE) defined as cardiovascular death, myocardial infarction and stroke as a composite endpoint will be the primary outcome measure. Secondary and other outcomes include cost-effectiveness, radiation exposure, health-related quality of life (HRQoL), socioeconomic status, lifestyle, adverse events related to CT/ICA, and gender differences. The DISCHARGE trial will assess the comparative effectiveness of CT and ICA. • Coronary artery disease (CAD) is a major cause of morbidity and mortality. • Invasive coronary angiography (ICA) is the reference standard for detection of CAD. • Noninvasive computed tomography angiography excludes CAD with high sensitivity. • CT may effectively reduce the approximately 2 million negative ICAs in Europe. • DISCHARGE addresses this hypothesis in patients with low-to-intermediate pretest probability for CAD.

Creation of system of computer-aided design for technological objects

NASA Astrophysics Data System (ADS)

Zubkova, T. M.; Tokareva, M. A.; Sultanov, N. Z.

2018-05-01

Due to the competition in the market of process equipment, its production should be flexible, retuning to various product configurations, raw materials and productivity, depending on the current market needs. This process is not possible without CAD (computer-aided design). The formation of CAD begins with planning. Synthesizing, analyzing, evaluating, converting operations, as well as visualization and decision-making operations, can be automated. Based on formal description of the design procedures, the design route in the form of an oriented graph is constructed. The decomposition of the design process, represented by the formalized description of the design procedures, makes it possible to make an informed choice of the CAD component for the solution of the task. The object-oriented approach allows us to consider the CAD as an independent system whose properties are inherited from the components. The first step determines the range of tasks to be performed by the system, and a set of components for their implementation. The second one is the configuration of the selected components. The interaction between the selected components is carried out using the CALS standards. The chosen CAD / CAE-oriented approach allows creating a single model, which is stored in the database of the subject area. Each of the integration stages is implemented as a separate functional block. The transformation of the CAD model into the model of the internal representation is realized by the block of searching for the geometric parameters of the technological machine, in which the XML-model of the construction is obtained on the basis of the feature method from the theory of image recognition. The configuration of integrated components is divided into three consecutive steps: configuring tasks, components, interfaces. The configuration of the components is realized using the theory of "soft computations" using the Mamdani fuzzy inference algorithm.

Coronary microvascular rarefaction and myocardial fibrosis in heart failure with preserved ejection fraction.

PubMed

Mohammed, Selma F; Hussain, Saad; Mirzoyev, Sultan A; Edwards, William D; Maleszewski, Joseph J; Redfield, Margaret M

2015-02-10

Characterization of myocardial structural changes in heart failure with preserved ejection fraction (HFpEF) has been hindered by the limited availability of human cardiac tissue. Cardiac hypertrophy, coronary artery disease (CAD), coronary microvascular rarefaction, and myocardial fibrosis may contribute to HFpEF pathophysiology. We identified HFpEF patients (n=124) and age-appropriate control subjects (noncardiac death, no heart failure diagnosis; n=104) who underwent autopsy. Heart weight and CAD severity were obtained from the autopsy reports. With the use of whole-field digital microscopy and automated analysis algorithms in full-thickness left ventricular sections, microvascular density (MVD), myocardial fibrosis, and their relationship were quantified. Subjects with HFpEF had heavier hearts (median, 538 g; 169% of age-, sex-, and body size-expected heart weight versus 335 g; 112% in controls), more severe CAD (65% with ≥1 vessel with >50% diameter stenosis in HFpEF versus 13% in controls), more left ventricular fibrosis (median % area fibrosis, 9.6 versus 7.1) and lower MVD (median 961 versus 1316 vessels/mm(2)) than control (P<0.0001 for all). Myocardial fibrosis increased with decreasing MVD in controls (r=-0.28, P=0.004) and HFpEF (r=-0.26, P=0.004). Adjusting for MVD attenuated the group differences in fibrosis. Heart weight, fibrosis, and MVD were similar in HFpEF patients with CAD versus without CAD. In this study, patients with HFpEF had more cardiac hypertrophy, epicardial CAD, coronary microvascular rarefaction, and myocardial fibrosis than controls. Each of these findings may contribute to the left ventricular diastolic dysfunction and cardiac reserve function impairment characteristic of HFpEF. © 2014 American Heart Association, Inc.

The Z-cad dual fluorescent sensor detects dynamic changes between the epithelial and mesenchymal cellular states.

PubMed

Toneff, M J; Sreekumar, A; Tinnirello, A; Hollander, P Den; Habib, S; Li, S; Ellis, M J; Xin, L; Mani, S A; Rosen, J M

2016-06-17

The epithelial to mesenchymal transition (EMT) has been implicated in metastasis and therapy resistance of carcinomas and can endow cancer cells with cancer stem cell (CSC) properties. The ability to detect cancer cells that are undergoing or have completed EMT has typically relied on the expression of cell surface antigens that correlate with an EMT/CSC phenotype. Alternatively these cells may be permanently marked through Cre-mediated recombination or through immunostaining of fixed cells. The EMT process is dynamic, and these existing methods cannot reveal such changes within live cells. The development of fluorescent sensors that mirror the dynamic EMT state by following the expression of bona fide EMT regulators in live cells would provide a valuable new tool for characterizing EMT. In addition, these sensors will allow direct observation of cellular plasticity with respect to the epithelial/mesenchymal state to enable more effective studies of EMT in cancer and development. We generated a lentiviral-based, dual fluorescent reporter system, designated as the Z-cad dual sensor, comprising destabilized green fluorescent protein containing the ZEB1 3' UTR and red fluorescent protein driven by the E-cadherin (CDH1) promoter. Using this sensor, we robustly detected EMT and mesenchymal to epithelial transition (MET) in breast cancer cells by flow cytometry and fluorescence microscopy. Importantly, we observed dynamic changes in cellular populations undergoing MET. Additionally, we used the Z-cad sensor to identify and isolate minor subpopulations of cells displaying mesenchymal properties within a population comprising predominately epithelial-like cells. The Z-cad dual sensor identified cells with CSC-like properties more effectively than either the ZEB1 3' UTR or E-cadherin sensor alone. The Z-cad dual sensor effectively reports the activities of two factors critical in determining the epithelial/mesenchymal state of carcinoma cells. The ability of this stably integrating dual sensor system to detect dynamic fluctuations between these two states through live cell imaging offers a significant improvement over existing methods and helps facilitate the study of EMT/MET plasticity in response to different stimuli and in cancer pathogenesis. Finally, the versatile Z-cad sensor can be adapted to a variety of in vitro or in vivo systems to elucidate whether EMT/MET contributes to normal and disease phenotypes.

Gene-gene and gene-environment interactions: new insights into the prevention, detection and management of coronary artery disease.

PubMed

Lanktree, Matthew B; Hegele, Robert A

2009-02-26

Despite the recent success of genome-wide association studies (GWASs) in identifying loci consistently associated with coronary artery disease (CAD), a large proportion of the genetic components of CAD and its metabolic risk factors, including plasma lipids, type 2 diabetes and body mass index, remain unattributed. Gene-gene and gene-environment interactions might produce a meaningful improvement in quantification of the genetic determinants of CAD. Testing for gene-gene and gene-environment interactions is thus a new frontier for large-scale GWASs of CAD. There are several anecdotal examples of monogenic susceptibility to CAD in which the phenotype was worsened by an adverse environment. In addition, small-scale candidate gene association studies with functional hypotheses have identified gene-environment interactions. For future evaluation of gene-gene and gene-environment interactions to achieve the same success as the single gene associations reported in recent GWASs, it will be important to pre-specify agreed standards of study design and statistical power, environmental exposure measurement, phenomic characterization and analytical strategies. Here we discuss these issues, particularly in relation to the investigation and potential clinical utility of gene-gene and gene-environment interactions in CAD.

Cardiovascular Risk Assessment and Management in Prerenal Transplantation Candidates.

PubMed

Lindley, Eric M; Hall, Amanda K; Hess, Jordan; Abraham, Jo; Smith, Brigham; Hopkins, Paul N; Shihab, Fuad; Welt, Frederick; Owan, Theophilus; Fang, James C

2016-01-01

Cardiovascular (CV) assessment in prerenal transplant patients varies by center. Current guidelines recommend stress testing for candidates if ≥ 3 CV risk factors exist. We evaluated the CV assessment and management in 685 patients referred for kidney transplant over a 7-year period. All patients had CV risk factors, and the most common cause of end-stage renal disease was diabetes. Thirty-three percent (n = 229) underwent coronary angiography. The sensitivity of stress testing to detect obstructive coronary artery disease (CAD) was poor (0.26). Patients who had no CAD, nonobstructive CAD, or CAD with intervention had significantly higher event-free survival compared with patients with obstructive CAD without intervention. There were no adverse clinical events (death, myocardial infarction, stroke, revascularization, and graft failure) within 30 days post-transplant in patients who had preoperative angiography (n = 77). Of the transplanted patients who did not have an angiogram (n = 289), there were 8 clinical events (6 myocardial infarctions) in the first 30 days. In conclusion, our results indicate that stress testing and usual risk factors were poor predictors of obstructive CAD and that revascularization may prove beneficial in these patients. Copyright © 2016 Elsevier Inc. All rights reserved.

Prevalence and severity of coronary artery disease in diabetic patients with aortic valve calcification.

PubMed

Chen, Zhang-wei; Qian, Ju-ying; Jian, Ying; Ge, Lei; Liu, Xue-bo; Shu, Xian-hong; Ge, Junbo

2011-02-01

Aortic valve calcification (AVC) is common in the elderly and associated with increased cardiovascular mortality, while diabetes is one of the confirmed risk factors for coronary artery disease (CAD). In this study, we aimed to evaluate the prevalence and severity of CAD in type-2 diabetic patients with AVC. From June to December in 2007, a total of 325 consecutive patients with chest pain or chest distress were admitted for coronary angiography. The severity of CAD was evaluated by the Gensini score and the number of stenosed vessels. All patients underwent transthoracic echocardiography for detecting AVC. Compared with the patients without diabetes (n = 221), the type-2 diabetic patients (n = 104) had a similar prevalence of CAD (66.5% vs. 72.1%, P = 0.312). Further classified by the presence of AVC, patients with AVC had a higher prevalence of CAD, average Gensini score and the number of stenosed vessels, both in the group with and without diabetes. It was also demonstrated that the odds ratio (OR) of AVC for CAD in the diabetic patients was higher than in the non-diabetic ones (3.405 vs 2.515) after chi-square analysis (single-variable). However, at multivariable logistic regression analysis for CAD, the OR of AVC was 3.757 (P = 0.03) in diabetic group, while it did not achieve statistical significance in the non-diabetic group (OR = 2.130, P= 0.074). Type-2 diabetic patients with AVC had a higher prevalence of and more severe CAD.

Computer-aided mass detection in mammography: False positive reduction via gray-scale invariant ranklet texture features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Masotti, Matteo; Lanconelli, Nico; Campanini, Renato

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Second, our previous CAD system achieves invariance to linear/nonlinear monotonic gray-scale transformations by encoding regions of interest into ranklet images through the ranklet transform, an image transformation similar to the wavelet transform, yet dealing with pixels' ranks rather than with theirmore » gray-scale values. Therefore, the new FPR approach proposed herein defines a set of texture features which are calculated directly from the ranklet images corresponding to the regions of interest surviving our previous CAD system, hence, ranklet texture features; then, a support vector machine (SVM) classifier is used for discrimination. As a result of this approach, texture-based information is used to discriminate FP marks surviving our previous CAD system; at the same time, invariance to linear/nonlinear monotonic gray-scale transformations of the new CAD system is guaranteed, as ranklet texture features are calculated from ranklet images that have this property themselves by construction. To emphasize the gray-scale invariance of both the previous and new CAD systems, training and testing are carried out without any in-between parameters' adjustment on mammograms having different gray-scale dynamics; in particular, training is carried out on analog digitized mammograms taken from a publicly available digital database, whereas testing is performed on full-field digital mammograms taken from an in-house database. Free-response receiver operating characteristic (FROC) curve analysis of the two CAD systems demonstrates that the new approach achieves a higher reduction of FP marks when compared to the previous one. Specifically, at 60%, 65%, and 70% per-mammogram sensitivity, the new CAD system achieves 0.50, 0.68, and 0.92 FP marks per mammogram, whereas at 70%, 75%, and 80% per-case sensitivity it achieves 0.37, 0.48, and 0.71 FP marks per mammogram, respectively. Conversely, at the same sensitivities, the previous CAD system reached 0.71, 0.87, and 1.15 FP marks per mammogram, and 0.57, 0.73, and 0.92 FPs per mammogram. Also, statistical significance of the difference between the two per-mammogram and per-case FROC curves is demonstrated by the p-value<0.001 returned by jackknife FROC analysis performed on the two CAD systems.« less

A new set of wavelet- and fractals-based features for Gleason grading of prostate cancer histopathology images

NASA Astrophysics Data System (ADS)

Mosquera Lopez, Clara; Agaian, Sos

2013-02-01

Prostate cancer detection and staging is an important step towards patient treatment selection. Advancements in digital pathology allow the application of new quantitative image analysis algorithms for computer-assisted diagnosis (CAD) on digitized histopathology images. In this paper, we introduce a new set of features to automatically grade pathological images using the well-known Gleason grading system. The goal of this study is to classify biopsy images belonging to Gleason patterns 3, 4, and 5 by using a combination of wavelet and fractal features. For image classification we use pairwise coupling Support Vector Machine (SVM) classifiers. The accuracy of the system, which is close to 97%, is estimated through three different cross-validation schemes. The proposed system offers the potential for automating classification of histological images and supporting prostate cancer diagnosis.

A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers.

PubMed

Tartar, A; Akan, A; Kilic, N

2014-01-01

Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves high classification performance. The proposed approach with bagging classifier results in 94.7 %, 90.0 % and 77.8 % classification sensitivities for benign, malignant and undetermined classes (89.5 % accuracy), respectively.

An Audit on the Appropriateness of Coronary Computed Tomography Angiography Referrals in a Tertiary Cardiac Center.

PubMed

Alderazi, Ahmed Ali; Lynch, Mary

2017-01-01

In response to growing concerns regarding the overuse of coronary computed tomography angiography (CCTA) in the clinical setting, multiple societies, including the American College of Cardiology Foundation, have jointly published revised criteria regarding the appropriate use of this imaging modality. However, previous research indicates significant discrepancies in the rate of adherence to these guidelines. To assess the appropriateness of CCTA referrals in a tertiary cardiac center in Bahrain. This retrospective clinical audit examined the records of patients referred to CCTA between the April 1, 2015 and December 31, 2015 in Mohammed bin Khalifa Cardiac Center. Using information from medical records, each case was meticulously audited against guidelines to categorize it as appropriate, inappropriate, or uncertain. Of the 234 records examined, 176 (75.2%) were appropriate, 47 (20.1%) were uncertain, and 11 (4.7%) were inappropriate. About 74.4% of all referrals were to investigate coronary artery disease (CAD). The most common indication that was deemed appropriate was the detection of CAD in the setting of suspected ischemic equivalent in patients with an intermediate pretest probability of CAD (65.9%). Most referrals deemed inappropriate were requested to detect CAD in asymptomatic patients at low or intermediate risk of CAD (63.6%). This audit demonstrates a relatively low rate of inappropriate CCTA referrals, indicating the appropriate and efficient use of this resource in the Mohammed bin Khalifa Cardiac Center. Agreement on and reclassification of "uncertain" cases by guideline authorities would facilitate a deeper understanding of referral appropriateness.

A hybrid deep learning approach to predict malignancy of breast lesions using mammograms

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Heidari, Morteza; Mirniaharikandehei, Seyedehnafiseh; Gong, Jing; Qian, Wei; Qiu, Yuchen; Zheng, Bin

2018-03-01

Applying deep learning technology to medical imaging informatics field has been recently attracting extensive research interest. However, the limited medical image dataset size often reduces performance and robustness of the deep learning based computer-aided detection and/or diagnosis (CAD) schemes. In attempt to address this technical challenge, this study aims to develop and evaluate a new hybrid deep learning based CAD approach to predict likelihood of a breast lesion detected on mammogram being malignant. In this approach, a deep Convolutional Neural Network (CNN) was firstly pre-trained using the ImageNet dataset and serve as a feature extractor. A pseudo-color Region of Interest (ROI) method was used to generate ROIs with RGB channels from the mammographic images as the input to the pre-trained deep network. The transferred CNN features from different layers of the CNN were then obtained and a linear support vector machine (SVM) was trained for the prediction task. By applying to a dataset involving 301 suspicious breast lesions and using a leave-one-case-out validation method, the areas under the ROC curves (AUC) = 0.762 and 0.792 using the traditional CAD scheme and the proposed deep learning based CAD scheme, respectively. An ensemble classifier that combines the classification scores generated by the two schemes yielded an improved AUC value of 0.813. The study results demonstrated feasibility and potentially improved performance of applying a new hybrid deep learning approach to develop CAD scheme using a relatively small dataset of medical images.

Exercise thallium-201 tomographic scintigraphy in the diagnosis of coronary artery disease: emphasis on the effect of exercise level.

PubMed

Huang, P J; Chieng, P U; Lee, Y T; Chiang, F T; Tseng, Y Z; Liau, C S; Tseng, C D; Su, C T; Lien, W P

1992-11-01

Exercise thallium-201 imaging using single-photon emission computed tomography (SPECT) was evaluated in 154 patients with angiographically documented coronary artery disease (CAD) and in 25 normal subjects. Of the 154 patients with CAD, 134 (87%) had abnormal thallium images. By contrast, only 77 (50%) patients had ischemic ST-segment depression (p < 0.001). Among 25 normal subjects, 20 had normal exercise SPECT images. The specificity of exercise SPECT imaging (80% or 20/25) in excluding patients with CAD was not significantly higher than that of exercise electrocardiography (76% or 19/25). For the detection of individual vessel involvement by analysis of territories of perfusion abnormalities, the sensitivity and specificity of exercise SPECT were 72% and 96% for the left anterior descending, 78% and 85% for the right coronary, and 47% and 98% for the left circumflex artery. Ninety (group 1) of the 154 patients with CAD achieved adequate exercise end points (ischemic ST-segment depression or > 85% of maximal predicted heart rate) and 64 (group 2) did not. Exercise SPECT showed significantly more perfusion abnormalities in group 1 than in group 2 (96% vs 75%, p < 0.001). We conclude that: (1) exercise SPECT thallium imaging is more sensitive than exercise electrocardiography for detecting patients with CAD; (2) the sensitivity of the test is affected by the level of exercise; and (3) it is valuable in the identification of individual vessel involvement.

Adjoint Algorithm for CAD-Based Shape Optimization Using a Cartesian Method

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.

2004-01-01

Adjoint solutions of the governing flow equations are becoming increasingly important for the development of efficient analysis and optimization algorithms. A well-known use of the adjoint method is gradient-based shape optimization. Given an objective function that defines some measure of performance, such as the lift and drag functionals, its gradient is computed at a cost that is essentially independent of the number of design variables (geometric parameters that control the shape). More recently, emerging adjoint applications focus on the analysis problem, where the adjoint solution is used to drive mesh adaptation, as well as to provide estimates of functional error bounds and corrections. The attractive feature of this approach is that the mesh-adaptation procedure targets a specific functional, thereby localizing the mesh refinement and reducing computational cost. Our focus is on the development of adjoint-based optimization techniques for a Cartesian method with embedded boundaries.12 In contrast t o implementations on structured and unstructured grids, Cartesian methods decouple the surface discretization from the volume mesh. This feature makes Cartesian methods well suited for the automated analysis of complex geometry problems, and consequently a promising approach to aerodynamic optimization. Melvin et developed an adjoint formulation for the TRANAIR code, which is based on the full-potential equation with viscous corrections. More recently, Dadone and Grossman presented an adjoint formulation for the Euler equations. In both approaches, a boundary condition is introduced to approximate the effects of the evolving surface shape that results in accurate gradient computation. Central to automated shape optimization algorithms is the issue of geometry modeling and control. The need to optimize complex, "real-life" geometry provides a strong incentive for the use of parametric-CAD systems within the optimization procedure. In previous work, we presented an effective optimization framework that incorporates a direct-CAD interface. In this work, we enhance the capabilities of this framework with efficient gradient computations using the discrete adjoint method. We present details of the adjoint numerical implementation, which reuses the domain decomposition, multigrid, and time-marching schemes of the flow solver. Furthermore, we explain and demonstrate the use of CAD in conjunction with the Cartesian adjoint approach. The final paper will contain a number of complex geometry, industrially relevant examples with many design variables to demonstrate the effectiveness of the adjoint method on Cartesian meshes.

Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics.

PubMed

Abdolali, Fatemeh; Zoroofi, Reza Aghaeizadeh; Otake, Yoshito; Sato, Yoshinobu

2017-02-01

Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential. This paper presents a novel framework for maxillofacial cysts detection. A hybrid methodology based on surface and texture information is introduced. The proposed approach consists of three main steps as follows: At first, each cystic lesion is segmented with high accuracy. Then, in the second and third steps, feature extraction and classification are performed. Contourlet and SPHARM coefficients are utilized as texture and shape features which are fed into the classifier. Two different classifiers are used in this study, i.e. support vector machine and sparse discriminant analysis. Generally SPHARM coefficients are estimated by the iterative residual fitting (IRF) algorithm which is based on stepwise regression method. In order to improve the accuracy of IRF estimation, a method based on extra orthogonalization is employed to reduce linear dependency. We have utilized a ground-truth dataset consisting of cone beam CT images of 96 patients, belonging to three maxillofacial cyst categories: radicular cyst, dentigerous cyst and keratocystic odontogenic tumor. Using orthogonalized SPHARM, residual sum of squares is decreased which leads to a more accurate estimation. Analysis of the results based on statistical measures such as specificity, sensitivity, positive predictive value and negative predictive value is reported. The classification rate of 96.48% is achieved using sparse discriminant analysis and orthogonalized SPHARM features. Classification accuracy at least improved by 8.94% with respect to conventional features. This study demonstrated that our proposed methodology can improve the computer assisted diagnosis (CAD) performance by incorporating more discriminative features. Using orthogonalized SPHARM is promising in computerized cyst detection and may have a significant impact in future CAD systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

PubMed

Manzi, Alessandro; Dario, Paolo; Cavallo, Filippo

2017-05-11

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

Assistive lesion-emphasis system: an assistive system for fundus image readers

PubMed Central

Rangrej, Samrudhdhi B.; Sivaswamy, Jayanthi

2017-01-01

Abstract. Computer-assisted diagnostic (CAD) tools are of interest as they enable efficient decision-making in clinics and the screening of diseases. The traditional approach to CAD algorithm design focuses on the automated detection of abnormalities independent of the end-user, who can be an image reader or an expert. We propose a reader-centric system design wherein a reader’s attention is drawn to abnormal regions in a least-obtrusive yet effective manner, using saliency-based emphasis of abnormalities and without altering the appearance of the background tissues. We present an assistive lesion-emphasis system (ALES) based on the above idea, for fundus image-based diabetic retinopathy diagnosis. Lesion-saliency is learnt using a convolutional neural network (CNN), inspired by the saliency model of Itti and Koch. The CNN is used to fine-tune standard low-level filters and learn high-level filters for deriving a lesion-saliency map, which is then used to perform lesion-emphasis via a spatially variant version of gamma correction. The proposed system has been evaluated on public datasets and benchmarked against other saliency models. It was found to outperform other saliency models by 6% to 30% and boost the contrast-to-noise ratio of lesions by more than 30%. Results of a perceptual study also underscore the effectiveness and, hence, the potential of ALES as an assistive tool for readers. PMID:28560245

The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

NASA Astrophysics Data System (ADS)

Meijs, M.; Debats, O.; Huisman, H.

2015-03-01

In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Lu, Xianglan; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Li, Shibo; Liu, Hong; Zheng, Bin

2016-03-01

Automated high throughput scanning microscopy is a fast developing screening technology used in cytogenetic laboratories for the diagnosis of leukemia or other genetic diseases. However, one of the major challenges of using this new technology is how to efficiently detect the analyzable metaphase chromosomes during the scanning process. The purpose of this investigation is to develop a computer aided detection (CAD) scheme based on deep learning technology, which can identify the metaphase chromosomes with high accuracy. The CAD scheme includes an eight layer neural network. The first six layers compose of an automatic feature extraction module, which has an architecture of three convolution-max-pooling layer pairs. The 1st, 2nd and 3rd pair contains 30, 20, 20 feature maps, respectively. The seventh and eighth layers compose of a multiple layer perception (MLP) based classifier, which is used to identify the analyzable metaphase chromosomes. The performance of new CAD scheme was assessed by receiver operation characteristic (ROC) method. A number of 150 regions of interest (ROIs) were selected to test the performance of our new CAD scheme. Each ROI contains either interphase cell or metaphase chromosomes. The results indicate that new scheme is able to achieve an area under the ROC curve (AUC) of 0.886+/-0.043. This investigation demonstrates that applying a deep learning technique may enable to significantly improve the accuracy of the metaphase chromosome detection using a scanning microscopic imaging technology in the future.

Outcomes of anatomical vs. functional testing for coronary artery disease : Lessons from the PROMISE trial.

PubMed

Shah, R; Foldyna, B; Hoffmann, U

2016-08-01

The development of coronary artery disease (CAD) is a major, final common pathway in heart disease worldwide. With a rise in stress testing and increased scrutiny on cost-effectiveness and radiation exposure in medical imaging, a focus on the relative merits of anatomic versus functional characterization of CAD has emerged. In this context, coronary computed tomography angiography (CCTA) is a noninvasive alternative to functional testing as a first-line test for CAD detection but is complimentary in its nature. Here, we discuss the design, results, and implications of the PROMISE trial, a randomized comparative effectiveness study of 10,003 patients across 193 sites in the United States and Canada comparing the prognostic and diagnostic power of CCTA and standard stress testing. Specifically, we discuss the safety (e. g., contrast, radiation exposure) of CCTA versus functional testing in CAD, the need for improved selection for noninvasive testing, the frequency of downstream testing after anatomic or functional imaging, the use of imaging results in clinical management, and novel modalities of CAD risk determination using CCTA. PROMISE demonstrated that in a real-world, low-to-intermediate risk patient population referred to noninvasive testing for CAD, both CCTA and functional testing approaches have similar clinical, economic, and safety-based outcomes. We conclude with open questions in CAD imaging, specifically as they pertain to the utilization of CCTA.

Combined ECG, Echocardiographic, and Biomarker Criteria for Diagnosing Acute Myocardial Infarction in Out-of-Hospital Cardiac Arrest Patients.

PubMed

Lee, Sang-Eun; Uhm, Jae-Sun; Kim, Jong-Youn; Pak, Hui-Nam; Lee, Moon-Hyoung; Joung, Boyoung

2015-07-01

Acute coronary lesions commonly trigger out-of-hospital cardiac arrest (OHCA). However, the prevalence of coronary artery disease (CAD) in Asian patients with OHCA and whether electrocardiogram (ECG) and other findings might predict acute myocardial infarction (AMI) have not been fully elucidated. Of 284 consecutive resuscitated OHCA patients seen between January 2006 and July 2013, we enrolled 135 patients who had undergone coronary evaluation. ECGs, echocardiography, and biomarkers were compared between patients with or without CAD. We included 135 consecutive patients aged 54 years (interquartile range 45-65) with sustained return of spontaneous circulation after OHCA between 2006 and 2012. Sixty six (45%) patients had CAD. The initial rhythm was shockable and non-shockable in 110 (81%) and 25 (19%) patients, respectively. ST-segment elevation predicted CAD with 42% sensitivity, 87% specificity, and 65% accuracy. ST elevation and/or regional wall motion abnormality (RWMA) showed 68% sensitivity, 52% specificity, and 70% accuracy in the prediction of CAD. Finally, a combination of ST elevation and/or RWMA and/or troponin T elevation predicted CAD with 94% sensitivity, 17% specificity, and 55% accuracy. In patients with OHCA without obvious non-cardiac causes, selection for coronary angiogram based on the combined criterion could detect 94% of CADs. However, compared with ECG only criteria, the combined criterion failed to improve diagnostic accuracy with a lower specificity.

Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

PubMed

Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

2014-08-01

Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature discrepancies and prove advantageous not only at the candidate detection level, but also at subsequent steps of a CAD system.

Absolute shape measurements using high-resolution optoelectronic holography methods

NASA Astrophysics Data System (ADS)

Furlong, Cosme; Pryputniewicz, Ryszard J.

2000-01-01

Characterization of surface shape and deformation is of primary importance in a number of testing and metrology applications related to the functionality, performance, and integrity of components. In this paper, a unique, compact, and versatile state-of-the-art fiber-optic-based optoelectronic holography (OEH) methodology is described. This description addresses apparatus and analysis algorithms, especially developed to perform measurements of both absolute surface shape and deformation. The OEH can be arranged in multiple configurations, which include the three-camera, three-illumination, and in-plane speckle correlation setups. With the OEH apparatus and analysis algorithms, absolute shape measurements can be made, using present setup, with a spatial resolution and accuracy of better than 30 and 10 micrometers , respectively, for volumes characterized by a 300-mm length. Optimizing the experimental setup and incorporating equipment, as it becomes available, having superior capabilities to the ones utilized in the present investigations can further increase resolution and accuracy in the measurements. The particular feature of this methodology is its capability to export the measurements data directly into CAD environments for subsequent processing, analysis, and definition of CAD/CAE models.

Impact of inflammation, gene variants, and cigarette smoking on coronary artery disease risk.

PubMed

Merhi, Mahmoud; Demirdjian, Sally; Hariri, Essa; Sabbah, Nada; Youhanna, Sonia; Ghassibe-Sabbagh, Michella; Naoum, Joseph; Haber, Marc; Othman, Raed; Kibbani, Samer; Chammas, Elie; Kanbar, Roy; Bayeh, Hamid El; Chami, Youssef; Abchee, Antoine; Platt, Daniel E; Zalloua, Pierre; Khazen, Georges

2015-06-01

The role of inflammation in coronary artery disease (CAD) pathogenesis is well recognized. Moreover, smoking inhalation increases the activity of inflammatory mediators through an increase in leukotriene synthesis essential in atherosclerosis pathogenesis. The aim of this study is to investigate the effect of "selected" genetic variants within the leukotriene (LT) pathway and other variants on the development of CAD. CAD was detected by cardiac catheterization. Logistic regression was performed to investigate the association of smoking and selected susceptibility variants in the LT pathway including ALOX5AP, LTA4H, LTC4S, PON1, and LTA as well as CYP1A1 on CAD risk while controlling for age, gender, BMI, family history, diabetes, hyperlipidemia, and hypertension. rs4769874 (ALOX5AP), rs854560 (PON1), and rs4646903 (CYP1A1 MspI polymorphism) are significantly associated with an increased risk of CAD with respective odds ratios of 1.53703, 1.67710, and 1.35520; the genetic variant rs9579646 (ALOX5AP) is significantly associated with a decreased risk of CAD (OR 0.76163). Moreover, a significant smoking-gene interaction is determined with CYP1A1 MspI polymorphism rs4646903 and is associated with a decreased risk of CAD in current smokers (OR 0.52137). This study provides further evidence that genetic variation of the LT pathway, PON1, and CYP1A1 can modulate the atherogenic processes and eventually increase the risk of CAD in our study population. Moreover, it also shows the effect of smoking-gene interaction on CAD risk, where the CYP1A1 MspI polymorphism revealed a decreased risk in current smokers.

Relationship of dental diseases with coronary artery diseases and diabetes in Bangladesh

PubMed Central

Choudhury, Arup Ratan; Choudhury, Kamrun Nahar

2016-01-01

Background Evidence suggests that dental diseases might have a role in the development and progression of coronary artery diseases (CAD) and diabetes mellitus (DM). The objective of this study was to determine the relationship of dental diseases with CAD and DM in Bangladesh. Methods We conducted a cross-sectional study among 216 consecutive patients admitted in a tertiary hospital between March and July 2011. Data were collected on socio-demographic status, smoking, blood pressure (BP), diet, physical activities, and biochemical measurements of lipid profile, glycated hemoglobin (HbA1c), C-reactive protein (CRP), fibrinogen, creatinine kinase MB (CK-MB), troponin, serum creatinine and serum glutamic-pyruvic transaminase (SGPT). CAD was detected using echocardiographic and coronary angiogram (CAG) reports. All patients underwent oral examination for dental disease. Relationship between dental disease with CAD and DM were explored statistically. Results The mean age of the participants was 57.8±12.5 years and almost two-thirds (67.1%) were male. A great majority of the patients had CAD (90.3%) and type 2 DM (83.8%), and only 44% suffered from dental diseases. Less than one-third patients presented with acute myocardial infarction (MI), 23% with old MI, 11% unstable angina (UA) and 26.4% with non-ST elevation MI. Logistic regression results indicated that patients with DM and CAD had approximately 2.6 and 4.6 times more odds of association with dental diseases than those without DM and CAD (both P value <0.001). Conclusions This study suggests a relationship of dental diseases with CAD and DM among Bangladeshi patients. Further studies are required to confirm these relationships in large clinical studies. Screening for CAD and DM should be considered among those with dental diseases and vice-versa. PMID:27054102

The effect of postural changes (leg lifting) on tissue Doppler parameters in coronary artery disease.

PubMed

Pirat, Bahar; Yildirir, Aylin; Simşek, Vahide; Ozin, Bülent; Müderrisoğlu, Haldun

2008-03-01

We investigated the effect of increased preload through postural changes (leg lifting) on tissue Doppler parameters in patients with and without coronary artery disease (CAD). The study included 42 patients who were scheduled for coronary angiography. All the patients underwent standard two-dimensional, color Doppler and tissue Doppler echocardiography before coronary angiography. Tissue Doppler imaging was performed from septal and lateral mitral annuluses at baseline and during 45 degrees leg lifting followed by two-minute stabilization. Patients were grouped based on coronary angiography findings: those having stenosis greater than 70% were considered to have CAD and those with normal coronary arteries comprised the control group. Echocardiography measurements were compared between the two groups. Angiography showed normal coronary arteries or border irregularities in 22 patients and CAD in 20 patients. The two groups were similar with regard to demographic data and ejection fractions, except for male preponderance in the CAD group. Compared with the control group, patients with CAD exhibited a significantly lower isovolumic acceleration rate (IVA) at the lateral (p=0.007) and septal (p=0.03) mitral annuluses. In the control group, leg lifting resulted in increased systolic velocity (S) compared with baseline at the lateral (p=0.009) and septal (p=0.01) annuluses, whereas S wave augmentation was only significant at the septal annulus (p=0.009) in patients with CAD. No significant change was observed in IVA following leg lifting in both groups. Preload alteration induced by leg lifting resulted in similar changes in tissue Doppler parameters in patients with and without CAD, except for blunted augmentation of S wave at the lateral annulus in CAD. Detection of decreased IVA at baseline may be a useful finding for CAD.

Evaluating the cost implications of a radio frequency identification feeding system for early detection of bovine respiratory disease in feedlot cattle.

PubMed

Wolfger, Barbara; Manns, Braden J; Barkema, Herman W; Schwartzkopf-Genswein, Karen S; Dorin, Craig; Orsel, Karin

2015-03-01

New technologies to identify diseased feedlot cattle in early stages of illness have been developed to reduce costs and welfare impacts associated with bovine respiratory disease (BRD). However, the economic value of early BRD detection has never been assessed. The objective was to simulate cost differences between two BRD detection methods during the first 61 d on feed (DOF) applied in moderate- to large-sized feedlots using an automated recording system (ARS) for feeding behavior and the current industry standard, pen-checking (visual appraisal confirmed by rectal temperature). Economic impact was assessed with a cost analysis in a simple decision model. Scenarios for Canadian and US feedlots with high- and low-risk cattle were modeled, and uncertainty was estimated using extensive sensitivity analyses. Input costs and probabilities were mainly extracted from publicly accessible market observations and a large-scale US feedlot study. In the baseline scenario, we modeled high-risk cattle with a treatment rate of 20% within the first 61 DOF in a feedlot of >8000 cattle in Canada. Early BRD detection was estimated to result in a relative risk of 0.60 in retreatment and 0.66 in mortality compared to pen-checking (based on previously published estimates). The additional cost of monitoring health with ARS in Canadian dollar (CAD) was 13.68 per steer. Scenario analysis for similar sized US feedlots and low-risk cattle with a treatment rate of 8% were included to account for variability in costs and probabilities in various cattle populations. Considering the cost of monitoring, all relevant treatment costs and sale price, ARS was more costly than visual appraisal during the first 61 DOF by CAD 9.61 and CAD 9.69 per steer in Canada and the US, respectively. This cost difference increased in low-risk cattle in Canada to CAD 12.45. Early BRD detection with ARS became less expensive if the costs for the system decreased to less than CAD 4.06/steer, or if the underlying true BRD incidence (not treatment rate) within the first 61 DOF exceeded 47%. The model was robust to variability in the remaining input variables. Some of the assumptions in the baseline analyses were conservative and may have underestimated the real value of early BRD detection. Systems such as ARS may reduce treatment costs in some scenarios, but the investment costs are currently too high to be cost-effective when used solely for BRD detection compared to pen-checking. Copyright © 2014 Elsevier B.V. All rights reserved.

Study on computer-aided diagnosis of hepatic MR imaging and mammography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang Xuejun

2005-04-01

It is well known that the liver is an organ easily attacked by diseases. The purpose of this study is to develop a computer-aided diagnosis (CAD) scheme for helping radiologists to differentiate hepatic diseases more efficiently. Our software named LIVERANN integrated the magnetic resonance (MR) imaging findings with different pulse sequences to classify the five categories of hepatic diseases by using the artificial neural network (ANN) method. The intensity and homogeneity within the region of interest (ROI) delineated by a radiologist were automatically calculated to obtain numerical data by the program for input signals to the ANN. Outputs were themore » five pathological categories of hepatic diseases (hepatic cyst, hepatocellular carcinoma, dysplasia in cirrhosis, cavernous hemangioma, and metastasis). The experiment demonstrated a testing accuracy of 93% from 80 patients. In order to differentiate the cirrhosis from normal liver, the volume ratio of left to whole (LTW) was proposed to quantify the degree of cirrhosis by three-dimensional (3D) volume analysis. The liver region was firstly extracted from computed tomography (CT) or MR slices based on edge detection algorithms, and then separated into left lobe and right lobe by the hepatic umbilical fissure. The volume ratio of these two parts showed that the LTW ratio in the liver was significantly improved in the differentiation performance, with (25.6%{+-}4.3%) in cirrhosis versus the normal liver (16.4%{+-}5.4%). In addition, the application of the ANN method for detecting clustered microcalcifications in masses on mammograms was described here as well. A new structural ANN, so-called a shift-invariant artificial neural network (SIANN), was integrated with our triple-ring filter (TRF) method in our CAD system. As the result, the sensitivity of detecting clusters was improved from 90% by our previous TRF method to 95% by using both SIANN and TRF.« less

Integrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease

PubMed Central

Mäkinen, Ville-Petteri; Civelek, Mete; Meng, Qingying; Zhang, Bin; Zhu, Jun; Levian, Candace; Huan, Tianxiao; Segrè, Ayellet V.; Ghosh, Sujoy; Vivar, Juan; Nikpay, Majid; Stewart, Alexandre F. R.; Nelson, Christopher P.; Willenborg, Christina; Erdmann, Jeanette; Blakenberg, Stefan; O'Donnell, Christopher J.; März, Winfried; Laaksonen, Reijo; Epstein, Stephen E.; Kathiresan, Sekar; Shah, Svati H.; Hazen, Stanley L.; Reilly, Muredach P.; Lusis, Aldons J.; Samani, Nilesh J.; Schunkert, Heribert; Quertermous, Thomas; McPherson, Ruth; Yang, Xia; Assimes, Themistocles L.

2014-01-01

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions. PMID:25033284

An interactive modeling program for the generation of planar polygons for boundary type solids representations of wire frame models

NASA Technical Reports Server (NTRS)

Ozsoy, T.; Ochs, J. B.

1984-01-01

The development of a general link between three dimensional wire frame models and rigid solid models is discussed. An interactive computer graphics program was developed to serve as a front end to an algorithm (COSMIC Program No. ARC-11446) which offers a general solution to the hidden line problem where the input data is either line segments of n-sided planar polygons of the most general type with internal boundaries. The program provides a general interface to CAD/CAM data bases and is implemented for models created on the Unigraphics VAX 11/780-based CAD/CAM systems with the display software written for DEC's VS11 color graphics devices.

Optimized FPGA Implementation of the Thyroid Hormone Secretion Mechanism Using CAD Tools.

PubMed

Alghazo, Jaafar M

2017-02-01

The goal of this paper is to implement the secretion mechanism of the Thyroid Hormone (TH) based on bio-mathematical differential eqs. (DE) on an FPGA chip. Hardware Descriptive Language (HDL) is used to develop a behavioral model of the mechanism derived from the DE. The Thyroid Hormone secretion mechanism is simulated with the interaction of the related stimulating and inhibiting hormones. Synthesis of the simulation is done with the aid of CAD tools and downloaded on a Field Programmable Gate Arrays (FPGAs) Chip. The chip output shows identical behavior to that of the designed algorithm through simulation. It is concluded that the chip mimics the Thyroid Hormone secretion mechanism. The chip, operating in real-time, is computer-independent stand-alone system.

Research on conceptual/innovative design for the life cycle

NASA Technical Reports Server (NTRS)

Cagan, Jonathan; Agogino, Alice M.

1990-01-01

The goal of this research is developing and integrating qualitative and quantitative methods for life cycle design. The definition of the problem includes formal computer-based methods limited to final detailing stages of design; CAD data bases do not capture design intent or design history; and life cycle issues were ignored during early stages of design. Viewgraphs outline research in conceptual design; the SYMON (SYmbolic MONotonicity analyzer) algorithm; multistart vector quantization optimization algorithm; intelligent manufacturing: IDES - Influence Diagram Architecture; and 1st PRINCE (FIRST PRINciple Computational Evaluator).

Increasing CAD system efficacy for lung texture analysis using a convolutional network

NASA Astrophysics Data System (ADS)

Tarando, Sebastian Roberto; Fetita, Catalin; Faccinetto, Alex; Brillet, Pierre-Yves

2016-03-01

The infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. For the large majority of CAD systems, such classification relies on a two-dimensional analysis of axial CT images. In a previously developed CAD system, we proposed a fully-3D approach exploiting a multi-scale morphological analysis which showed good performance in detecting diseased areas, but with a major drawback consisting of sometimes overestimating the pathological areas and mixing different type of lung patterns. This paper proposes a combination of the existing CAD system with the classification outcome provided by a convolutional network, specifically tuned-up, in order to increase the specificity of the classification and the confidence to diagnosis. The advantage of using a deep learning approach is a better regularization of the classification output (because of a deeper insight into a given pathological class over a large series of samples) where the previous system is extra-sensitive due to the multi-scale response on patient-specific, localized patterns. In a preliminary evaluation, the combined approach was tested on a 10 patient database of various lung pathologies, showing a sharp increase of true detections.

High-resolution myocardial stress perfusion at 3 T in patients with suspected coronary artery disease.

PubMed

Meyer, Carsten; Strach, Katharina; Thomas, Daniel; Litt, Harold; Nähle, Claas P; Tiemann, Klaus; Schwenger, Ulrich; Schild, Hans H; Sommer, Torsten

2008-02-01

To implement a high-resolution first-pass myocardial perfusion imaging protocol (HRPI) at 3 T, and to evaluate the feasibility, image quality and accuracy of this approach prospectively in patients with suspected CAD. We hypothesized that utilizing the gain in SNR at 3 T to increase spatial resolution would reduce partial volume effects and subendocardial dark rim artifacts in comparison to 1.5 T. HRPI studies were performed on 60 patients using a segmented k-space gradient echo sequence (in plane resolution 1.97 x 1.94 mm(2)). Semiquantitative assessment of dark rim artifacts was performed for the stress studies on a slice-by-slice basis. Qualitative visual analysis was compared to quantitative coronary angiography (QCA) results; hemodynamically significant CAD was defined as stenosis >or=70% at QCA. Dark rim artifacts appeared in 108 of 180 slices (average extent 1.3 +/- 1.2 mm representing 11.8 +/- 10.8% of the transmural myocardial thickness). Sensitivity, specifity, and test accuracy for the detection of significant CAD were 89%,79%, and 85%. HRPI studies at 3 T are feasible in a clinical setting, providing good image quality and high accuracy for detection of significant CAD. The presence of dark rim artifacts does not appear to represent a diagnostic problem when using a HRPI approach.

Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

PubMed Central

Benammar Elgaaied, Amel; Cascio, Donato; Bruno, Salvatore; Ciaccio, Maria Cristina; Cipolla, Marco; Fauci, Alessandro; Morgante, Rossella; Taormina, Vincenzo; Gorgi, Yousr; Marrakchi Triki, Raja; Ben Ahmed, Melika; Louzir, Hechmi; Yalaoui, Sadok; Imene, Sfar; Issaoui, Yassine; Abidi, Ahmed; Ammar, Myriam; Bedhiafi, Walid; Ben Fraj, Oussama; Bouhaha, Rym; Hamdi, Khouloud; Soumaya, Koudhi; Neili, Bilel; Asma, Gati; Lucchese, Mariano; Catanzaro, Maria; Barbara, Vincenza; Brusca, Ignazio; Fregapane, Maria; Amato, Gaetano; Friscia, Giuseppe; Neila, Trai; Turkia, Souayeh; Youssra, Haouami; Rekik, Raja; Bouokez, Hayet; Vasile Simone, Maria; Fauci, Francesco; Raso, Giuseppe

2016-01-01

Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of a CAD (Computer Aided Detection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%). PMID:27042658

MT's algorithm: A new algorithm to search for the optimum set of modulation indices for simultaneous range, command, and telemetry

NASA Technical Reports Server (NTRS)

Nguyen, Tien Manh

1989-01-01

MT's algorithm was developed as an aid in the design of space telecommunications systems when utilized with simultaneous range/command/telemetry operations. This algorithm provides selection of modulation indices for: (1) suppression of undesired signals to achieve desired link performance margins and/or to allow for a specified performance degradation in the data channel (command/telemetry) due to the presence of undesired signals (interferers); and (2) optimum power division between the carrier, the range, and the data channel. A software program using this algorithm was developed for use with MathCAD software. This software program, called the MT program, provides the computation of optimum modulation indices for all possible cases that are recommended by the Consultative Committee on Space Data System (CCSDS) (with emphasis on the squarewave, NASA/JPL ranging system).

Rapid Process to Generate Beam Envelopes for Optical System Analysis

NASA Technical Reports Server (NTRS)

Howard, Joseph; Seals, Lenward

2012-01-01

The task of evaluating obstructions in the optical throughput of an optical system requires the use of two disciplines, and hence, two models: optical models for the details of optical propagation, and mechanical models for determining the actual structure that exists in the optical system. Previous analysis methods for creating beam envelopes (or cones of light) for use in this obstruction analysis were found to be cumbersome to calculate and take significant time and resources to complete. A new process was developed that takes less time to complete beam envelope analysis, is more accurate and less dependent upon manual node tracking to create the beam envelopes, and eases the burden on the mechanical CAD (computer-aided design) designers to form the beam solids. This algorithm allows rapid generation of beam envelopes for optical system obstruction analysis. Ray trace information is taken from optical design software and used to generate CAD objects that represent the boundary of the beam envelopes for detailed analysis in mechanical CAD software. Matlab is used to call ray trace data from the optical model for all fields and entrance pupil points of interest. These are chosen to be the edge of each space, so that these rays produce the bounding volume for the beam. The x and y global coordinate data is collected on the surface planes of interest, typically an image of the field and entrance pupil internal of the optical system. This x and y coordinate data is then evaluated using a convex hull algorithm, which removes any internal points, which are unnecessary to produce the bounding volume of interest. At this point, tolerances can be applied to expand the size of either the field or aperture, depending on the allocations. Once this minimum set of coordinates on the pupil and field is obtained, a new set of rays is generated between the field plane and aperture plane (or vice-versa). These rays are then evaluated at planes between the aperture and field, at a desired number of steps perceived necessary to build up the bounding volume or cone shape. At each plane, the ray coordinates are again evaluated using the convex hull algorithm to reduce the data to a minimal set. When all of the coordinates of interest are obtained for every plane of the propagation, the data is formatted into an xyz file suitable for FRED optical analysis software to import and create a STEP file of the data. This results in a spiral-like structure that is easily imported by mechanical CAD users who can then use an automated algorithm to wrap a skin around it and create a solid that represents the beam.

Precursor ion scan driven fast untargeted screening and semi-determination of caffeoylquinic acid derivatives in Cynara scolymus L.

PubMed

Shen, Qing; Lu, Yanbin; Dai, Zhiyuan; Cheung, Hon-Yeung

2015-01-01

A precursor ion scan (PIS) technique based strategy was developed for rapid screening and semi-determination of caffeoylquinic acid derivatives (CADs) in artichoke (Cynara scolymus L.) using ultra-performance liquid chromatography (UPLC) coupled with tandem mass spectrometry. 1,5-Dicaffeoylquinic acid and 5-caffeoylquinic acid were used for studying the fragmentation behaviour of two classes of CADs, setting m/z 191 as a diagnostic moiety. When it was applied to artichoke sample, ten CADs were detected and elucidated in a single PIS run. Furthermore, method validation was implemented including: specificity (no interference), linearity (≥0.9993), limit of detection (LOD<0.12 ng mL(-1)) and limit of quantification (LOQ<0.25 ng mL(-1)), precision (RSD≤3.6), recovery (91.4-95.9%) and stability (at least 12 h). This approach was proven to be a powerful, selective and sensitive tool for rapid screening and semi-determination of untargeted components in natural products. Copyright © 2014 Elsevier Ltd. All rights reserved.

Implementation Research to Inform the Use of Xpert MTB/RIF in Primary Health Care Facilities in High TB and HIV Settings in Resource Constrained Settings.

PubMed

Muyoyeta, Monde; Moyo, Maureen; Kasese, Nkatya; Ndhlovu, Mapopa; Milimo, Deborah; Mwanza, Winfridah; Kapata, Nathan; Schaap, Albertus; Godfrey Faussett, Peter; Ayles, Helen

2015-01-01

The current cost of Xpert MTB RIF (Xpert) consumables is such that algorithms are needed to select which patients to prioritise for testing with Xpert. To evaluate two algorithms for prioritisation of Xpert in primary health care settings in a high TB and HIV burden setting. Consecutive, presumptive TB patients with a cough of any duration were offered either Xpert or Fluorescence microscopy (FM) test depending on their CXR score or HIV status. In one facility, sputa from patients with an abnormal CXR were tested with Xpert and those with a normal CXR were tested with FM ("CXR algorithm"). CXR was scored automatically using a Computer Aided Diagnosis (CAD) program. In the other facility, patients who were HIV positive were tested using Xpert and those who were HIV negative were tested with FM ("HIV algorithm"). Of 9482 individuals pre-screened with CXR, Xpert detected TB in 2090/6568 (31.8%) with an abnormal CXR, and FM was AFB positive in 8/2455 (0.3%) with a normal CXR. Of 4444 pre-screened with HIV, Xpert detected TB in 508/2265 (22.4%) HIV positive and FM was AFB positive in 212/1920 (11.0%) in HIV negative individuals. The notification rate of new bacteriologically confirmed TB increased; from 366 to 620/ 100,000/yr and from 145 to 261/100,000/yr at the CXR and HIV algorithm sites respectively. The median time to starting TB treatment at the CXR site compared to the HIV algorithm site was; 1(IQR 1-3 days) and 3 (2-5 days) (p<0.0001) respectively. Use of Xpert in a resource-limited setting at primary care level in conjunction with pre-screening tests reduced the number of Xpert tests performed. The routine use of Xpert resulted in additional cases of confirmed TB patients starting treatment. However, there was no increase in absolute numbers of patients starting TB treatment. Same day diagnosis and treatment commencement was achieved for both bacteriologically confirmed and empirically diagnosed patients where Xpert was used in conjunction with CXR.

Unique antibody responses to malondialdehyde-acetaldehyde (MAA)-protein adducts predict coronary artery disease.

PubMed

Anderson, Daniel R; Duryee, Michael J; Shurmur, Scott W; Um, John Y; Bussey, Walter D; Hunter, Carlos D; Garvin, Robert P; Sayles, Harlan R; Mikuls, Ted R; Klassen, Lynell W; Thiele, Geoffrey M

2014-01-01

Malondialdehyde-acetaldehyde adducts (MAA) have been implicated in atherosclerosis. The purpose of this study was to investigate the role of MAA in atherosclerotic disease. Serum samples from controls (n = 82) and patients with; non-obstructive coronary artery disease (CAD), (n = 40), acute myocardial infarction (AMI) (n = 42), or coronary artery bypass graft (CABG) surgery due to obstructive multi-vessel CAD (n = 72), were collected and tested for antibody isotypes to MAA-modifed human serum albumin (MAA-HSA). CAD patients had elevated relative levels of IgG and IgA anti-MAA, compared to control patients (p<0.001). AMI patients had a significantly increased relative levels of circulating IgG anti-MAA-HSA antibodies as compared to stable angina (p<0.03) or CABG patients (p<0.003). CABG patients had significantly increased relative levels of circulating IgA anti-MAA-HSA antibodies as compared to non-obstructive CAD (p<0.001) and AMI patients (p<0.001). Additionally, MAA-modified proteins were detected in the tissue of human AMI lesions. In conclusion, the IgM, IgG and IgA anti-MAA-HSA antibody isotypes are differentially and significantly associated with non-obstructive CAD, AMI, or obstructive multi-vessel CAD and may serve as biomarkers of atherosclerotic disease.

Unique Antibody Responses to Malondialdehyde-Acetaldehyde (MAA)-Protein Adducts Predict Coronary Artery Disease

PubMed Central

Anderson, Daniel R.; Duryee, Michael J.; Shurmur, Scott W.; Um, John Y.; Bussey, Walter D.; Hunter, Carlos D.; Garvin, Robert P.; Sayles, Harlan R.; Mikuls, Ted R.; Klassen, Lynell W.; Thiele, Geoffrey M.

2014-01-01

Malondialdehyde-acetaldehyde adducts (MAA) have been implicated in atherosclerosis. The purpose of this study was to investigate the role of MAA in atherosclerotic disease. Serum samples from controls (n = 82) and patients with; non-obstructive coronary artery disease (CAD), (n = 40), acute myocardial infarction (AMI) (n = 42), or coronary artery bypass graft (CABG) surgery due to obstructive multi-vessel CAD (n = 72), were collected and tested for antibody isotypes to MAA-modifed human serum albumin (MAA-HSA). CAD patients had elevated relative levels of IgG and IgA anti-MAA, compared to control patients (p<0.001). AMI patients had a significantly increased relative levels of circulating IgG anti-MAA-HSA antibodies as compared to stable angina (p<0.03) or CABG patients (p<0.003). CABG patients had significantly increased relative levels of circulating IgA anti-MAA-HSA antibodies as compared to non-obstructive CAD (p<0.001) and AMI patients (p<0.001). Additionally, MAA-modified proteins were detected in the tissue of human AMI lesions. In conclusion, the IgM, IgG and IgA anti-MAA-HSA antibody isotypes are differentially and significantly associated with non-obstructive CAD, AMI, or obstructive multi-vessel CAD and may serve as biomarkers of atherosclerotic disease. PMID:25210746

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve for Therapeutic Decision Making.

PubMed

Tesche, Christian; Vliegenthart, Rozemarijn; Duguay, Taylor M; De Cecco, Carlo N; Albrecht, Moritz H; De Santis, Domenico; Langenbach, Marcel C; Varga-Szemes, Akos; Jacobs, Brian E; Jochheim, David; Baquet, Moritz; Bayer, Richard R; Litwin, Sheldon E; Hoffmann, Ellen; Steinberg, Daniel H; Schoepf, U Joseph

2017-12-15

This study investigated the performance of coronary computed tomography angiography (cCTA) with cCTA-derived fractional flow reserve (CT-FFR) compared with invasive coronary angiography (ICA) with fractional flow reserve (FFR) for therapeutic decision making in patients with suspected coronary artery disease (CAD). Seventy-four patients (62 ± 11 years, 62% men) with at least 1 coronary stenosis of ≥50% on clinically indicated dual-source cCTA, who had subsequently undergone ICA with FFR measurement, were retrospectively evaluated. CT-FFR values were computed using an on-site machine-learning algorithm to assess the functional significance of CAD. The therapeutic strategy (optimal medical therapy alone vs revascularization) and the appropriate revascularization procedure (percutaneous coronary intervention vs coronary artery bypass grafting) were selected using cCTA-CT-FFR. Thirty-six patients (49%) had a functionally significant CAD based on ICA-FFR. cCTA-CT-FFR correctly identified a functionally significant CAD and the need of revascularization in 35 of 36 patients (97%). When revascularization was deemed indicated, the same revascularization procedure (32 percutaneous coronary interventions and 3 coronary artery bypass grafting) was chosen in 35 of 35 patients (100%). Overall, identical management strategies were selected in 73 of the 74 patients (99%). cCTA-CT-FFR shows excellent performance to identify patients with and without the need for revascularization and to select the appropriate revascularization strategy. cCTA-CT-FFR as a noninvasive "one-stop shop" has the potential to change diagnostic workflows and to directly inform therapeutic decision making in patients with suspected CAD. Copyright © 2017 Elsevier Inc. All rights reserved.

Vertebral degenerative disc disease severity evaluation using random forest classification

NASA Astrophysics Data System (ADS)

Munoz, Hector E.; Yao, Jianhua; Burns, Joseph E.; Pham, Yasuyuki; Stieger, James; Summers, Ronald M.

2014-03-01

Degenerative disc disease (DDD) develops in the spine as vertebral discs degenerate and osseous excrescences or outgrowths naturally form to restabilize unstable segments of the spine. These osseous excrescences, or osteophytes, may progress or stabilize in size as the spine reaches a new equilibrium point. We have previously created a CAD system that detects DDD. This paper presents a new system to determine the severity of DDD of individual vertebral levels. This will be useful to monitor the progress of developing DDD, as rapid growth may indicate that there is a greater stabilization problem that should be addressed. The existing DDD CAD system extracts the spine from CT images and segments the cortical shell of individual levels with a dual-surface model. The cortical shell is unwrapped, and is analyzed to detect the hyperdense regions of DDD. Three radiologists scored the severity of DDD of each disc space of 46 CT scans. Radiologists' scores and features generated from CAD detections were used to train a random forest classifier. The classifier then assessed the severity of DDD at each vertebral disc level. The agreement between the computer severity score and the average radiologist's score had a quadratic weighted Cohen's kappa of 0.64.

Coronary artery disease: new insights into the pathophysiology, prevalence, and early detection of a monster menace.

PubMed

Slater, James; Rill, Velisar

2004-04-01

Coronary artery disease (CAD) is the leading cause of morbidity and mortality in the United States and other industrialized countries. In the undeveloped world a similar epidemic is brewing. A new pathophysiologic paradigm has emerged, which assigns the mediators of inflammation a much larger role in the disease process. This paradigm has helped explain the unpredictable nature of many adverse consequences of CAD. The long latent phase of the disease, and often sudden initial presentation, make efforts at early detection extremely important. Considerable work has been devoted to identify, as well as influence, predisposing risk factors for developing arteriosclerosis. Novel markers of inflammation, like C-reactive protein, have been identified and compared to traditional risk factors. In addition, new imaging modalities introduce the possibility of screening for subclinical disease. Electron beam and multidetector computed tomography (CT) scanners, as well as other techniques, are emerging as powerful tools to detect early disease presence and allow intervention to take place before major clinical events occur. Advances in our understanding of the pathophysiology of CAD, and our ability to image the stages of silent disease will go hand in hand to revolutionize our approach to prevention and treatment of this deadly malady.

Development of an improved CAD scheme for automated detection of lung nodules in digital chest images.

PubMed

Xu, X W; Doi, K; Kobayashi, T; MacMahon, H; Giger, M L

1997-09-01

Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital chest images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference image (which corresponds to the subtraction of a signal-enhanced image and a signal-suppressed image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per chest image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one chest image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation.

Delayed contrast-enhanced MRI of the coronary artery wall in takayasu arteritis.

PubMed

Schneeweis, Christopher; Schnackenburg, Bernhard; Stuber, Matthias; Berger, Alexander; Schneider, Udo; Yu, Jing; Gebker, Rolf; Weiss, Robert G; Fleck, Eckart; Kelle, Sebastian

2012-01-01

Takayasu arteritis (TA) is a rare form of chronic inflammatory granulomatous arteritis of the aorta and its major branches. Late gadolinium enhancement (LGE) with magnetic resonance imaging (MRI) has demonstrated its value for the detection of vessel wall alterations in TA. The aim of this study was to assess LGE of the coronary artery wall in patients with TA compared to patients with stable CAD. We enrolled 9 patients (8 female, average age 46±13 years) with proven TA. In the CAD group 9 patients participated (8 male, average age 65±10 years). Studies were performed on a commercial 3T whole-body MR imaging system (Achieva; Philips, Best, The Netherlands) using a 3D inversion prepared navigator gated spoiled gradient-echo sequence, which was repeated 34-45 minutes after low-dose gadolinium administration. No coronary vessel wall enhancement was observed prior to contrast in either group. Post contrast, coronary LGE on IR scans was detected in 28 of 50 segments (56%) seen on T2-Prep scans in TA and in 25 of 57 segments (44%) in CAD patients. LGE quantitative assessment of coronary artery vessel wall CNR post contrast revealed no significant differences between the two groups (CNR in TA: 6.0±2.4 and 7.3±2.5 in CAD; p = 0.474). Our findings suggest that LGE of the coronary artery wall seems to be common in patients with TA and similarly pronounced as in CAD patients. The observed coronary LGE seems to be rather unspecific, and differentiation between coronary vessel wall fibrosis and inflammation still remains unclear.

Novel Therapies for Familial Hypercholesterolemia.

PubMed

Parizo, Justin; Sarraju, Ashish; Knowles, Joshua W

2016-11-01

Both HeFH and HoFH require dietary and lifestyle modification. Pharmacotherapy of adult HeFH patients is largely driven by the American Heart Association (AHA) algorithm. A high-potency statin is started initially with a goal low-density lipoprotein cholesterol (LDL-C) reduction of >50 %. The LDL-C target is adjusted to <100 or <70 mg/dL in subjects with coronary artery disease (CAD) with ezetimibe being second line. If necessary, a third adjunctive therapy, such as a PSCK9 inhibitor (not yet approved in children) or bile acid-binding resin, can be added. Finally, LDL-C apheresis can be considered in patients with LDL-C >300 mg/dL (or >200 mg/dL with significant CAD, although now approved for LDL-C as low as 160 mg/dL with CAD). Due to the early, severe LDL-C elevation in HoFH patients, concerning natural history, rarity of the condition, and nuances of treatment, all HoFH patients should be treated at a pediatric or adult center with HoFH experience. LDL-C apheresis should be considered as early as 5 years of age. However, apheresis availability and tolerability is limited and pharmacotherapy is required. Generally, the AHA algorithm with reference to the European Atherosclerosis Society Consensus Panel recommendations is reasonable with all patients initiated on high-dose, high-potency statin, ezetimibe, and bile acid-binding resins. In most, additional LDL-C lowering is required with PCSK9 inhibitors and/or lomitapide or mipomersen. Liver transplantation can also be considered at experienced centers as a last resort.

Automated detection of microaneurysms using robust blob descriptors

NASA Astrophysics Data System (ADS)

Adal, K.; Ali, S.; Sidibé, D.; Karnowski, T.; Chaum, E.; Mériaudeau, F.

2013-03-01

Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fundus images. Then, Hessian-based candidate selection algorithm is applied to extract image regions which are more likely to be MAs. For each candidate region, robust low-level blob descriptors such as Speeded Up Robust Features (SURF) and Intensity Normalized Radon Transform are extracted to characterize candidate MA regions. The combined features are then classified using SVM which has been trained using ten manually annotated training images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. Preliminary results show the competitiveness of the proposed candidate selection techniques against state-of-the art methods as well as the promising future for the proposed descriptors to be used in the localization of MAs from fundus images.

Reduction of bias and variance for evaluation of computer-aided diagnostic schemes.

PubMed

Li, Qiang; Doi, Kunio

2006-04-01

Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists in detecting various lesions in medical images. In addition to the development, an equally important problem is the reliable evaluation of the performance levels of various CAD schemes. It is good to see that more and more investigators are employing more reliable evaluation methods such as leave-one-out and cross validation, instead of less reliable methods such as resubstitution, for assessing their CAD schemes. However, the common applications of leave-one-out and cross-validation evaluation methods do not necessarily imply that the estimated performance levels are accurate and precise. Pitfalls often occur in the use of leave-one-out and cross-validation evaluation methods, and they lead to unreliable estimation of performance levels. In this study, we first identified a number of typical pitfalls for the evaluation of CAD schemes, and conducted a Monte Carlo simulation experiment for each of the pitfalls to demonstrate quantitatively the extent of bias and/or variance caused by the pitfall. Our experimental results indicate that considerable bias and variance may exist in the estimated performance levels of CAD schemes if one employs various flawed leave-one-out and cross-validation evaluation methods. In addition, for promoting and utilizing a high standard for reliable evaluation of CAD schemes, we attempt to make recommendations, whenever possible, for overcoming these pitfalls. We believe that, with the recommended evaluation methods, we can considerably reduce the bias and variance in the estimated performance levels of CAD schemes.

Research into the Architecture of CAD Based Robot Vision Systems

DTIC Science & Technology

1988-02-09

Vision 󈨚 and "Automatic Generation of Recognition Features for Com- puter Vision," Mudge, Turney and Volz, published in Robotica (1987). All of the...Occluded Parts," (T.N. Mudge, J.L. Turney, and R.A. Volz), Robotica , vol. 5, 1987, pp. 117-127. 5. "Vision Algorithms for Hypercube Machines," (T.N. Mudge

The effect of feature selection methods on computer-aided detection of masses in mammograms

NASA Astrophysics Data System (ADS)

Hupse, Rianne; Karssemeijer, Nico

2010-05-01

In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

Automated detection of pulmonary nodules in CT images with support vector machines

NASA Astrophysics Data System (ADS)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Spectral identification of a 90Sr source in the presence of masking nuclides using Maximum-Likelihood deconvolution

NASA Astrophysics Data System (ADS)

Neuer, Marcus J.

2013-11-01

A technique for the spectral identification of strontium-90 is shown, utilising a Maximum-Likelihood deconvolution. Different deconvolution approaches are discussed and summarised. Based on the intensity distribution of the beta emission and Geant4 simulations, a combined response matrix is derived, tailored to the β- detection process in sodium iodide detectors. It includes scattering effects and attenuation by applying a base material decomposition extracted from Geant4 simulations with a CAD model for a realistic detector system. Inversion results of measurements show the agreement between deconvolution and reconstruction. A detailed investigation with additional masking sources like 40K, 226Ra and 131I shows that a contamination of strontium can be found in the presence of these nuisance sources. Identification algorithms for strontium are presented based on the derived technique. For the implementation of blind identification, an exemplary masking ratio is calculated.

Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

NASA Astrophysics Data System (ADS)

Georgiou, Harris

2009-10-01

Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.

Quantitative Digital Tomosynthesis Mammography for Improved Breast Cancer Detection and Diagnosis

DTIC Science & Technology

2008-04-01

include breast-shape slabs consisted of breast- tissue-equivalent materials, i.e. heterogeneous mixture of fibroglandular-tissue- mimicking material. We...collected previ- ously in the Department of Radiology at the University of Michigan for our CAD study.46 The resulting mean effi- ciency ratio for 96 CC...may obscure the characteristics of mass margins. Development of CAD systems for DBT is still at an early stage. In this preliminary study, we compared

Software Performs Complex Design Analysis

NASA Technical Reports Server (NTRS)

2008-01-01

Designers use computational fluid dynamics (CFD) to gain greater understanding of the fluid flow phenomena involved in components being designed. They also use finite element analysis (FEA) as a tool to help gain greater understanding of the structural response of components to loads, stresses and strains, and the prediction of failure modes. Automated CFD and FEA engineering design has centered on shape optimization, which has been hindered by two major problems: 1) inadequate shape parameterization algorithms, and 2) inadequate algorithms for CFD and FEA grid modification. Working with software engineers at Stennis Space Center, a NASA commercial partner, Optimal Solutions Software LLC, was able to utilize its revolutionary, one-of-a-kind arbitrary shape deformation (ASD) capability-a major advancement in solving these two aforementioned problems-to optimize the shapes of complex pipe components that transport highly sensitive fluids. The ASD technology solves the problem of inadequate shape parameterization algorithms by allowing the CFD designers to freely create their own shape parameters, therefore eliminating the restriction of only being able to use the computer-aided design (CAD) parameters. The problem of inadequate algorithms for CFD grid modification is solved by the fact that the new software performs a smooth volumetric deformation. This eliminates the extremely costly process of having to remesh the grid for every shape change desired. The program can perform a design change in a markedly reduced amount of time, a process that would traditionally involve the designer returning to the CAD model to reshape and then remesh the shapes, something that has been known to take hours, days-even weeks or months-depending upon the size of the model.

Computer-aided detection of initial polyp candidates with level set-based adaptive convolution

NASA Astrophysics Data System (ADS)

Zhu, Hongbin; Duan, Chaijie; Liang, Zhengrong

2009-02-01

In order to eliminate or weaken the interference between different topological structures on the colon wall, adaptive and normalized convolution methods were used to compute the first and second order spatial derivatives of computed tomographic colonography images, which is the beginning of various geometric analyses. However, the performance of such methods greatly depends on the single-layer representation of the colon wall, which is called the starting layer (SL) in the following text. In this paper, we introduce a level set-based adaptive convolution (LSAC) method to compute the spatial derivatives, in which the level set method is employed to determine a more reasonable SL. The LSAC was applied to a computer-aided detection (CAD) scheme to detect the initial polyp candidates, and experiments showed that it benefits the CAD scheme in both the detection sensitivity and specificity as compared to our previous work.

Stain susceptibility of composite and ceramic CAD/CAM blocks versus direct resin composites with different resinous matrices.

PubMed

Alharbi, Amal; Ardu, Stefano; Bortolotto, Tissiana; Krejci, Ivo

2017-04-01

To evaluate the stain susceptibility of CAD/CAM blocks and direct composite after long term exposure to various staining agents. 40 disk-shaped samples were fabricated from each of nine materials; six CAD/CAM (Vitablocs Mark II, Paradigm MZ100, Experimental Vita Hybrid Ceramic, Vita Enamic, Experimental Kerr and Lava Ultimate) and three direct composites (Filtek Supreme, Venus Diamond and Filtek Silorane). Samples were randomly divided into five groups (n = 8) according to different staining solutions (distilled water, tea, red wine, coffee and artificial saliva). Initial L*a*b* values were assessed using a calibrated digital spectrophotometer. Specimens were immersed in staining solutions and stored in an incubator at 37 °C for 120 days. L*a*b* values were assessed again and color change (∆E) was calculated as difference between recorded L*a*b* values. ANOVA, and Duncan test were used to identify differences between groups (α = 0.05). Significant differences in ∆E values were detected between materials (p = 0.000). Among all staining solutions, the highest ∆E value was observed with red wine. The new CAD/CAM blocks (Vita Enamic, Vita Hybrid Ceramic and Lava Ultimate) showed the highest resistance to staining compared to the MZ100 composite resin blocks. Filtek Silorane, a direct composite, showed high stain resistance values compared to CAD/CAM materials and other direct composites. Ceramic and composite CAD/CAM blocks had lower staining susceptibility than methacrylate based direct composite. Staining susceptibility of the new resin based CAD/CAM materials Vita Enamic and Lava Ultimate was comparable to feldspathic ceramic blocks (Vitablocs Mark II). Filtek Silorane showed promising results that were comparable to some CAD/CAM blocks.

Computerized multiple image analysis on mammograms: performance improvement of nipple identification for registration of multiple views using texture convergence analyses

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Paramagul, Chintana

2004-05-01

Automated registration of multiple mammograms for CAD depends on accurate nipple identification. We developed two new image analysis techniques based on geometric and texture convergence analyses to improve the performance of our previously developed nipple identification method. A gradient-based algorithm is used to automatically track the breast boundary. The nipple search region along the boundary is then defined by geometric convergence analysis of the breast shape. Three nipple candidates are identified by detecting the changes along the gray level profiles inside and outside the boundary and the changes in the boundary direction. A texture orientation-field analysis method is developed to estimate the fourth nipple candidate based on the convergence of the tissue texture pattern towards the nipple. The final nipple location is determined from the four nipple candidates by a confidence analysis. Our training and test data sets consisted of 419 and 368 randomly selected mammograms, respectively. The nipple location identified on each image by an experienced radiologist was used as the ground truth. For 118 of the training and 70 of the test images, the radiologist could not positively identify the nipple, but provided an estimate of its location. These were referred to as invisible nipple images. In the training data set, 89.37% (269/301) of the visible nipples and 81.36% (96/118) of the invisible nipples could be detected within 1 cm of the truth. In the test data set, 92.28% (275/298) of the visible nipples and 67.14% (47/70) of the invisible nipples were identified within 1 cm of the truth. In comparison, our previous nipple identification method without using the two convergence analysis techniques detected 82.39% (248/301), 77.12% (91/118), 89.93% (268/298) and 54.29% (38/70) of the nipples within 1 cm of the truth for the visible and invisible nipples in the training and test sets, respectively. The results indicate that the nipple on mammograms can be detected accurately. This will be an important step towards automatic multiple image analysis for CAD techniques.

Computerized mass detection in whole breast ultrasound images: reduction of false positives using bilateral subtraction technique

NASA Astrophysics Data System (ADS)

Ikedo, Yuji; Fukuoka, Daisuke; Hara, Takeshi; Fujita, Hiroshi; Takada, Etsuo; Endo, Tokiko; Morita, Takako

2007-03-01

The comparison of left and right mammograms is a common technique used by radiologists for the detection and diagnosis of masses. In mammography, computer-aided detection (CAD) schemes using bilateral subtraction technique have been reported. However, in breast ultrasonography, there are no reports on CAD schemes using comparison of left and right breasts. In this study, we propose a scheme of false positive reduction based on bilateral subtraction technique in whole breast ultrasound images. Mass candidate regions are detected by using the information of edge directions. Bilateral breast images are registered with reference to the nipple positions and skin lines. A false positive region is detected based on a comparison of the average gray values of a mass candidate region and a region with the same position and same size as the candidate region in the contralateral breast. In evaluating the effectiveness of the false positive reduction method, three normal and three abnormal bilateral pairs of whole breast images were employed. These abnormal breasts included six masses larger than 5 mm in diameter. The sensitivity was 83% (5/6) with 13.8 (165/12) false positives per breast before applying the proposed reduction method. By applying the method, false positives were reduced to 4.5 (54/12) per breast without removing a true positive region. This preliminary study indicates that the bilateral subtraction technique is effective for improving the performance of a CAD scheme in whole breast ultrasound images.

Analysis of quaternary ammonium and phosphonium ionic liquids by reversed-phase high-performance liquid chromatography with charged aerosol detection and unified calibration.

PubMed

Stojanovic, Anja; Lämmerhofer, Michael; Kogelnig, Daniel; Schiesel, Simone; Sturm, Martin; Galanski, Markus; Krachler, Regina; Keppler, Bernhard K; Lindner, Wolfgang

2008-10-31

Several hydrophobic ionic liquids (ILs) based on long-chain aliphatic ammonium- and phosphonium cations and selected aromatic anions were analyzed by reversed-phase high-performance liquid chromatography (RP-HPLC) employing trifluoroacetic acid as ion-pairing additive to the acetonitrile-containing mobile phase and adopting a step-gradient elution mode. The coupling of charged aerosol detection (CAD) for the non-chromophoric aliphatic cations with diode array detection (DAD) for the aromatic anions allowed their simultaneous analysis in a set of new ILs derived from either tricaprylmethylammonium chloride (Aliquat 336) and trihexyltetradecylphosphonium chloride as precursors. Aliquat 336 is a mix of ammonium cations with distinct aliphatic chain lengths. In the course of the studies it turned out that CAD generates an identical detection response for all the distinct aliphatic cations. Due to lack of single component standards of the individual Aliquat 336 cation species, a unified calibration function was established for the quantitative analysis of the quaternary ammonium cations of the ILs. The developed method was validated according to ICH guidelines, which confirmed the validity of the unified calibration. The application of the method revealed molar ratios of cation to anion close to 1 indicating a quantitative exchange of the chloride ions of the precursors by the various aromatic anions in the course of the synthesis of new ILs. Anomalies of CAD observed for the detection of some aromatic anions (thiosalicylate and benzoate) are discussed.

3D sensor algorithms for spacecraft pose determination

NASA Astrophysics Data System (ADS)

Trenkle, John M.; Tchoryk, Peter, Jr.; Ritter, Greg A.; Pavlich, Jane C.; Hickerson, Aaron S.

2006-05-01

Researchers at the Michigan Aerospace Corporation have developed accurate and robust 3-D algorithms for pose determination (position and orientation) of satellites as part of an on-going effort supporting autonomous rendezvous, docking and space situational awareness activities. 3-D range data from a LAser Detection And Ranging (LADAR) sensor is the expected input; however, the approach is unique in that the algorithms are designed to be sensor independent. Parameterized inputs allow the algorithms to be readily adapted to any sensor of opportunity. The cornerstone of our approach is the ability to simulate realistic range data that may be tailored to the specifications of any sensor. We were able to modify an open-source raytracing package to produce point cloud information from which high-fidelity simulated range images are generated. The assumptions made in our experimentation are as follows: 1) we have access to a CAD model of the target including information about the surface scattering and reflection characteristics of the components; 2) the satellite of interest may appear at any 3-D attitude; 3) the target is not necessarily rigid, but does have a limited number of configurations; and, 4) the target is not obscured in any way and is the only object in the field of view of the sensor. Our pose estimation approach then involves rendering a large number of exemplars (100k to 5M), extracting 2-D (silhouette- and projection-based) and 3-D (surface-based) features, and then training ensembles of decision trees to predict: a) the 4-D regions on a unit hypersphere into which the unit quaternion that represents the vehicle [Q X, Q Y, Q Z, Q W] is pointing, and, b) the components of that unit quaternion. Results have been quite promising and the tools and simulation environment developed for this application may also be applied to non-cooperative spacecraft operations, Autonomous Hazard Detection and Avoidance (AHDA) for landing craft, terrain mapping, vehicle guidance, path planning and obstacle avoidance.

Genetic risk analysis of coronary artery disease in Pakistani subjects using a genetic risk score of 21 variants.

PubMed

Shahid, Saleem Ullah; Shabana; Cooper, Jackie A; Beaney, Katherine E; Li, Kawah; Rehman, Abdul; Humphries, Steve E

2017-03-01

Conventional coronary artery disease (CAD) risk factors like age, gender, blood lipids, hypertension and smoking have been the basis of CAD risk prediction algorithms, but provide only modest discrimination. Genetic risk score (GRS) may provide improved discrimination over and above conventional risk factors. Here we analyzed the genetic risk of CAD in subjects from Pakistan, using a GRS of 21 variants in 18 genes and examined whether the GRS is associated with blood lipid levels. 625 (405 cases and 220 controls) subjects were genotyped for variants, NOS3 rs1799983, SMAD3 rs17228212, APOB rs1042031, LPA rs3798220, LPA rs10455872, SORT1 rs646776, APOE rs429358, GLUL rs10911021, FTO rs9939609, MIA3 rs17465637, CDKN2Ars10757274, DAB2IP rs7025486, CXCL12 rs1746048, ACE rs4341, APOA5 rs662799, CETP rs708272, MRAS rs9818870, LPL rs328, LPL rs1801177, PCSK9 rs11591147 and APOE rs7412 by TaqMan and KASPar allele discrimination techniques. Individually, the single SNPs were not associated with CAD except APOB rs1042031 and FTO rs993969 (p = 0.01 and 0.009 respectively). However, the combined GRS of 21 SNPs was significantly higher in cases than controls (19.37 ± 2.56 vs. 18.47 ± 2.45, p = 2.9 × 10 -5 ), and compared to the bottom quintile, CAD risk in the top quintile of the GRS was 2.96 (95% CI 1.71-5.13). Atherogenic blood lipids showed significant positive association with GRS. The GRS was quantitatively associated with CAD risk and showed association with blood lipid levels, suggesting that the mechanism of these variants is likely to be, in part at least, through creating an atherogenic lipid profile in subjects carrying high numbers of risk alleles. Copyright © 2017 Elsevier B.V. All rights reserved.

Association of Rare and Common Variation in the Lipoprotein Lipase Gene with Coronary Artery Disease

PubMed Central

Khera, Amit V.; Won, Hong-Hee; Peloso, Gina M.; O’Dushlaine, Colm; Liu, Dajiang; Stitziel, Nathan O.; Natarajan, Pradeep; Nomura, Akihiro; Emdin, Connor A.; Gupta, Namrata; Borecki, Ingrid B.; Asselta, Rosanna; Duga, Stefano; Merlini, Piera Angelica; Correa, Adolfo; Kessler, Thorsten; Wilson, James G.; Bown, Matthew J.; Hall, Alistair S.; Braund, Peter S.; Carey, David J.; Murray, Michael F.; Kirchner, H. Lester; Leader, Joseph B.; Lavage, Daniel R.; Manus, J. Neil; Hartzel, Dustin N.; Samani, Nilesh J.; Schunkert, Heribert; Marrugat, Jaume; Elosua, Roberto; McPherson, Ruth; Farrall, Martin; Watkins, Hugh; Lander, Eric S.; Rader, Daniel J.; Danesh, John; Ardissino, Diego; Gabriel, Stacey; Willer, Cristen; Abecasis, Gonçalo R.; Saleheen, Danish; Dewey, Frederick E.; Kathiresan, Sekar

2017-01-01

Importance The activity of lipoprotein lipase (LPL) is the rate-determining step in clearing triglyceride-rich lipoproteins from the circulation. Mutations that damage the LPL gene lead to lifelong deficiency in enzymatic activity and can provide insight into the relationship of LPL to human disease. Objective Determine if rare and/or common variants in the LPL gene are associated with early-onset coronary artery disease (CAD). Design, Setting, and Participants Cross-sectional study. The LPL gene was sequenced in 10 CAD case-control cohorts of the multinational Myocardial Infarction Genetics Consortium and a nested CAD case-control cohort of the Geisinger Health System DiscovEHR cohort between 2010 and 2015. Common variants were genotyped in up to 305,699 individuals of the Global Lipids Genetics Consortium and up to 120,600 individuals of the CARDIoGRAM Exome Consortium between 2012 and 2014. Study-specific estimates were pooled via meta-analysis. Exposure Rare damaging mutations in LPL included loss-of-function variants and missense variants annotated as pathogenic in a human genetics database or predicted to be damaging by computer prediction algorithms trained to identify mutations that impair protein function. Common variants in the LPL gene region included those independently associated with circulating triglyceride levels. Main Outcomes and Measures Circulating lipid levels and CAD. Results Among 46,891 individuals with LPL gene sequencing data available, mean age was 50 years (SD 12.6) and 51% were female. 188 participants (0.40%; 95%CI 0.35–0.46) carried a damaging mutation in the LPL gene – 105 of 32,646 control participants (0.32%) and 83 of 14,245 (0.58%) early-onset CAD cases. Compared to 46,703 non-carriers, the 188 heterozygous carriers of a LPL damaging mutation displayed higher plasma triglycerides (Beta coefficient= +19.6 mg/dL; 95%CI 4.6–34.6) and higher odds of CAD (odds ratio 1.84; 95%CI 1.35–2.51; P<0.001). An analysis of 6 common LPL variants noted an odds ratio for CAD of 1.51 (95%CI 1.39–1.64; P=1.1×10−22) per standard deviation increase in triglycerides. Conclusions and Relevance The presence of rare damaging mutations in the LPL gene was significantly associated with higher triglyceride levels and presence of CAD. However, further research is needed to assess causal mechanisms by which heterozygous LPL deficiency could lead to CAD. PMID:28267856

The Handbook of Medical Image Perception and Techniques

NASA Astrophysics Data System (ADS)

Samei, Ehsan; Krupinski, Elizabeth

2014-07-01

1. Medical image perception Ehsan Samei and Elizabeth Krupinski; Part I. Historical Reflections and Theoretical Foundations: 2. A short history of image perception in medical radiology Harold Kundel and Calvin Nodine; 3. Spatial vision research without noise Arthur Burgess; 4. Signal detection theory, a brief history Arthur Burgess; 5. Signal detection in radiology Arthur Burgess; 6. Lessons from dinners with the giants of modern image science Robert Wagner; Part II. Science of Image Perception: 7. Perceptual factors in reading medical images Elizabeth Krupinski; 8. Cognitive factors in reading medical images David Manning; 9. Satisfaction of search in traditional radiographic imaging Kevin Berbaum, Edmund Franken, Robert Caldwell and Kevin Schartz; 10. The role of expertise in radiologic image interpretation Calvin Nodine and Claudia Mello-Thoms; 11. A primer of image quality and its perceptual relevance Robert Saunders and Ehsan Samei; 12. Beyond the limitations of human vision Maria Petrou; Part III. Perception Metrology: 13. Logistical issues in designing perception experiments Ehsan Samei and Xiang Li; 14. ROC analysis: basic concepts and practical applications Georgia Tourassi; 15. Multi-reader ROC Steve Hillis; 16. Recent developments in FROC methodology Dev Chakraborty; 17. Observer models as a surrogate to perception experiments Craig Abbey and Miguel Eckstein; 18. Implementation of observer models Matthew Kupinski; Part IV. Decision Support and Computer Aided Detection: 19. CAD: an image perception perspective Maryellen Giger and Weijie Chen; 20. Common designs of CAD studies Yulei Jiang; 21. Perceptual effect of CAD in reading chest images Matthew Freedman and Teresa Osicka; 22. Perceptual issues in mammography and CAD Michael Ulissey; 23. How perceptual factors affect the use and accuracy of CAD for interpretation of CT images Ronald Summers; 24. CAD: risks and benefits for radiologists' decisions Eugenio Alberdi, Andrey Povyakalo, Lorenzo Strigini and Peter Ayton; Part V. Optimization and Practical Issues: 25. Optimization of 2D and 3D radiographic systems Jeff Siewerdson; 26. Applications of AFC methodology in optimization of CT imaging systems Kent Ogden and Walter Huda; 27. Perceptual issues in reading mammograms Margarita Zuley; 28. Perceptual optimization of display processing techniques Richard Van Metter; 29. Optimization of display systems Elizabeth Krupinski and Hans Roehrig; 30. Ergonomic radiologist workplaces in the PACS environment Carl Zylack; Part VI. Epilogue: 31. Future prospects of medical image perception Ehsan Samei and Elizabeth Krupinski; Index.

The Handbook of Medical Image Perception and Techniques

NASA Astrophysics Data System (ADS)

Samei, Ehsan; Krupinski, Elizabeth

2009-12-01

1. Medical image perception Ehsan Samei and Elizabeth Krupinski; Part I. Historical Reflections and Theoretical Foundations: 2. A short history of image perception in medical radiology Harold Kundel and Calvin Nodine; 3. Spatial vision research without noise Arthur Burgess; 4. Signal detection theory, a brief history Arthur Burgess; 5. Signal detection in radiology Arthur Burgess; 6. Lessons from dinners with the giants of modern image science Robert Wagner; Part II. Science of Image Perception: 7. Perceptual factors in reading medical images Elizabeth Krupinski; 8. Cognitive factors in reading medical images David Manning; 9. Satisfaction of search in traditional radiographic imaging Kevin Berbaum, Edmund Franken, Robert Caldwell and Kevin Schartz; 10. The role of expertise in radiologic image interpretation Calvin Nodine and Claudia Mello-Thoms; 11. A primer of image quality and its perceptual relevance Robert Saunders and Ehsan Samei; 12. Beyond the limitations of human vision Maria Petrou; Part III. Perception Metrology: 13. Logistical issues in designing perception experiments Ehsan Samei and Xiang Li; 14. ROC analysis: basic concepts and practical applications Georgia Tourassi; 15. Multi-reader ROC Steve Hillis; 16. Recent developments in FROC methodology Dev Chakraborty; 17. Observer models as a surrogate to perception experiments Craig Abbey and Miguel Eckstein; 18. Implementation of observer models Matthew Kupinski; Part IV. Decision Support and Computer Aided Detection: 19. CAD: an image perception perspective Maryellen Giger and Weijie Chen; 20. Common designs of CAD studies Yulei Jiang; 21. Perceptual effect of CAD in reading chest images Matthew Freedman and Teresa Osicka; 22. Perceptual issues in mammography and CAD Michael Ulissey; 23. How perceptual factors affect the use and accuracy of CAD for interpretation of CT images Ronald Summers; 24. CAD: risks and benefits for radiologists' decisions Eugenio Alberdi, Andrey Povyakalo, Lorenzo Strigini and Peter Ayton; Part V. Optimization and Practical Issues: 25. Optimization of 2D and 3D radiographic systems Jeff Siewerdson; 26. Applications of AFC methodology in optimization of CT imaging systems Kent Ogden and Walter Huda; 27. Perceptual issues in reading mammograms Margarita Zuley; 28. Perceptual optimization of display processing techniques Richard Van Metter; 29. Optimization of display systems Elizabeth Krupinski and Hans Roehrig; 30. Ergonomic radiologist workplaces in the PACS environment Carl Zylack; Part VI. Epilogue: 31. Future prospects of medical image perception Ehsan Samei and Elizabeth Krupinski; Index.

Initial versus final fracture of metal-free crowns, analyzed via acoustic emission.

PubMed

Ereifej, Nadia; Silikas, Nick; Watts, David C

2008-09-01

To discriminate between initial and final fracture failure loads of four metal-free crown systems by the conjoint detection of acoustic emission signals during compressive loading. Teeth were prepared and used for crown construction with four crown systems; Vita Mark II (VM II) (Vita Zahnfabrik), IPS e.max Ceram/CAD (CAD) (Ivoclar-Vivadent), IPS e.max Ceram/ZirCAD (ZirCAD) (Ivoclar-Vivadent) and BelleGlass/EverStick (BGES) (Kerr/Stick Tech Ltd.). All samples were loaded in compression via a Co/Cr maxillary first molar tooth at 0.2mm/min and released acoustic signals were collected and analyzed. A minimum number of 15 crowns per group were loaded to final failure and values of loading at initial and final fracture were compared. Additional four samples per group were loaded till fracture initiation and were fractographically examined under the optical microscope. A lower threshold of 50dB was selected to exclude spurious background signals. Initial fracture forces were significantly lower than those of final fracture (p<0.05) in all groups and initial failure AE amplitudes were lower than those of final fracture. Mean initial fracture force of ZirCAD samples (1029.1N) was higher than those of VMII (744.4N), CAD (808.8 N) and BGES (979.7 N). Final fracture of ZirCAD also occurred at significantly higher force values (2091.7 N) than the rest of the groups; VMII (1120.9 N), CAD (1468.9 N) and BGES (1576.6 N). Significantly higher values of initial failure AE amplitude were found in VMII than CAD and BGES while those of final fracture were similar. All crowns observed under the microscope at initial fracture had signs of failure. Whereas the metal-free crowns examined showed significant variations in final failure loads, acoustic emission data showed that they all manifested initial failures at significantly lower load values.

Calcium Dobesilate Inhibits the Alterations in Tight Junction Proteins and Leukocyte Adhesion to Retinal Endothelial Cells Induced by Diabetes

PubMed Central

Leal, Ermelindo C.; Martins, João; Voabil, Paula; Liberal, Joana; Chiavaroli, Carlo; Bauer, Jacques; Cunha-Vaz, José; Ambrósio, António F.

2010-01-01

OBJECTIVE Calcium dobesilate (CaD) has been used in the treatment of diabetic retinopathy in the last decades, but its mechanisms of action are not elucidated. CaD is able to correct the excessive vascular permeability in the retina of diabetic patients and in experimental diabetes. We investigated the molecular and cellular mechanisms underlying the protective effects of CaD against the increase in blood–retinal barrier (BRB) permeability induced by diabetes. RESEARCH DESIGN AND METHODS Wistar rats were divided into three groups: controls, streptozotocin-induced diabetic rats, and diabetic rats treated with CaD. The BRB breakdown was evaluated using Evans blue. The content or distribution of tight junction proteins (occludin, claudin-5, and zonula occluden-1 [ZO-1]), intercellular adhesion molecule-1 (ICAM-1), and p38 mitogen-activated protein kinase (p38 MAPK) was evaluated by Western blotting and immunohistochemistry. Leukocyte adhesion was evaluated in retinal vessels and in vitro. Oxidative stress was evaluated by the detection of oxidized carbonyls and tyrosine nitration. NF-κB activation was measured by enzyme-linked immunosorbent assay. RESULTS Diabetes increased the BRB permeability and retinal thickness. Diabetes also decreased occludin and claudin-5 levels and altered the distribution of ZO-1 and occludin in retinal vessels. These changes were inhibited by CaD treatment. CaD also inhibited the increase in leukocyte adhesion to retinal vessels or endothelial cells and in ICAM-1 levels, induced by diabetes or elevated glucose. Moreover, CaD decreased oxidative stress and p38 MAPK and NF-κB activation caused by diabetes. CONCLUSIONS CaD prevents the BRB breakdown induced by diabetes, by restoring tight junction protein levels and organization and decreasing leukocyte adhesion to retinal vessels. The protective effects of CaD are likely to involve the inhibition of p38 MAPK and NF-κB activation, possibly through the inhibition of oxidative/nitrosative stress. PMID:20627932

PON1 L55M and Q192R gene polymorphisms and CAD risks in patients with hyperlipidemia : Clinical study of possible associations.

PubMed

Chen, H; Ding, S; Zhou, M; Wu, X; Liu, X; Liu, J; Wu, Y; Liu, D

2017-08-23

A decreased plasma high density lipoprotein (HDL) cholesterol level is a strong risk factor for coronary artery disease (CAD). Antioxidant activity of HDL mainly lies in the activity of paraoxonase (PON). This study aimed to investigate the relationships between PON1 L55M and Q192R polymorphisms, and the risks of CAD in patients with hyperlipidemia. From January 2014 to January 2016, 244 patients were divided into hyperlipidemia, hyperlipidemia + CAD, and control groups. The hyperlipidemia and hyperlipidemia + CAD groups were designated as the case group. Serum PON1 concentrations were measured using the enzyme-linked immunosorbent assay. After isolating genomic DNA, the PON1 L55M and Q192R genes were amplified by polymerase chain reaction and sequenced. In the case group, the genotypes LM and LL were detected significantly more often than in the control group, as were the alleles R (33.33%, 42.12%) and L (22.78%, 29.11%). The frequency of QR and RR genotypes was significantly higher in the hyperlipidemia + CAD group than in the hyperlipidemia group; the allele R in the hyperlipidemia + CAD group (42.77%) was more frequent than in the hyperlipidemia group (23.78%). The Q192R polymorphism was associated with low serum PON1 concentrations, and the lowest concentration was observed in the 192QR + 192RR genotype (P = 0.03). Logistic regression analysis showed a significant correlation between the 192R allele and smoking (P = 0.03), body mass index (P = 0.02), systolic blood pressure (P = 0.004), total cholesterol (P = 0.03), triglycerides (P = 0.01), HDL (P = 0.004), and low density lipoprotein (P = 0.02). The PON1 alleles 192R and 55L are associated with CAD, and the Q192R polymorphism may be a risk factor for CAD.

Coronary revascularization vs. medical therapy following coronary-computed tomographic angiography in patients with low-, intermediate- and high-risk coronary artery disease: results from the CONFIRM long-term registry

PubMed Central

Schulman-Marcus, Joshua; Lin, Fay Y.; Gransar, Heidi; Berman, Daniel; Callister, Tracy; DeLago, Augustin; Hadamitzky, Martin; Hausleiter, Joerg; Al-Mallah, Mouaz; Budoff, Matthew; Kaufmann, Philipp; Achenbach, Stephan; Raff, Gilbert; Chinnaiyan, Kavitha; Cademartiri, Filippo; Maffei, Erica; Villines, Todd; Kim, Yong-Jin; Leipsic, Jonathon; Feuchtner, Gudrun; Rubinshtein, Ronen; Pontone, Gianluca; Andreini, Daniele; Marques, Hugo; Chang, Hyuk-Jae; Chow, Benjamin J.W.; Cury, Ricardo C.; Dunning, Allison; Shaw, Leslee; Min, James K.

2017-01-01

Abstract Aims To identify the effect of early revascularization on 5-year survival in patients with CAD diagnosed by coronary-computed tomographic angiography (CCTA). Methods and results We examined 5544 stable patients with suspected CAD undergoing CCTA who were followed a median of 5.5 years in a large international registry. Patients were categorized as having low-, intermediate-, or high-risk CAD based on CCTA findings. Two treatment groups were defined: early revascularization within 90 days of CCTA (n = 1171) and medical therapy (n = 4373). To account for the non-randomized referral to revascularization, we developed a propensity score by logistic regression. This score was incorporated into Cox proportional hazard models to calculate the effect of revascularization on all-cause mortality. Death occurred in 363 (6.6%) patients and was more frequent in medical therapy. In multivariable models, when compared with medical therapy, the mortality benefit of revascularization varied significantly over time and by CAD risk (P for interaction 0.04). In high-risk CAD, revascularization was significantly associated with lower mortality at 1 year (hazard ratio [HR] 0.22, 95% confidence interval [CI] 0.11–0.47) and 5 years (HR 0.31, 95% CI 0.18–0.54). For intermediate-risk CAD, revascularization was associated with reduced mortality at 1 year (HR 0.45, 95% CI 0.22–0.93) but not 5 years (HR 0.63, 95% CI 0.33–1.20). For low-risk CAD, there was no survival benefit at either time point. Conclusions Early revascularization was associated with reduced 1-year mortality in intermediate- and high-risk CAD detected by CCTA, but this association only persisted for 5-year mortality in high-risk CAD. PMID:28329294

An intercomparison of five ammonia measurement techniques

NASA Technical Reports Server (NTRS)

Williams, E. J.; Sandholm, S. T.; Bradshaw, J. D.; Schendel, J. S.; Langford, A. O.; Quinn, P. K.; Lebel, P. J.; Vay, S. A.; Roberts, P. D.; Norton, R. B.

1992-01-01

Results obtained from five techniques for measuring gas-phase ammonia at low concentration in the atmosphere are compared. These methods are: (1) a photofragmentation/laser-induced fluorescence (PF/LIF) instrument; (2) a molybdenum oxide annular denuder sampling/chemiluminescence detection technique; (3) a tungsten oxide denuder sampling/chemiluminescence detection system; (4) a citric-acid-coated denuder sampling/ion chromatographic analysis (CAD/IC) method; and (5) an oxalic-acid-coated filter pack sampling/colorimetric analysis method. It was found that two of the techniques, the PF/LIF and the CAD/IC methods, measured approximately 90 percent of the calculated ammonia added in the spiking tests and agreed very well with each other in the ambient measurements.

Rationale and design of the dual-energy computed tomography for ischemia determination compared to "gold standard" non-invasive and invasive techniques (DECIDE-Gold): A multicenter international efficacy diagnostic study of rest-stress dual-energy computed tomography angiography with perfusion.

PubMed

Truong, Quynh A; Knaapen, Paul; Pontone, Gianluca; Andreini, Daniele; Leipsic, Jonathon; Carrascosa, Patricia; Lu, Bin; Branch, Kelley; Raman, Subha; Bloom, Stephen; Min, James K

2015-10-01

Dual-energy CT (DECT) has potential to improve myocardial perfusion for physiologic assessment of coronary artery disease (CAD). Diagnostic performance of rest-stress DECT perfusion (DECTP) is unknown. DECIDE-Gold is a prospective multicenter study to evaluate the accuracy of DECT to detect hemodynamic (HD) significant CAD, as compared to fractional flow reserve (FFR) as a reference standard. Eligible participants are subjects with symptoms of CAD referred for invasive coronary angiography (ICA). Participants will undergo DECTP, which will be performed by pharmacological stress, and participants will subsequently proceed to ICA and FFR. HD-significant CAD will be defined as FFR ≤ 0.80. In those undergoing myocardial stress imaging (MPI) by positron emission tomography (PET), single photon emission computed tomography (SPECT) or cardiac magnetic resonance (CMR) imaging, ischemia will be graded by % ischemic myocardium. Blinded core laboratory interpretation will be performed for CCTA, DECTP, MPI, ICA, and FFR. Primary endpoint is accuracy of DECTP to detect ≥1 HD-significant stenosis at the subject level when compared to FFR. Secondary and tertiary endpoints are accuracies of combinations of DECTP at the subject and vessel levels compared to FFR and MPI. DECIDE-Gold will determine the performance of DECTP for diagnosing ischemia.

Myocardial perfusion magnetic resonance imaging using sliding-window conjugate-gradient highly constrained back-projection reconstruction for detection of coronary artery disease.

PubMed

Ma, Heng; Yang, Jun; Liu, Jing; Ge, Lan; An, Jing; Tang, Qing; Li, Han; Zhang, Yu; Chen, David; Wang, Yong; Liu, Jiabin; Liang, Zhigang; Lin, Kai; Jin, Lixin; Bi, Xiaoming; Li, Kuncheng; Li, Debiao

2012-04-15

Myocardial perfusion magnetic resonance imaging (MRI) with sliding-window conjugate-gradient highly constrained back-projection reconstruction (SW-CG-HYPR) allows whole left ventricular coverage, improved temporal and spatial resolution and signal/noise ratio, and reduced cardiac motion-related image artifacts. The accuracy of this technique for detecting coronary artery disease (CAD) has not been determined in a large number of patients. We prospectively evaluated the diagnostic performance of myocardial perfusion MRI with SW-CG-HYPR in patients with suspected CAD. A total of 50 consecutive patients who were scheduled for coronary angiography with suspected CAD underwent myocardial perfusion MRI with SW-CG-HYPR at 3.0 T. The perfusion defects were interpreted qualitatively by 2 blinded observers and were correlated with x-ray angiographic stenoses ≥50%. The prevalence of CAD was 56%. In the per-patient analysis, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of SW-CG-HYPR was 96% (95% confidence interval 82% to 100%), 82% (95% confidence interval 60% to 95%), 87% (95% confidence interval 70% to 96%), 95% (95% confidence interval 74% to100%), and 90% (95% confidence interval 82% to 98%), respectively. In the per-vessel analysis, the corresponding values were 98% (95% confidence interval 91% to 100%), 89% (95% confidence interval 80% to 94%), 86% (95% confidence interval 76% to 93%), 99% (95% confidence interval 93% to 100%), and 93% (95% confidence interval 89% to 97%), respectively. In conclusion, myocardial perfusion MRI using SW-CG-HYPR allows whole left ventricular coverage and high resolution and has high diagnostic accuracy in patients with suspected CAD. Copyright © 2012 Elsevier Inc. All rights reserved.

Understanding wax screen-printing: a novel patterning process for microfluidic cloth-based analytical devices.

PubMed

Liu, Min; Zhang, Chunsun; Liu, Feifei

2015-09-03

In this work, we first introduce the fabrication of microfluidic cloth-based analytical devices (μCADs) using a wax screen-printing approach that is suitable for simple, inexpensive, rapid, low-energy-consumption and high-throughput preparation of cloth-based analytical devices. We have carried out a detailed study on the wax screen-printing of μCADs and have obtained some interesting results. Firstly, an analytical model is established for the spreading of molten wax in cloth. Secondly, a new wax screen-printing process has been proposed for fabricating μCADs, where the melting of wax into the cloth is much faster (∼5 s) and the heating temperature is much lower (75 °C). Thirdly, the experimental results show that the patterning effects of the proposed wax screen-printing method depend to a certain extent on types of screens, wax melting temperatures and melting time. Under optimized conditions, the minimum printing width of hydrophobic wax barrier and hydrophilic channel is 100 μm and 1.9 mm, respectively. Importantly, the developed analytical model is also well validated by these experiments. Fourthly, the μCADs fabricated by the presented wax screen-printing method are used to perform a proof-of-concept assay of glucose or protein in artificial urine with rapid high-throughput detection taking place on a 48-chamber cloth-based device and being performed by a visual readout. Overall, the developed cloth-based wax screen-printing and arrayed μCADs should provide a new research direction in the development of advanced sensor arrays for detection of a series of analytes relevant to many diverse applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Delayed Contrast-Enhanced MRI of the Coronary Artery Wall in Takayasu Arteritis

PubMed Central

Schneeweis, Christopher; Schnackenburg, Bernhard; Stuber, Matthias; Berger, Alexander; Schneider, Udo; Yu, Jing; Gebker, Rolf; Weiss, Robert G.; Fleck, Eckart; Kelle, Sebastian

2012-01-01

Background Takayasu arteritis (TA) is a rare form of chronic inflammatory granulomatous arteritis of the aorta and its major branches. Late gadolinium enhancement (LGE) with magnetic resonance imaging (MRI) has demonstrated its value for the detection of vessel wall alterations in TA. The aim of this study was to assess LGE of the coronary artery wall in patients with TA compared to patients with stable CAD. Methods We enrolled 9 patients (8 female, average age 46±13 years) with proven TA. In the CAD group 9 patients participated (8 male, average age 65±10 years). Studies were performed on a commercial 3T whole-body MR imaging system (Achieva; Philips, Best, The Netherlands) using a 3D inversion prepared navigator gated spoiled gradient-echo sequence, which was repeated 34–45 minutes after low-dose gadolinium administration. Results No coronary vessel wall enhancement was observed prior to contrast in either group. Post contrast, coronary LGE on IR scans was detected in 28 of 50 segments (56%) seen on T2-Prep scans in TA and in 25 of 57 segments (44%) in CAD patients. LGE quantitative assessment of coronary artery vessel wall CNR post contrast revealed no significant differences between the two groups (CNR in TA: 6.0±2.4 and 7.3±2.5 in CAD; p = 0.474). Conclusion Our findings suggest that LGE of the coronary artery wall seems to be common in patients with TA and similarly pronounced as in CAD patients. The observed coronary LGE seems to be rather unspecific, and differentiation between coronary vessel wall fibrosis and inflammation still remains unclear. PMID:23236382

Noninvasive detection of increased carotid artery temperature in patients with coronary artery disease predicts major cardiovascular events at one year: Results from a prospective multicenter study.

PubMed

Toutouzas, Konstantinos; Benetos, Georgios; Koutagiar, Iosif; Barampoutis, Nikolaos; Mitropoulou, Fotini; Davlouros, Periklis; Sfikakis, Petros P; Alexopoulos, Dimitrios; Stefanadis, Christodoulos; Siores, Elias; Tousoulis, Dimitris

2017-07-01

Limited prospective data have been reported regarding the impact of carotid inflammation on cardiovascular events in patients with coronary artery disease (CAD). Microwave radiometry (MWR) is a noninvasive, simple method that has been used for evaluation of carotid artery temperature which, when increased, predicts 'inflamed' plaques with vulnerable characteristics. We prospectively tested the hypothesis that increased carotid artery temperature predicts future cerebro- and cardiovascular events in patients with CAD. Consecutive patients from 3 centers, with documented CAD by coronary angiography, were studied. In both carotid arteries, common carotid intima-media thickness and plaque thickness were evaluated by ultrasound. Temperature difference (ΔT), measured by MWR, was considered as the maximal temperature along the carotid artery minus the minimum; ΔT ≥0.90 °C was assigned as high. Major cardiovascular events (MACE, death, stroke, myocardial infarction or revascularization) were recorded during the following year. In total, 250 patients were studied; of them 40 patients (16%) had high ΔT values in both carotid arteries. MACEs occurred in 30% of patients having bilateral high ΔT versus 3.8% in the remaining patients (p<0.001). Bilateral high ΔT was independently associated with increased one-year MACE rate (HR = 6.32, 95% CI 2.42-16.53, p<0.001, by multivariate cox regression hazard model). The addition of ΔT information on a baseline model based on cardiovascular risk factors and extent of CAD significantly increased the prognostic value of the model (c-statistic increase 0.744 to 0.845, p dif  = 0.05) CONCLUSIONS: Carotid inflammation, detected by MWR, has an incremental prognostic value in patients with documented CAD. Copyright © 2017 Elsevier B.V. All rights reserved.

Computer aided diagnosis of diabetic peripheral neuropathy

NASA Astrophysics Data System (ADS)

Chekh, Viktor; Soliz, Peter; McGrew, Elizabeth; Barriga, Simon; Burge, Mark; Luan, Shuang

2014-03-01

Diabetic peripheral neuropathy (DPN) refers to the nerve damage that can occur in diabetes patients. It most often affects the extremities, such as the feet, and can lead to peripheral vascular disease, deformity, infection, ulceration, and even amputation. The key to managing diabetic foot is prevention and early detection. Unfortunately, current existing diagnostic techniques are mostly based on patient sensations and exhibit significant inter- and intra-observer differences. We have developed a computer aided diagnostic (CAD) system for diabetic peripheral neuropathy. The thermal response of the feet of diabetic patients following cold stimulus is captured using an infrared camera. The plantar foot in the images from a thermal video are segmented and registered for tracking points or specific regions. The temperature recovery of each point on the plantar foot is extracted using our bio-thermal model and analyzed. The regions that exhibit abnormal ability to recover are automatically identified to aid the physicians to recognize problematic areas. The key to our CAD system is the segmentation of infrared video. The main challenges for segmenting infrared video compared to normal digital video are (1) as the foot warms up, it also warms up the surrounding, creating an ever changing contrast; and (2) there may be significant motion during imaging. To overcome this, a hybrid segmentation algorithm was developed based on a number of techniques such as continuous max-flow, model based segmentation, shape preservation, convex hull, and temperature normalization. Verifications of the automatic segmentation and registration using manual segmentation and markers show good agreement.

Hough transform for clustered microcalcifications detection in full-field digital mammograms

NASA Astrophysics Data System (ADS)

Fanizzi, A.; Basile, T. M. A.; Losurdo, L.; Amoroso, N.; Bellotti, R.; Bottigli, U.; Dentamaro, R.; Didonna, V.; Fausto, A.; Massafra, R.; Moschetta, M.; Tamborra, P.; Tangaro, S.; La Forgia, D.

2017-09-01

Many screening programs use mammography as principal diagnostic tool for detecting breast cancer at a very early stage. Despite the efficacy of the mammograms in highlighting breast diseases, the detection of some lesions is still doubtless for radiologists. In particular, the extremely minute and elongated salt-like particles of microcalcifications are sometimes no larger than 0.1 mm and represent approximately half of all cancer detected by means of mammograms. Hence the need for automatic tools able to support radiologists in their work. Here, we propose a computer assisted diagnostic tool to support radiologists in identifying microcalcifications in full (native) digital mammographic images. The proposed CAD system consists of a pre-processing step, that improves contrast and reduces noise by applying Sobel edge detection algorithm and Gaussian filter, followed by a microcalcification detection step performed by exploiting the circular Hough transform. The procedure performance was tested on 200 images coming from the Breast Cancer Digital Repository (BCDR), a publicly available database. The automatically detected clusters of microcalcifications were evaluated by skilled radiologists which asses the validity of the correctly identified regions of interest as well as the system error in case of missed clustered microcalcifications. The system performance was evaluated in terms of Sensitivity and False Positives per images (FPi) rate resulting comparable to the state-of-art approaches. The proposed model was able to accurately predict the microcalcification clusters obtaining performances (sensibility = 91.78% and FPi rate = 3.99) which favorably compare to other state-of-the-art approaches.

Automated CD-SEM recipe creation technology for mass production using CAD data

NASA Astrophysics Data System (ADS)

Kawahara, Toshikazu; Yoshida, Masamichi; Tanaka, Masashi; Ido, Sanyu; Nakano, Hiroyuki; Adachi, Naokaka; Abe, Yuichi; Nagatomo, Wataru

2011-03-01

Critical Dimension Scanning Electron Microscope (CD-SEM) recipe creation needs sample preparation necessary for matching pattern registration, and recipe creation on CD-SEM using the sample, which hinders the reduction in test production cost and time in semiconductor manufacturing factories. From the perspective of cost reduction and improvement of the test production efficiency, automated CD-SEM recipe creation without the sample preparation and the manual operation has been important in the production lines. For the automated CD-SEM recipe creation, we have introduced RecipeDirector (RD) that enables the recipe creation by using Computer-Aided Design (CAD) data and text data that includes measurement information. We have developed a system that automatically creates the CAD data and the text data necessary for the recipe creation on RD; and, for the elimination of the manual operation, we have enhanced RD so that all measurement information can be specified in the text data. As a result, we have established an automated CD-SEM recipe creation system without the sample preparation and the manual operation. For the introduction of the CD-SEM recipe creation system using RD to the production lines, the accuracy of the pattern matching was an issue. The shape of design templates for the matching created from the CAD data was different from that of SEM images in vision. Thus, a development of robust pattern matching algorithm that considers the shape difference was needed. The addition of image processing of the templates for the matching and shape processing of the CAD patterns in the lower layer has enabled the robust pattern matching. This paper describes the automated CD-SEM recipe creation technology for the production lines without the sample preparation and the manual operation using RD applied in Sony Semiconductor Kyusyu Corporation Kumamoto Technology Center (SCK Corporation Kumamoto TEC).

Efficient Computational Prototyping of Mixed Technology Microfluidic Components and Systems

DTIC Science & Technology

2002-08-01

AFRL-IF-RS-TR-2002-190 Final Technical Report August 2002 EFFICIENT COMPUTATIONAL PROTOTYPING OF MIXED TECHNOLOGY MICROFLUIDIC...SUBTITLE EFFICIENT COMPUTATIONAL PROTOTYPING OF MIXED TECHNOLOGY MICROFLUIDIC COMPONENTS AND SYSTEMS 6. AUTHOR(S) Narayan R. Aluru, Jacob White...Aided Design (CAD) tools for microfluidic components and systems were developed in this effort. Innovative numerical methods and algorithms for mixed

Modeling, Analysis, and Optimization Issues for Large Space Structures.

DTIC Science & Technology

1983-02-01

There are numerous opportunities - provided by new advances in computer hardware, firmware, software , CAD/CAM systems, computational algorithms and...Institute Department of Mechanical Engineering Dept. of Civil Engineering & Mechanics Troy, NY 12181 Drexel University Philadelphia, PA 19104 Dr...Mechanical Engineering Hampton, VA 23665 Washington, DC 20059 Dr. K. T. Alfriend Mr. Siva S. Banda Department of the Navy Flight Dynamics LaboratoryNaval

Fifth SIAM conference on geometric design 97: Final program and abstracts. Final technical report

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-12-31

The meeting was divided into the following sessions: (1) CAD/CAM; (2) Curve/Surface Design; (3) Geometric Algorithms; (4) Multiresolution Methods; (5) Robotics; (6) Solid Modeling; and (7) Visualization. This report contains the abstracts of papers presented at the meeting. Proceding the conference there was a short course entitled ``Wavelets for Geometric Modeling and Computer Graphics``.

Optimizing decision making at the end of life of a product

NASA Astrophysics Data System (ADS)

Gonzalez-Torre, Beatriz; Adenso-Diaz, Belarmino

2004-02-01

European environmental legislation has significantly evolved over the last few years, forcing manufacturers to be more environmentally aware and to introduce ecological criteria in their traditional practices. One of the most important goals of this set of regulations is to reduce the amount of solid waste generated per unit of time by promoting recycling, repair, reuse and other recovery strategies at the product end of life (EOL). However, one of the most difficult steps for manufacturers is that of deciding which of these options or which combination of them should be implemented to get the maximum recovery value taking into account the specific characteristics of each product. In this paper, a recurrent algorithm is proposed to determine the optimal end-of-life strategy. On the basis of the product bill of materials and its graphical CAD/CAM representation, the model will determine to what extent the product should be disassembled and what the final end of each disassembled part should be (reuse, recycling or disposal). The paper starts by presenting an overview of the model, to then focus on the CAD-integrated algorithm for determining the optimum disassembly sequence, a necessary step in EOL decision-making.

Performance evaluation of an automatic segmentation method of cerebral arteries in MRA images by use of a large image database

NASA Astrophysics Data System (ADS)

Uchiyama, Yoshikazu; Asano, Tatsunori; Hara, Takeshi; Fujita, Hiroshi; Kinosada, Yasutomi; Asano, Takahiko; Kato, Hiroki; Kanematsu, Masayuki; Hoshi, Hiroaki; Iwama, Toru

2009-02-01

The detection of cerebrovascular diseases such as unruptured aneurysm, stenosis, and occlusion is a major application of magnetic resonance angiography (MRA). However, their accurate detection is often difficult for radiologists. Therefore, several computer-aided diagnosis (CAD) schemes have been developed in order to assist radiologists with image interpretation. The purpose of this study was to develop a computerized method for segmenting cerebral arteries, which is an essential component of CAD schemes. For the segmentation of vessel regions, we first used a gray level transformation to calibrate voxel values. To adjust for variations in the positioning of patients, registration was subsequently employed to maximize the overlapping of the vessel regions in the target image and reference image. The vessel regions were then segmented from the background using gray-level thresholding and region growing techniques. Finally, rule-based schemes with features such as size, shape, and anatomical location were employed to distinguish between vessel regions and false positives. Our method was applied to 854 clinical cases obtained from two different hospitals. The segmentation of cerebral arteries in 97.1%(829/854) of the MRA studies was attained as an acceptable result. Therefore, our computerized method would be useful in CAD schemes for the detection of cerebrovascular diseases in MRA images.

The WOMEN study: what is the optimal method for ischemia evaluation in women? A multi-center, prospective, randomized study to establish the optimal method for detection of coronary artery disease (CAD) risk in women at an intermediate-high pretest likelihood of CAD: study design.

PubMed

Mieres, Jennifer H; Shaw, Leslee J; Hendel, Robert C; Heller, Gary V

2009-01-01

Coronary artery disease remains the leading cause of morbidity and mortality in women. The optimal non-invasive test for evaluation of ischemic heart disease in women is unknown. Although current guidelines support the choice of the exercise tolerance test (ETT) as a first line test for women with a normal baseline ECG and adequate exercise capabilities, supportive data for this recommendation are controversial. The what is the optimal method for ischemia evaluation in women? (WOMEN) study was designed to determine the optimal non-invasive strategy for CAD risk detection of intermediate and high risk women presenting with chest pain or equivalent symptoms suggestive of ischemic heart disease. The study will prospectively compare the 2-year event rates in women capable of performing exercise treadmill testing or Tc-99 m tetrofosmin SPECT myocardial perfusion imaging (MPI). The study will enroll women presenting for the evaluation of chest pain or anginal equivalent symptoms who are capable of performing >5 METs of exercise while at intermediate-high pretest risk for ischemic heart disease who will be randomized to either ETT testing alone or with Tc-99 m tetrofosmin SPECT MPI. The null hypothesis for this project is that the exercise ECG has the same negative predictive value for risk detection as gated myocardial perfusion SPECT in women. The primary aim is to compare 2-year cardiac event rates in women randomized to SPECT MPI to those randomized to ETT. The WOMEN study seeks to provide objective information for guidelines for the evaluation of symptomatic women with an intermediate-high likelihood for CAD.

Variable size computer-aided detection prompts and mammography film reader decisions

PubMed Central

Gilbert, Fiona J; Astley, Susan M; Boggis, Caroline RM; McGee, Magnus A; Griffiths, Pamela M; Duffy, Stephen W; Agbaje, Olorunsola F; Gillan, Maureen GC; Wilson, Mary; Jain, Anil K; Barr, Nicola; Beetles, Ursula M; Griffiths, Miriam A; Johnson, Jill; Roberts, Rita M; Deans, Heather E; Duncan, Karen A; Iyengar, Geeta

2008-01-01

Introduction The purpose of the present study was to investigate the effect of computer-aided detection (CAD) prompts on reader behaviour in a large sample of breast screening mammograms by analysing the relationship of the presence and size of prompts to the recall decision. Methods Local research ethics committee approval was obtained; informed consent was not required. Mammograms were obtained from women attending routine mammography at two breast screening centres in 1996. Films, previously double read, were re-read by a different reader using CAD. The study material included 315 cancer cases comprising all screen-detected cancer cases, all subsequent interval cancers and 861 normal cases randomly selected from 10,267 cases. Ground truth data were used to assess the efficacy of CAD prompting. Associations between prompt attributes and tumour features or reader recall decisions were assessed by chi-squared tests. Results There was a highly significant relationship between prompting and a decision to recall for cancer cases and for a random sample of normal cases (P < 0.001). Sixty-four per cent of all cases contained at least one CAD prompt. In cancer cases, larger prompts were more likely to be recalled (P = 0.02) for masses but there was no such association for calcifications (P = 0.9). In a random sample of 861 normal cases, larger prompts were more likely to be recalled (P = 0.02) for both mass and calcification prompts. Significant associations were observed with prompting and breast density (p = 0.009) for cancer cases but not for normal cases (P = 0.05). Conclusions For both normal cases and cancer cases, prompted mammograms were more likely to be recalled and the prompt size was also associated with a recall decision. PMID:18724867

Variable size computer-aided detection prompts and mammography film reader decisions.

PubMed

Gilbert, Fiona J; Astley, Susan M; Boggis, Caroline Rm; McGee, Magnus A; Griffiths, Pamela M; Duffy, Stephen W; Agbaje, Olorunsola F; Gillan, Maureen Gc; Wilson, Mary; Jain, Anil K; Barr, Nicola; Beetles, Ursula M; Griffiths, Miriam A; Johnson, Jill; Roberts, Rita M; Deans, Heather E; Duncan, Karen A; Iyengar, Geeta

2008-01-01

The purpose of the present study was to investigate the effect of computer-aided detection (CAD) prompts on reader behaviour in a large sample of breast screening mammograms by analysing the relationship of the presence and size of prompts to the recall decision. Local research ethics committee approval was obtained; informed consent was not required. Mammograms were obtained from women attending routine mammography at two breast screening centres in 1996. Films, previously double read, were re-read by a different reader using CAD. The study material included 315 cancer cases comprising all screen-detected cancer cases, all subsequent interval cancers and 861 normal cases randomly selected from 10,267 cases. Ground truth data were used to assess the efficacy of CAD prompting. Associations between prompt attributes and tumour features or reader recall decisions were assessed by chi-squared tests. There was a highly significant relationship between prompting and a decision to recall for cancer cases and for a random sample of normal cases (P < 0.001). Sixty-four per cent of all cases contained at least one CAD prompt. In cancer cases, larger prompts were more likely to be recalled (P = 0.02) for masses but there was no such association for calcifications (P = 0.9). In a random sample of 861 normal cases, larger prompts were more likely to be recalled (P = 0.02) for both mass and calcification prompts. Significant associations were observed with prompting and breast density (p = 0.009) for cancer cases but not for normal cases (P = 0.05). For both normal cases and cancer cases, prompted mammograms were more likely to be recalled and the prompt size was also associated with a recall decision.

Cardiac Magnetic Resonance Imaging for the Diagnosis of Coronary Artery Disease

PubMed Central

2010-01-01

Executive Summary In July 2009, the Medical Advisory Secretariat (MAS) began work on Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease (CAD), an evidence-based review of the literature surrounding different cardiac imaging modalities to ensure that appropriate technologies are accessed by patients suspected of having CAD. This project came about when the Health Services Branch at the Ministry of Health and Long-Term Care asked MAS to provide an evidentiary platform on effectiveness and cost-effectiveness of non-invasive cardiac imaging modalities. After an initial review of the strategy and consultation with experts, MAS identified five key non-invasive cardiac imaging technologies for the diagnosis of CAD. Evidence-based analyses have been prepared for each of these five imaging modalities: cardiac magnetic resonance imaging, single photon emission computed tomography, 64-slice computed tomographic angiography, stress echocardiography, and stress echocardiography with contrast. For each technology, an economic analysis was also completed (where appropriate). A summary decision analytic model was then developed to encapsulate the data from each of these reports (available on the OHTAC and MAS website). The Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease series is made up of the following reports, which can be publicly accessed at the MAS website at: www.health.gov.on.ca/mas or at www.health.gov.on.ca/english/providers/program/mas/mas_about.html Single Photon Emission Computed Tomography for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis Stress Echocardiography for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis Stress Echocardiography with Contrast for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis 64-Slice Computed Tomographic Angiography for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis Cardiac Magnetic Resonance Imaging for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis Pease note that two related evidence-based analyses of non-invasive cardiac imaging technologies for the assessment of myocardial viability are also available on the MAS website: Positron Emission Tomography for the Assessment of Myocardial Viability: An Evidence-Based Analysis Magnetic Resonance Imaging for the Assessment of Myocardial Viability: an Evidence-Based Analysis The Toronto Health Economics and Technology Assessment Collaborative has also produced an associated economic report entitled: The Relative Cost-effectiveness of Five Non-invasive Cardiac Imaging Technologies for Diagnosing Coronary Artery Disease in Ontario [Internet]. Available from: http://theta.utoronto.ca/reports/?id=7 Objective The objective of this analysis was to determine the diagnostic accuracy of cardiac magnetic resonance imaging (MRI) for the diagnosis of patients with known/suspected coronary artery disease (CAD) compared to coronary angiography. Cardiac MRI Stress cardiac MRI is a non-invasive, x-ray free imaging technique that takes approximately 30 to 45 minutes to complete and can be performed using to two different methods, a) perfusion imaging following a first pass of an intravenous bolus of gadolinium contrast, or b) wall motion imaging. Stress is induced pharmacologically with either dobutamine, dipyridamole, or adenosine, as physical exercise is difficult to perform within the magnet bore and often induces motion artifacts. Alternatives to stress cardiac perfusion MRI include stress single-photon emission computed tomography (SPECT) and stress echocardiography (ECHO). The advantage of cardiac MRI is that it does not pose the radiation burden associated with SPECT. During the same sitting, cardiac MRI can also assess left and right ventricular dimensions, viability, and cardiac mass. It may also mitigate the need for invasive diagnostic coronary angiography in patients with intermediate risk factors for CAD. Evidence-Based Analysis Literature Search A literature search was performed on October 9, 2009 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from January 1, 2005 to October 9, 2008. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria, full-text articles were obtained. Reference lists were also examined for any relevant studies not identified through the search. Articles with unknown eligibility were reviewed with a second clinical epidemiologist and then a group of epidemiologists until consensus was established. The quality of evidence was assessed as high, moderate, low or very low according to GRADE methodology. Given the large amount of clinical heterogeneity of the articles meeting the inclusion criteria, as well as suggestions from an Expert Advisory Panel Meeting held on October 5, 2009, the inclusion criteria were revised to examine the effectiveness of cardiac MRI for the detection of CAD. Inclusion Criteria Exclusion Criteria Heath technology assessments, systematic reviews, randomized controlled trials, observational studies ≥20 adult patients enrolled. Published 2004-2009 Licensed by Health Canada For diagnosis of CAD: Reference standard is coronary angiography Significant CAD defined as ≥ 50% coronary stenosis Patients with suspected or known CAD Reported results by patient, not segment Non-English studies Grey literature Planar imaging MUGA Patients with recent MI (i.e., within 1 month) Patients with non-ischemic heart disease Studies done exclusively in special populations (e.g., women, diabetics) Outcomes of Interest Sensitivity and specificity Area under the curve (AUC) Diagnostic odds ratio (DOR) Summary of Findings Stress cardiac MRI using perfusion analysis yielded a pooled sensitivity of 0.91 (95% CI: 0.89 to 0.92) and specificity of 0.79 (95% CI: 0.76 to 0.82) for the detection of CAD. Stress cardiac MRI using wall motion analysis yielded a pooled sensitivity of 0.81 (95% CI: 0.77 to 0.84) and specificity of 0.85 (95% CI: 0.81 to 0.89) for the detection of CAD. Based on DORs, there was no significant difference between pooled stress cardiac MRI using perfusion analysis and pooled stress cardiac MRI using wall motion analysis (P=0.26) for the detection of CAD. Pooled subgroup analysis of stress cardiac MRI using perfusion analysis showed no significant difference in the DORs between 1.5T and 3T MRI (P=0.72) for the detection of CAD. One study (N=60) was identified that examined stress cardiac MRI using wall motion analysis with a 3T MRI. The sensitivity and specificity of 3T MRI were 0.64 (95% CI: 0.44 to 0.81) and 1.00 (95% CI: 0.89 to 1.00), respectively, for the detection of CAD. The effectiveness of stress cardiac MRI for the detection of CAD in unstable patients with acute coronary syndrome was reported in only one study (N=35). Using perfusion analysis, the sensitivity and specificity were 0.72 (95% CI: 0.53 to 0.87) and 1.00 (95% CI: 0.54 to 1.00), respectively, for the detection of CAD. Ontario Health System Impact Analysis According to an expert consultant, in Ontario: Stress first pass perfusion is currently performed in small numbers in London (London Health Sciences Centre) and Toronto (University Health Network at the Toronto General Hospital site and Sunnybrook Health Sciences Centre). Stress wall motion is only performed as part of research protocols and not very often. Cardiac MRI machines use 1.5T almost exclusively, with 3T used in research for first pass perfusion. On November 25 2009, the Cardiac Imaging Expert Advisory Panel met and made the following comments about stress cardiac MRI for perfusion analysis: Accessibility to cardiac MRI is limited and generally used to assess structural abnormalities. Most MRIs in Ontario are already in 24–hour, constant use and it would thus be difficult to add cardiac MRI for CAD diagnosis as an additional indication. The performance of cardiac MRI for the diagnosis of CAD can be technically challenging. GRADE Quality of Evidence for Cardiac MRI in the Diagnosis of CAD The quality of the body of evidence was assessed according to the GRADE Working Group criteria for diagnostic tests. For perfusion analysis, the overall quality was determined to be low and for wall motion analysis the overall quality was very low. PMID:23074389

Automated identification of the lung contours in positron emission tomography

NASA Astrophysics Data System (ADS)

Nery, F.; Silvestre Silva, J.; Ferreira, N. C.; Caramelo, F. J.; Faustino, R.

2013-03-01

Positron Emission Tomography (PET) is a nuclear medicine imaging technique that permits to analyze, in three dimensions, the physiological processes in vivo. One of the areas where PET has demonstrated its advantages is in the staging of lung cancer, where it offers better sensitivity and specificity than other techniques such as CT. On the other hand, accurate segmentation, an important procedure for Computer Aided Diagnostics (CAD) and automated image analysis, is a challenging task given the low spatial resolution and the high noise that are intrinsic characteristics of PET images. This work presents an algorithm for the segmentation of lungs in PET images, to be used in CAD and group analysis in a large patient database. The lung boundaries are automatically extracted from a PET volume through the application of a marker-driven watershed segmentation procedure which is robust to the noise. In order to test the effectiveness of the proposed method, we compared the segmentation results in several slices using our approach with the results obtained from manual delineation. The manual delineation was performed by nuclear medicine physicians that used a software routine that we developed specifically for this task. To quantify the similarity between the contours obtained from the two methods, we used figures of merit based on region and also on contour definitions. Results show that the performance of the algorithm was similar to the performance of human physicians. Additionally, we found that the algorithm-physician agreement is similar (statistically significant) to the inter-physician agreement.

Real-time myocardial perfusion imaging for pharmacologic stress testing: added value to single photon emission computed tomography.

PubMed

Korosoglou, Grigorios; Dubart, Alain-Eric; DaSilva, K Gaspar C; Labadze, Nino; Hardt, Stefan; Hansen, Alexander; Bekeredjian, Raffi; Zugck, Christian; Zehelein, Joerg; Katus, Hugo A; Kuecherer, Helmut

2006-01-01

Little is known about the incremental value of real-time myocardial contrast echocardiography (MCE) as an adjunct to pharmacologic stress testing. This study was performed to evaluate the diagnostic value of MCE to detect abnormal myocardial perfusion by technetium Tc 99m sestamibi-single photon emission computed tomography (SPECT) and anatomically significant coronary artery disease (CAD) by angiography. Myocardial contrast echocardiography was performed at rest and during vasodilator stress in consecutive patients (N = 120) undergoing SPECT imaging for known or suspected CAD. Myocardial opacification, wall motion, and tracer uptake were visually analyzed in 12 myocardial segments by 2 pairs of blinded observers. Concordance between the 2 methods was assessed using the kappa statistic. Of 1356 segments, 1025 (76%) were interpretable by MCE, wall motion, and SPECT. Sensitivity of wall motion was 75%, specificity 83%, and accuracy 81% for detecting abnormal myocardial perfusion by SPECT (kappa = 0.53). Myocardial contrast echocardiography and wall motion together yielded significantly higher sensitivity (85% vs 74%, P < .05), specificity of 83%, and accuracy of 85% (kappa = 0.64) for the detection of abnormal myocardial perfusion. In 89 patients who underwent coronary angiography, MCE and wall motion together yielded higher sensitivity (83% vs 64%, P < .05) and accuracy (77% vs 68%, P < .05) but similar specificity (72%) compared with SPECT for the detection of high-grade, stenotic (> or = 75%) coronary lesions. Assessment of myocardial perfusion adds value to conventional stress echocardiography by increasing its sensitivity for the detection of functionally abnormal myocardial perfusion. Myocardial contrast echocardiography and wall motion together provide higher sensitivity and accuracy for detection of CAD compared with SPECT.

Rationale and design of a trial to personalize risk assessment in familial coronary artery disease.

PubMed

Marwick, Thomas H; Whitmore, Kristyn; Nicholls, Stephen J; Stanton, Tony; Mitchell, Geoffrey; Tonkin, Andrew; Blizzard, Christopher; Neil, Amanda; Jones, Catherine; Watts, Gerald F

2018-05-01

The lifetime risk of coronary artery disease (CAD) is doubled in people with a family history of premature disease, yet this risk is not captured in most 5- or 10-year risk assessment algorithms. Coronary artery calcium scoring (CCS) is a marker of subclinical CAD risk, which has been shown in observational studies to provide prognostic information that is incremental to clinical assessment; is relatively inexpensive; and is performed with a small radiation dose. However, the use of CCS in guiding prevention is not strongly supported by guidelines. Showing definitive evidence of the efficacy and cost-effectiveness of CCS is therefore of importance. The proposed randomized controlled trial of the use of CCS will be targeted to 40- to 70-year-old first-degree relatives of patients with CAD onset <60 years old or second-degree relatives of patients with onset <50 years old. Control patients will undergo standard risk scoring and be blinded to CCS results. In the intervention group, primary prevention in patients undergoing CCS will be informed by this score. At 3 years, effectiveness will be assessed on change in plaque volume at computed tomography coronary angiography, the extent of which has been strongly linked to outcome. The CAUGHT-CAD trial will provide evidence to inform the guidelines regarding the place of CCS in decision making regarding primary prevention of patients with a family history of premature disease. Copyright © 2017 Elsevier Inc. All rights reserved.

Health care resource utilization and costs during episodes of care for type 2 diabetes mellitus-related comorbidities.

PubMed

Candrilli, S D; Meyers, J L; Boye, K; Bae, J P

2015-01-01

To obtain costs of episodes of care for type 2 diabetes mellitus (T2DM)-related comorbidities. Data from the MarketScan Commercial Claims and Encounters Database were analyzed with the Medical Episode Grouper software, which uses proprietary algorithms to identify episodes of care. Episodes relevant to the T2DM population were examined, including: coronary artery disease with acute myocardial infarction, ventricular fibrillation, shock, and/or cardiac arrest (CAD episodes); cerebrovascular disease with stroke (CVD episodes); hypoglycemia; T2DM with complications (complication episodes); and renal failure. 45,350 CAD; 85,287 CVD; 29,886 hypoglycemia; 40,339 complication; and 211,673 renal failure episodes were identified. Mean (SD) episode durations were 15.2 (39.1), 25.5 (55.0), 5.9 (24.0), 21.2 (54.6), and 364.0 (0.0) days, respectively. Inpatient visits were the largest component of unadjusted costs for CAD, CVD, and complication episodes (93.4%, 78.3%, and 91.9%, respectively). Other ancillary care represented the largest component of unadjusted costs for hypoglycemia (53.3%) and renal failure (80.5%) episodes. Mean adjusted total costs were $16,435; $4558; $445; $5675; and $8765 for CAD, CVD, hypoglycemia, complication, and renal failure episodes, respectively. This study adds important information to the literature regarding costs of episodes of care for patients with T2DM in the US. Copyright © 2015 Elsevier Inc. All rights reserved.

Precision reconstruction of manufactured free-form components

NASA Astrophysics Data System (ADS)

Ristic, Mihailo; Brujic, Djordje; Ainsworth, Iain

2000-03-01

Manufacturing needs in many industries, especially the aerospace and the automotive, involve CAD remodeling of manufactured free-form parts using NURBS. This is typically performed as part of 'first article inspection' or 'closing the design loop.' The reconstructed model must satisfy requirements such as accuracy, compatibility with the original CAD model and adherence to various constraints. The paper outlines a methodology for realizing this task. Efficiency and quality of the results are achieved by utilizing the nominal CAD model. It is argued that measurement and remodeling steps are equally important. We explain how the measurement was optimized in terms of accuracy, point distribution and measuring speed using a CMM. Remodeling steps include registration, data segmentation, parameterization and surface fitting. Enforcement of constraints such as continuity was performed as part of the surface fitting process. It was found necessary that the relevant algorithms are able to perform in the presence of measurement noise, while making no special assumptions about regularity of data distribution. In order to deal with real life situations, a number of supporting functions for geometric modeling were required and these are described. The presented methodology was applied using real aeroengine parts and the experimental results are presented.

Cadmium-sensitive, cad1 mutants of Arabidopsis thaliana are phytochelatin deficient.

PubMed Central

Howden, R; Goldsbrough, P B; Andersen, C R; Cobbett, C S

1995-01-01

An allelic series of cad1, cadmium-sensitive mutants of Arabidopsis thaliana, was isolated. These mutants were sensitive to cadmium to different extents and were deficient in their ability to form cadmium-peptide complexes as detected by gel-filtration chromatography. Each mutant was deficient in its ability to accumulate phytochelatins (PCs) as detected by high-performance liquid chromatography and the amount of PCs accumulated by each mutant correlated with its degree of sensitivity to cadmium. The mutants had wild-type levels of glutathione, the substrate for PC biosynthesis, and in vitro assays demonstrated that each of the mutants was deficient in PC synthase activity. These results demonstrate conclusively the importance of PCs for cadmium tolerance in plants. PMID:7770517

Parametric bicubic spline and CAD tools for complex targets shape modelling in physical optics radar cross section prediction

NASA Astrophysics Data System (ADS)

Delogu, A.; Furini, F.

1991-09-01

Increasing interest in radar cross section (RCS) reduction is placing new demands on theoretical, computation, and graphic techniques for calculating scattering properties of complex targets. In particular, computer codes capable of predicting the RCS of an entire aircraft at high frequency and of achieving RCS control with modest structural changes, are becoming of paramount importance in stealth design. A computer code, evaluating the RCS of arbitrary shaped metallic objects that are computer aided design (CAD) generated, and its validation with measurements carried out using ALENIA RCS test facilities are presented. The code, based on the physical optics method, is characterized by an efficient integration algorithm with error control, in order to contain the computer time within acceptable limits, and by an accurate parametric representation of the target surface in terms of bicubic splines.

Automated Design Tools for Integrated Mixed-Signal Microsystems (NeoCAD)

DTIC Science & Technology

2005-02-01

method, Model Order Reduction (MOR) tools, system-level, mixed-signal circuit synthesis and optimization tools, and parsitic extraction tools. A unique...Mission Area: Command and Control mixed signal circuit simulation parasitic extraction time-domain simulation IC design flow model order reduction... Extraction 1.2 Overall Program Milestones CHAPTER 2 FAST TIME DOMAIN MIXED-SIGNAL CIRCUIT SIMULATION 2.1 HAARSPICE Algorithms 2.1.1 Mathematical Background

Shear Bond Strength of Repair Systems to New CAD/CAM Restorative Materials.

PubMed

Üstün, Özlem; Büyükhatipoğlu, Işıl Keçik; Seçilmiş, Aslı

2016-11-23

To evaluate the bond strength of repair systems (Ceramic Repair, Clearfil Repair) to computer-aided design/computer-assisted machining (CAD/CAM) restorative materials (IPS e.max CAD, Vita Suprinity, Vita Enamic, Lava Ultimate). Thermally aged CAD/CAM restorative material specimens (5000 cycles between 5°C and 55°C) were randomly divided into two groups according to the repair system: Ceramic Repair (37% phosphoric acid + Monobond-S + Heliobond + Tetric N Ceram) or Clearfil Repair (40% phosphoric acid + mixture of Clearfil Porcelain Bond Activator and Clearfil SE Bond Primer + Clearfil SE Bond + Filtek Z250). The resin composite was light-cured on conditioned specimens. All specimens were stored in distilled water at 37°C for 24 hours and then additionally aged for 5000 thermal cycles. The shear bond strength test was performed using a universal testing machine (0.5 mm/min). Two-way ANOVA was used to detect significance differences according to the CAD/CAM material and composite repair system factors. Subgroup analyses were conducted using the least significant difference post-hoc test. The results of two-way ANOVA indicated that bond strength values varied according to the restorative materials (p < 0.05). No significant differences were observed between the CAD/CAM restorative materials (p > 0.05), except in the Vita Suprinity group (p < 0.05). Moreover, no differences were observed between the repair systems. Both the Clearfil and Ceramic repair systems used in the study allow for successful repairs. © 2016 by the American College of Prosthodontists.

Acute Myocardial Infarction Risk in Patients with Coronary Artery Disease Doubled after Upper Gastrointestinal Tract Bleeding: A Nationwide Nested Case-Control Study.

PubMed

Wu, Chia-Jung; Lin, Hung-Jung; Weng, Shih-Feng; Hsu, Chien-Chin; Wang, Jhi-Joung; Su, Shih-Bin; Huang, Chien-Cheng; Guo, How-Ran

2015-01-01

Prior studies of upper gastrointestinal bleeding (UGIB) and acute myocardial infarction (AMI) are small, and long-term effects of UGIB on AMI have not been delineated. We investigated whether UGIB in patients diagnosed with coronary artery disease (CAD) increased their risk of subsequent AMI. This was a population-based, nested case-control study using Taiwan's National Health Insurance Research Database. After propensity-score matching for age, gender, comorbidities, CAD date, and follow-up duration, we identified 1,677 new-onset CAD patients with AMI (AMI[+]) between 2001 and 2006 as the case group and 10,062 new-onset CAD patients without (AMI[-]) as the control group. Conditional logistic regression was used to examine the association between UGIB and AMI. Compared with UGIB[-] patients, UGIB[+] patients had twice the risk for subsequent AMI (adjusted odds ratio [AOR] = 2.08; 95% confidence interval [CI], 1.72-2.50). In the subgroup analysis for gender and age, UGIB[+] women (AOR = 2.70; 95% CI, 2.03-3.57) and patients < 65 years old (AOR = 2.23; 95% CI, 1.56-3.18) had higher odds of an AMI. UGIB[+] AMI[+] patients used nonsignificantly less aspirin than did UGIB[-] AMI[+] patients (27.69% vs. 35.61%, respectively). UGIB increased the risk of subsequent AMI in CAD patients, especially in women and patients < 65. This suggests that physicians need to use earlier and more aggressive intervention to detect UGIB and prevent AMI in CAD patients.

Predicting obstructive coronary artery disease using carotid ultrasound parameters: a nomogram from a large real-world clinical data.

PubMed

Wu, Na; Chen, Xinghua; Li, Mingyang; Qu, Xiaolong; Li, Yueli; Xie, Weijia; Wu, Long; Xiang, Ying; Li, Yafei; Zhong, Li

2018-05-21

Carotid ultrasound is a non-invasive tool for risk assessment of coronary artery disease (CAD). There is no consensus on which carotid ultrasound parameter constitutes the best measurement of atherosclerosis. We investigated which model of carotid ultrasound parameters and clinical risk factors (CRF) have the highest predictive value for CAD. We enrolled 2431 consecutive patients who have suspected CAD and underwent coronary angiography and carotid ultrasound with measurements of carotid intima-media thickness (CIMT), total number of plaques and areas of different types of plaques classified by echogenicity. Total number of plaques demonstrated the highest incremental prediction ability to predict CAD over CRF (area under the curve [AUC] 0.752 vs 0.701, net reclassification index [NRI] = 0.514, P < 0.001), followed by area of maximum mixed and soft plaques. CIMT had no significant incremental value over CRF (AUC 0.704 vs 0.701, P = 0.241; NRI = 0.062, P = 0.168). The model comprising total number of plaques, areas of maximum soft, hard and mixed plaques plus CRF had the highest discriminatory (AUC = 0.757) and reclassification value (NRI = 0.567) for CAD. A nomogram based on this model was developed to predict CAD. For subjects at low and intermediate risk, the model comprising total number of plaques plus CRF was the best. Total number of plaques, area of maximum soft, hard and mixed plaques showed significantly incremental prediction ability over CRF. A nomogram based on these factors provided an intuitive and practical method in detecting CAD. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Haptic feedback improves surgeons' user experience and fracture reduction in facial trauma simulation.

PubMed

Girod, Sabine; Schvartzman, Sara C; Gaudilliere, Dyani; Salisbury, Kenneth; Silva, Rebeka

2016-01-01

Computer-assisted surgical (CAS) planning tools are available for craniofacial surgery, but are usually based on computer-aided design (CAD) tools that lack the ability to detect the collision of virtual objects (i.e., fractured bone segments). We developed a CAS system featuring a sense of touch (haptic) that enables surgeons to physically interact with individual, patient-specific anatomy and immerse in a three-dimensional virtual environment. In this study, we evaluated initial user experience with our novel system compared to an existing CAD system. Ten surgery resident trainees received a brief verbal introduction to both the haptic and CAD systems. Users simulated mandibular fracture reduction in three clinical cases within a 15 min time limit for each system and completed a questionnaire to assess their subjective experience. We compared standard landmarks and linear and angular measurements between the simulated results and the actual surgical outcome and found that haptic simulation results were not significantly different from actual postoperative outcomes. In contrast, CAD results significantly differed from both the haptic simulation and actual postoperative results. In addition to enabling a more accurate fracture repair, the haptic system provided a better user experience than the CAD system in terms of intuitiveness and self-reported quality of repair.

Defeaturing CAD models using a geometry-based size field and facet-based reduction operators.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quadros, William Roshan; Owen, Steven James

2010-04-01

We propose a method to automatically defeature a CAD model by detecting irrelevant features using a geometry-based size field and a method to remove the irrelevant features via facet-based operations on a discrete representation. A discrete B-Rep model is first created by obtaining a faceted representation of the CAD entities. The candidate facet entities are then marked for reduction by using a geometry-based size field. This is accomplished by estimating local mesh sizes based on geometric criteria. If the field value at a facet entity goes below a user specified threshold value then it is identified as an irrelevant featuremore » and is marked for reduction. The reduction of marked facet entities is primarily performed using an edge collapse operator. Care is taken to retain a valid geometry and topology of the discrete model throughout the procedure. The original model is not altered as the defeaturing is performed on a separate discrete model. Associativity between the entities of the discrete model and that of original CAD model is maintained in order to decode the attributes and boundary conditions applied on the original CAD entities onto the mesh via the entities of the discrete model. Example models are presented to illustrate the effectiveness of the proposed approach.« less

Computer-aided diagnosis of prostate cancer using multi-parametric MRI: comparison between PUN and Tofts models

NASA Astrophysics Data System (ADS)

Mazzetti, S.; Giannini, V.; Russo, F.; Regge, D.

2018-05-01

Computer-aided diagnosis (CAD) systems are increasingly being used in clinical settings to report multi-parametric magnetic resonance imaging (mp-MRI) of the prostate. Usually, CAD systems automatically highlight cancer-suspicious regions to the radiologist, reducing reader variability and interpretation errors. Nevertheless, implementing this software requires the selection of which mp-MRI parameters can best discriminate between malignant and non-malignant regions. To exploit functional information, some parameters are derived from dynamic contrast-enhanced (DCE) acquisitions. In particular, much CAD software employs pharmacokinetic features, such as K trans and k ep, derived from the Tofts model, to estimate a likelihood map of malignancy. However, non-pharmacokinetic models can be also used to describe DCE-MRI curves, without any requirement for prior knowledge or measurement of the arterial input function, which could potentially lead to large errors in parameter estimation. In this work, we implemented an empirical function derived from the phenomenological universalities (PUN) class to fit DCE-MRI. The parameters of the PUN model are used in combination with T2-weighted and diffusion-weighted acquisitions to feed a support vector machine classifier to produce a voxel-wise malignancy likelihood map of the prostate. The results were all compared to those for a CAD system based on Tofts pharmacokinetic features to describe DCE-MRI curves, using different quality aspects of image segmentation, while also evaluating the number and size of false positive (FP) candidate regions. This study included 61 patients with 70 biopsy-proven prostate cancers (PCa). The metrics used to evaluate segmentation quality between the two CAD systems were not statistically different, although the PUN-based CAD reported a lower number of FP, with reduced size compared to the Tofts-based CAD. In conclusion, the CAD software based on PUN parameters is a feasible means with which to detect PCa, without affecting segmentation quality, and hence it could be successfully applied in clinical settings, improving the automated diagnosis process and reducing computational complexity.

Functional evaluation of a CAD/CAM prosthesis for immediate defect repair after total maxillectomy: a case series of 18 patients with maxillary sinus cancer.

PubMed

Jiang, Fei-Fei; Hou, Yan; Lu, Li; Ding, Xiao-Xu; Li, Wei; Yan, Ai-Hui

2015-01-01

To evaluate the facial profiles and functional recovery of 18 patients treated by a computer-aided designed/manufactured hollow obturator prosthesis (CAD/CAM prosthesis) after total maxillectomy for malignant maxillary sinus tumor. A retrospective observational study was performed to evaluate the facial profiles and functional recovery of 18 patients with T3-4a N0 M0 maxillary sinus cancer, who were treated by total maxillectomy and simultaneous implantation of a computer-aided designed/manufactured hollow obturator prosthesis (CAD/CAM prosthesis). Follow-ups were performed 1, 3, 6, and 12 months after surgery. Facial measurements, speech intelligibility, and chewing and swallowing functions were examined. Thirteen patients converted to a permanent prosthesis 6 months after surgery. Comparisons were made between patients with and without the CAD/CAM or permanent prosthesis at various times using SPSS13.0 statistical software (SPSS Inc., Chicago, IL, USA). Speech intelligibility, facial depression, and eyeball prolapse results showed improvements with prosthesis use at 1, 3, and 6 months after surgery (p < 0.05). Swallowing function improved from level V to level II-IV with prosthesis use at 1, 3, and 6 months, and reached level I or II with permanent prosthesis use at 12 months after surgery. Simultaneous CAD/CAM prosthesis implantation recovered the facial profile, enhanced the speaking, swallowing, and chewing functions, and improved the quality of life of patients. Tumor recurrence can be detected by direct observation of the postoperative maxillary cavity. Therefore, this operation is recommended for simultaneous excision repair and functional reconstruction after total maxillectomy. This surgical treatment of maxillary sinus cancer is applied rarely in China, but it has a good effect based on our observation. Simultaneous CAD/CAM prosthesis implantation after total maxillectomy can recover the facial profile, enhance the speaking, swallowing, and chewing functions, and improve the quality of life of patients. Tumor recurrence can be detected by direct observation of the postoperative maxillary cavity. This technique avoids the need for dental implants because the bottom part of the prosthesis contains a palatal plate with dentures. © 2014 Wiley Periodicals, Inc.

Association of polymorphisms G(-174)C in IL-6 gene and G(-1082)A in IL-10 gene with traditional cardiovascular risk factors in patients with coronary artery disease.

PubMed

Elsaid, Afaf; Abdel-Aziz, A F; Elmougy, Rehab; Elwaseef, A M

2014-08-01

Interleukin-6 (IL-6) polymorphism has been associated with the genetic susceptibility to coronary artery disease (CAD) and also with the lipid profile in different populations. The present work aimed at studying the association, if any between the IL-6 (174) G/C and IL-10 (1082) G/A genes with hypertension or hyperlipidimia in Egyptian patients with CAD and the association of the IL-6 -174 G/C polymorphism with serum IL-6 levels. 108 Egyptian patients with CAD and 143 unrelated healthy subjects were included in the study. The different genotypes of IL-6 and IL-10 were detected by polymerase chain reaction. Serum levels of lipoprotein(a) [Lp(a)] and IL-6 were estimated in the patients, as well as in the healthy subjects. Increased frequency of G allele, GG and GC genotypes in IL-6, as well as decreased frequency of C allele and CC genotype were found in CAD patients, compared to healthy subjects [P = < 0.0001, OR = 3.95, 95% CI (2.16-7.22) for GG and GC vs CC genotype], [P = < 0.0001, OR = 3.44, 95% CI (2.26-5.23) for G allele]. There was an increased frequency of G allele vs A allele in IL-10 genotype in CAD patients, compared to healthy subjects [P = 0.005, OR = 1.866, 95% CI (1.2-2.9]. Higher levels of both Lp(a) and IL-6 were observed in CAD patients, compared to control subjects (P = 0.0012, P = 0.0346, respectively). Increased frequency of IL-6 -174 G-allele was implicated in a greater cardiovascular risk and the presence of G allele or homozygosity for G allele of IL-10 G/A (1082) was associated with an increased prevalence of CAD. The GC genotype and G allele in IL-6 had significant correlation with hyperlipidimic CAD patients; however, G allele in IL-6 and IL-10 showed significant association with hypertension. Thus, G allele in IL-6 and IL-10 was considered as an independent risk factor in hypertensive CAD patients.

Marker-based or model-based RSA for evaluation of hip resurfacing arthroplasty? A clinical validation and 5-year follow-up.

PubMed

Lorenzen, Nina Dyrberg; Stilling, Maiken; Jakobsen, Stig Storgaard; Gustafson, Klas; Søballe, Kjeld; Baad-Hansen, Thomas

2013-11-01

The stability of implants is vital to ensure a long-term survival. RSA determines micro-motions of implants as a predictor of early implant failure. RSA can be performed as a marker- or model-based analysis. So far, CAD and RE model-based RSA have not been validated for use in hip resurfacing arthroplasty (HRA). A phantom study determined the precision of marker-based and CAD and RE model-based RSA on a HRA implant. In a clinical study, 19 patients were followed with stereoradiographs until 5 years after surgery. Analysis of double-examination migration results determined the clinical precision of marker-based and CAD model-based RSA, and at the 5-year follow-up, results of the total translation (TT) and the total rotation (TR) for marker- and CAD model-based RSA were compared. The phantom study showed that comparison of the precision (SDdiff) in marker-based RSA analysis was more precise than model-based RSA analysis in TT (p CAD < 0.001; p RE = 0.04) and TR (p CAD = 0.01; p RE < 0.001). The clinical precision (double examination in 8 patients) comparing the precision SDdiff was better evaluating the TT using the marker-based RSA analysis (p = 0.002), but showed no difference between the marker- and CAD model-based RSA analysis regarding the TR (p = 0.91). Comparing the mean signed values regarding the TT and the TR at the 5-year follow-up in 13 patients, the TT was lower (p = 0.03) and the TR higher (p = 0.04) in the marker-based RSA compared to CAD model-based RSA. The precision of marker-based RSA was significantly better than model-based RSA. However, problems with occluded markers lead to exclusion of many patients which was not a problem with model-based RSA. HRA were stable at the 5-year follow-up. The detection limit was 0.2 mm TT and 1° TR for marker-based and 0.5 mm TT and 1° TR for CAD model-based RSA for HRA.

Differential DNases are selectively used in neuronal apoptosis depending on the differentiation state.

PubMed

Shiokawa, D; Tanuma, S

2004-10-01

In this study, we investigate the roles of two apoptotic endonucleases, CAD and DNase gamma, in neuronal apoptosis. High expression of CAD, but not DNase gamma, is detected in proliferating N1E-115 neuroblastoma cells, and apoptotic DNA fragmentation induced by staurosporine under proliferating conditions is abolished by the expression of a caspase-resistant form of ICAD. After the induction of neuronal differentiation, CAD disappearance and the induction of DNase gamma occur simultaneously in N1E-115 cells. Apoptotic DNA fragmentation that occurs under differentiating conditions is suppressed by the downregulation of DNase gamma caused by its antisense RNA. The induction of DNase gamma is also observed during neuronal differentiation of PC12 cells, and apoptotic DNA fragmentation induced by NGF deprivation is inhibited by the antisense-mediated downregulation of DNase gamma. These observations suggest that DNA fragmentation in neuronal apoptosis is catalyzed by either CAD or DNase gamma depending on the differentiation state. Furthermore, DNase gamma is suggested to be involved in naturally occurring apoptosis in developing nervous systems.

Lesion classification using clinical and visual data fusion by multiple kernel learning

NASA Astrophysics Data System (ADS)

Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

2014-03-01

To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: a meta-analysis focused on post-test disease probability.

PubMed

Knuuti, Juhani; Ballo, Haitham; Juarez-Orozco, Luis Eduardo; Saraste, Antti; Kolh, Philippe; Rutjes, Anne Wilhelmina Saskia; Jüni, Peter; Windecker, Stephan; Bax, Jeroen J; Wijns, William

2018-05-29

To determine the ranges of pre-test probability (PTP) of coronary artery disease (CAD) in which stress electrocardiogram (ECG), stress echocardiography, coronary computed tomography angiography (CCTA), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and cardiac magnetic resonance (CMR) can reclassify patients into a post-test probability that defines (>85%) or excludes (<15%) anatomically (defined by visual evaluation of invasive coronary angiography [ICA]) and functionally (defined by a fractional flow reserve [FFR] ≤0.8) significant CAD. A broad search in electronic databases until August 2017 was performed. Studies on the aforementioned techniques in >100 patients with stable CAD that utilized either ICA or ICA with FFR measurement as reference, were included. Study-level data was pooled using a hierarchical bivariate random-effects model and likelihood ratios were obtained for each technique. The PTP ranges for each technique to rule-in or rule-out significant CAD were defined. A total of 28 664 patients from 132 studies that used ICA as reference and 4131 from 23 studies using FFR, were analysed. Stress ECG can rule-in and rule-out anatomically significant CAD only when PTP is ≥80% (76-83) and ≤19% (15-25), respectively. Coronary computed tomography angiography is able to rule-in anatomic CAD at a PTP ≥58% (45-70) and rule-out at a PTP ≤80% (65-94). The corresponding PTP values for functionally significant CAD were ≥75% (67-83) and ≤57% (40-72) for CCTA, and ≥71% (59-81) and ≤27 (24-31) for ICA, demonstrating poorer performance of anatomic imaging against FFR. In contrast, functional imaging techniques (PET, stress CMR, and SPECT) are able to rule-in functionally significant CAD when PTP is ≥46-59% and rule-out when PTP is ≤34-57%. The various diagnostic modalities have different optimal performance ranges for the detection of anatomically and functionally significant CAD. Stress ECG appears to have very limited diagnostic power. The selection of a diagnostic technique for any given patient to rule-in or rule-out CAD should be based on the optimal PTP range for each test and on the assumed reference standard.

From nonlinear optimization to convex optimization through firefly algorithm and indirect approach with applications to CAD/CAM.

PubMed

Gálvez, Akemi; Iglesias, Andrés

2013-01-01

Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor's method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently.

From Nonlinear Optimization to Convex Optimization through Firefly Algorithm and Indirect Approach with Applications to CAD/CAM

PubMed Central

Gálvez, Akemi; Iglesias, Andrés

2013-01-01

Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor's method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently. PMID:24376380

Automated localization of costophrenic recesses and costophrenic angle measurement on frontal chest radiographs

NASA Astrophysics Data System (ADS)

Maduskar, Pragnya; Hogeweg, Laurens; Philipsen, Rick; van Ginneken, Bram

2013-03-01

Computer aided detection (CAD) of tuberculosis (TB) on chest radiographs (CXR) is difficult because the disease has varied manifestations, like opacification, hilar elevation, and pleural effusions. We have developed a CAD research prototype for TB (CAD4TB v1.08, Diagnostic Image Analysis Group, Nijmegen, The Netherlands) which is trained to detect textural abnormalities inside unobscured lung fields. If the only abnormality visible on a CXR would be a blunt costophrenic angle, caused by pleural fluid in the costophrenic recess, this is likely to be missed by texture analysis in the lung fields. The goal of this work is therefore to detect the presence of blunt costophrenic (CP) angles caused by pleural effusion on chest radiographs. The CP angle is the angle formed by the hemidiaphragm and the chest wall. We define the intersection point of both as the CP angle point. We first detect the CP angle point automatically from a lung field segmentation by finding the foreground pixel of each lung with maximum y location. Patches are extracted around the CP angle point and boundary tracing is performed to detect 10 consecutive pixels along the hemidiaphragm and the chest wall and derive the CP angle from these. We evaluate the method on a data set of 250 normal CXRs, 200 CXRs with only one or two blunt CP angles and 200 CXRs with one or two blunt CP angles but also other abnormalities. For these three groups, the CP angle location and angle measurements were accurate in 91%, 88%, and 92% of all the cases, respectively. The average CP angles for the three groups are indeed different with 71.6° +/- 22.9, 87.5° +/- 25.7, and 87.7° +/- 25.3, respectively.

Reentry-Vehicle Shape Optimization Using a Cartesian Adjoint Method and CAD Geometry

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.

2006-01-01

A DJOINT solutions of the governing flow equations are becoming increasingly important for the development of efficient analysis and optimization algorithms. A well-known use of the adjoint method is gradient-based shape. Given an objective function that defines some measure of performance, such as the lift and drag functionals, its gradient is computed at a cost that is essentially independent of the number of design variables (e.g., geometric parameters that control the shape). Classic aerodynamic applications of gradient-based optimization include the design of cruise configurations for transonic and supersonic flow, as well as the design of high-lift systems. are perhaps the most promising approach for addressing the issues of flow solution automation for aerodynamic design problems. In these methods, the discretization of the wetted surface is decoupled from that of the volume mesh. This not only enables fast and robust mesh generation for geometry of arbitrary complexity, but also facilitates access to geometry modeling and manipulation using parametric computer-aided design (CAD). In previous work on Cartesian adjoint solvers, Melvin et al. developed an adjoint formulation for the TRANAIR code, which is based on the full-potential equation with viscous corrections. More recently, Dadone and Grossman presented an adjoint formulation for the two-dimensional Euler equations using a ghost-cell method to enforce the wall boundary conditions. In Refs. 18 and 19, we presented an accurate and efficient algorithm for the solution of the adjoint Euler equations discretized on Cartesian meshes with embedded, cut-cell boundaries. Novel aspects of the algorithm were the computation of surface shape sensitivities for triangulations based on parametric-CAD models and the linearization of the coupling between the surface triangulation and the cut-cells. The accuracy of the gradient computation was verified using several three-dimensional test cases, which included design variables such as the free stream parameters and the planform shape of an isolated wing. The objective of the present work is to extend our adjoint formulation to problems involving general shape changes. Factors under consideration include the computation of mesh sensitivities that provide a reliable approximation of the objective function gradient, as well as the computation of surface shape sensitivities based on a direct-CAD interface. We present detailed gradient verification studies and then focus on a shape optimization problem for an Apollo-like reentry vehicle. The goal of the optimization is to enhance the lift-to-drag ratio of the capsule by modifying the shape of its heat-shield in conjunction with a center-of-gravity (c.g.) offset. This multipoint and multi-objective optimization problem is used to demonstrate the overall effectiveness of the Cartesian adjoint method for addressing the issues of complex aerodynamic design.

Acute Myocardial Infarction Risk in Patients with Coronary Artery Disease Doubled after Upper Gastrointestinal Tract Bleeding: A Nationwide Nested Case-Control Study

PubMed Central

Weng, Shih-Feng; Hsu, Chien-Chin; Wang, Jhi-Joung; Su, Shih-Bin; Huang, Chien-Cheng; Guo, How-Ran

2015-01-01

Prior studies of upper gastrointestinal bleeding (UGIB) and acute myocardial infarction (AMI) are small, and long-term effects of UGIB on AMI have not been delineated. We investigated whether UGIB in patients diagnosed with coronary artery disease (CAD) increased their risk of subsequent AMI. This was a population-based, nested case-control study using Taiwan’s National Health Insurance Research Database. After propensity-score matching for age, gender, comorbidities, CAD date, and follow-up duration, we identified 1,677 new-onset CAD patients with AMI (AMI[+]) between 2001 and 2006 as the case group and 10,062 new-onset CAD patients without (AMI[−]) as the control group. Conditional logistic regression was used to examine the association between UGIB and AMI. Compared with UGIB[−] patients, UGIB[+] patients had twice the risk for subsequent AMI (adjusted odds ratio [AOR] = 2.08; 95% confidence interval [CI], 1.72–2.50). In the subgroup analysis for gender and age, UGIB[+] women (AOR = 2.70; 95% CI, 2.03–3.57) and patients < 65 years old (AOR = 2.23; 95% CI, 1.56–3.18) had higher odds of an AMI. UGIB[+] AMI[+] patients used nonsignificantly less aspirin than did UGIB[−] AMI[+] patients (27.69% vs. 35.61%, respectively). UGIB increased the risk of subsequent AMI in CAD patients, especially in women and patients < 65. This suggests that physicians need to use earlier and more aggressive intervention to detect UGIB and prevent AMI in CAD patients. PMID:26529110

Relationship between aortic valve calcification and the severity of coronary atherosclerotic disease.

PubMed

Qian, Juying; Chen, Zhangwei; Ge, Junbo; Ma, Jianying; Chang, Shufu; Fan, Bing; Liu, Xuebo; Ge, Lei

2010-07-01

Aortic valve calcification (AVC), which has been confirmed to be associated with various risk factors of cardiac disease, is common in the elderly and associated with increased cardiovascular mortality. It has been hypothesized that AVC is associated with coronary atherosclerotic disease, and its severity. Between July 2007 and November 2007, a total of 235 patients with chest pain or chest distress were admitted to the authors' institution for coronary angiography. The severity of coronary atherosclerotic disease (CAD) was evaluated by the Gensini score, the number of stenosed vessels, and the prevalence of total occlusion. All patients underwent transthoracic echocardiography to detect AVC. Patients with CAD had a higher prevalence of AVC than those without CAD (44% versus 26%, p = 0.005). Likewise, the prevalence of AVC was significantly higher in patients with a higher Gensini score than in those with a lower score. Patients with AVC had a higher prevalence of CAD, and higher Gensini scores and numbers of stenosed coronary arteries, even after stratification by age (65 years). On multivariable logistic regression analysis for CAD, the odds ratio (OR) of AVC was 2.315 (95% confidence interval (CI): 1.158-4.629, p = 0.018); this value was higher than that for total cholesterol (OR = 1.637, p = 0.008), lipoprotein-a (OR = 1.003, p = 0.015) and fibrinogen (OR = 1.009, p = 0.006), and marginally less than that for male gender (OR = 2.665, p = 0.005). Patients with AVC had a higher prevalence and greater severity of CAD.

SNP-SNP Interaction between TLR4 and MyD88 in Susceptibility to Coronary Artery Disease in the Chinese Han Population.

PubMed

Sun, Dandan; Sun, Liping; Xu, Qian; Gong, Yuehua; Wang, Honghu; Yang, Jun; Yuan, Yuan

2016-03-04

The toll-like receptor 4 (TLR4)-myeloid differentiation factor 88 (MyD88)-dependent signaling pathway plays a role in the initiation and progression of coronary artery disease (CAD). We investigated SNP-SNP interactions between the TLR4 and MyD88 genes in CAD susceptibility and assessed whether the effects of such interactions were modified by confounding risk factors (hyperglycemia, hyperlipidemia and Helicobacter pylori (H. pylori) infection). Participants with CAD (n = 424) and controls (n = 424) without CAD were enrolled. Polymerase chain restriction-restriction fragment length polymorphism was performed on genomic DNA to detect polymorphisms in TLR4 (rs10116253, rs10983755, and rs11536889) and MyD88 (rs7744). H. pylori infections were evaluated by enzyme-linked immunosorbent assays, and the cardiovascular risk factors for each subject were evaluated clinically. The significant interaction between TLR4 rs11536889 and MyD88 rs7744 was associated with an increased CAD risk (p value for interaction = 0.024). In conditions of hyperglycemia, the interaction effect was strengthened between TLR4 rs11536889 and MyD88 rs7744 (p value for interaction = 0.004). In hyperlipidemic participants, the interaction strength was also enhanced for TLR4 rs11536889 and MyD88 rs7744 (p value for interaction = 0.006). Thus, the novel interaction between TLR4 rs11536889 and MyD88 rs7744 was related with an increased risk of CAD, that could be strengthened by the presence of hyperglycemia or hyperlipidemia.

The influence of aortic valve calcification on the risk of periprocedural myocardial injury after elective coronary intervention.

PubMed

Chen, Zhang-Wei; Yang, Hong-Bo; Chen, Ying-Hua; Qian, Ju-Ying; Shu, Xian-Hong; Ge, Jun-Bo

2015-10-01

Aortic valve calcification (AVC) is a common progressive condition that involves several inflammatory and atherosclerotic mediators. However, it is unclear whether the occurrence of periprocedural myocardial injury (PMI) after elective coronary intervention is associated with AVC in stable coronary artery disease (CAD) patients. A total of 530 stable CAD patients who underwent elective coronary intervention were enrolled in this clinical study. High sensitive cardiac troponin T (hs-cTnT) was detected before and after the procedure. PMI was defined as hs-cTnT after coronary intervention higher than 99th percentile upper reference limit (URL). All patients underwent echocardiography to detect the occurrence of AVC. Univariate and multivariate analyses were applied to analyze risk factors of PMI. A total of 210 patients (39.6 %) were diagnosed with PMI after elective coronary intervention. Compared with non-AVC patients (n = 386), AVC patients (n = 144) had higher rate of PMI (64.6 vs. 30.3 %, P < 0.01). CAD patients with AVC had higher Gensini score (39.9 ± 26.6 vs. 34.2 ± 22.1, P < 0.05) and more number of implanted stents (1.7 ± 0.8 vs. 1.5 ± 0.7, P < 0.05). After stratification by classic risk factors of CAD (such as age, male gender and diabetes) in subgroup analyses, we found that AVC patients had increased risk of PMI compared with non-AVC patients. Importantly, even after being adjusted by multivariate analysis, AVC still independently increased the risk of PMI (OR = 3.329, 95 % CI = 2.087-5.308, P < 0.01). AVC significantly increased the risk of PMI after elective coronary intervention. It could be one of the independent predictors for PMI in stable CAD patients.

Computer-aided detection of artificial pulmonary nodules using an ex vivo lung phantom: influence of exposure parameters and iterative reconstruction.

PubMed

Wielpütz, Mark O; Wroblewski, Jacek; Lederlin, Mathieu; Dinkel, Julien; Eichinger, Monika; Koenigkam-Santos, M; Biederer, Jürgen; Kauczor, Hans-Ulrich; Puderbach, Michael U; Jobst, Bertram J

2015-05-01

To evaluate the influence of exposure parameters and raw-data based iterative reconstruction (IR) on the performance of computer-aided detection (CAD) of pulmonary nodules on chest multidetector computed tomography (MDCT). Seven porcine lung explants were inflated in a dedicated ex vivo phantom shell and prepared with n=162 artificial nodules of a clinically relevant volume and maximum diameter (46-1063 μl, and 6.2-21.5 mm). n=118 nodules were solid and n=44 part-solid. MDCT was performed with different combinations of 120 and 80 kV with 120, 60, 30 and 12 mA*s, and reconstructed with both filtered back projection (FBP) and IR. Subsequently, 16 datasets per lung were subjected to dedicated CAD software. The rate of true positive, false negative and false positive CAD marks was measured for each reconstruction. The rate of true positive findings ranged between 88.9-91.4% for FBP and 88.3-90.1% for IR (n.s.) with most exposure settings, but was significantly lower with the combination of 80 kV and 12 mA*s (80.9% and 81.5%, respectively, p<0.05). False positive findings ranged between 2.3-8.1 annotations per lung. For nodule volumes <200 μl the rate of true positives was significantly lower than for >300 μl (p<0.05). Similarly, it was significantly lower for diameters <12 mm compared to ≥12 mm (p<0.05). The rate of true positives for solid and part-solid nodules was similar. Nodule CAD on chest MDCT is robust over a wide range of exposure settings. Noise reduction by IR is not detrimental for CAD, and may be used to improve image quality in the setting of low-dose MDCT for lung cancer screening. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth.

PubMed

Thon, Anika; Teichgräber, Ulf; Tennstedt-Schenk, Cornelia; Hadjidemetriou, Stathis; Winzler, Sven; Malich, Ansgar; Papageorgiou, Ismini

2017-01-01

Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. To assess the performance of Watson Elementary™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies. The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ2 test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation). The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis.

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth

PubMed Central

Thon, Anika; Teichgräber, Ulf; Tennstedt-Schenk, Cornelia; Hadjidemetriou, Stathis; Winzler, Sven; Malich, Ansgar

2017-01-01

Background Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. Aim/Objective To assess the performance of Watson Elementary™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies. Methods The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. Results The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ2 test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation). Conclusion The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis. PMID:29023572

A flexible new method for 3D measurement based on multi-view image sequences

NASA Astrophysics Data System (ADS)

Cui, Haihua; Zhao, Zhimin; Cheng, Xiaosheng; Guo, Changye; Jia, Huayu

2016-11-01

Three-dimensional measurement is the base part for reverse engineering. The paper developed a new flexible and fast optical measurement method based on multi-view geometry theory. At first, feature points are detected and matched with improved SIFT algorithm. The Hellinger Kernel is used to estimate the histogram distance instead of traditional Euclidean distance, which is immunity to the weak texture image; then a new filter three-principle for filtering the calculation of essential matrix is designed, the essential matrix is calculated using the improved a Contrario Ransac filter method. One view point cloud is constructed accurately with two view images; after this, the overlapped features are used to eliminate the accumulated errors caused by added view images, which improved the camera's position precision. At last, the method is verified with the application of dental restoration CAD/CAM, experiment results show that the proposed method is fast, accurate and flexible for tooth 3D measurement.

Ultimate Limit to the Spatial Resolution in Magnetic Imaging

NASA Astrophysics Data System (ADS)

Matthews, John; Wellstood, Frederick C.; Chatraphorn, Sojiphong

2003-03-01

Motivated by the continual improvement in the spatial resolution of source currents detected by magnetic field imaging, in particular scanning SQUID microscopy, we have determined a theoretical limit to the spatial resolution for a given set of parameters. The guiding principle here is that by adding known information (e.g. CAD diagram) about the source currents into the inversion algorithm, we reduce the number of unknown parameters and hence lower the uncertainty in the remaining parameters. We consider the ultimate limit to be the case where all the information about the system is known, except for a single parameter, e.g. the separation w of two long, straight wires each carrying a current I/2. For this particular example we find that for a current I=100;μA, with magnetic field noise Δ B=10 pT, at a standoff z=100;μm, the minimum resolvable separation is 2;μm, about an order of magnitude less than the present limit.

Orion Active Thermal Control System Dynamic Modeling Using Simulink/MATLAB

NASA Technical Reports Server (NTRS)

Wang, Xiao-Yen J.; Yuko, James

2010-01-01

This paper presents dynamic modeling of the crew exploration vehicle (Orion) active thermal control system (ATCS) using Simulink (Simulink, developed by The MathWorks). The model includes major components in ATCS, such as heat exchangers and radiator panels. The mathematical models of the heat exchanger and radiator are described first. Four different orbits were used to validate the radiator model. The current model results were compared with an independent Thermal Desktop (TD) (Thermal Desktop, PC/CAD-based thermal model builder, developed in Cullimore & Ring (C&R) Technologies) model results and showed good agreement for all orbits. In addition, the Orion ATCS performance was presented for three orbits and the current model results were compared with three sets of solutions- FloCAD (FloCAD, PC/CAD-based thermal/fluid model builder, developed in C&R Technologies) model results, SINDA/FLUINT (SINDA/FLUINT, a generalized thermal/fluid network-style solver ) model results, and independent Simulink model results. For each case, the fluid temperatures at every component on both the crew module and service module sides were plotted and compared. The overall agreement is reasonable for all orbits, with similar behavior and trends for the system. Some discrepancies exist because the control algorithm might vary from model to model. Finally, the ATCS performance for a 45-hr nominal mission timeline was simulated to demonstrate the capability of the model. The results show that the ATCS performs as expected and approximately 2.3 lb water was consumed in the sublimator within the 45 hr timeline before Orion docked at the International Space Station.

Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry.

PubMed

van Rosendael, Alexander R; Maliakal, Gabriel; Kolli, Kranthi K; Beecy, Ashley; Al'Aref, Subhi J; Dwivedi, Aeshita; Singh, Gurpreet; Panday, Mohit; Kumar, Amit; Ma, Xiaoyue; Achenbach, Stephan; Al-Mallah, Mouaz H; Andreini, Daniele; Bax, Jeroen J; Berman, Daniel S; Budoff, Matthew J; Cademartiri, Filippo; Callister, Tracy Q; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin J W; Cury, Ricardo C; DeLago, Augustin; Feuchtner, Gudrun; Hadamitzky, Martin; Hausleiter, Joerg; Kaufmann, Philipp A; Kim, Yong-Jin; Leipsic, Jonathon A; Maffei, Erica; Marques, Hugo; Pontone, Gianluca; Raff, Gilbert L; Rubinshtein, Ronen; Shaw, Leslee J; Villines, Todd C; Gransar, Heidi; Lu, Yao; Jones, Erica C; Peña, Jessica M; Lin, Fay Y; Min, James K

Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores. From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data). In total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P < 0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events). A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification. Published by Elsevier Inc.

Incorporation of composite defects from ultrasonic NDE into CAD and FE models

NASA Astrophysics Data System (ADS)

Bingol, Onur Rauf; Schiefelbein, Bryan; Grandin, Robert J.; Holland, Stephen D.; Krishnamurthy, Adarsh

2017-02-01

Fiber-reinforced composites are widely used in aerospace industry due to their combined properties of high strength and low weight. However, owing to their complex structure, it is difficult to assess the impact of manufacturing defects and service damage on their residual life. While, ultrasonic testing (UT) is the preferred NDE method to identify the presence of defects in composites, there are no reasonable ways to model the damage and evaluate the structural integrity of composites. We have developed an automated framework to incorporate flaws and known composite damage automatically into a finite element analysis (FEA) model of composites, ultimately aiding in accessing the residual life of composites and make informed decisions regarding repairs. The framework can be used to generate a layer-by-layer 3D structural CAD model of the composite laminates replicating their manufacturing process. Outlines of structural defects, such as delaminations, are automatically detected from UT of the laminate and are incorporated into the CAD model between the appropriate layers. In addition, the framework allows for direct structural analysis of the resulting 3D CAD models with defects by automatically applying the appropriate boundary conditions. In this paper, we show a working proof-of-concept for the composite model builder with capabilities of incorporating delaminations between laminate layers and automatically preparing the CAD model for structural analysis using a FEA software.

TinkerCell: modular CAD tool for synthetic biology.

PubMed

Chandran, Deepak; Bergmann, Frank T; Sauro, Herbert M

2009-10-29

Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. Using a CAD application, it would be possible to construct models using available biological "parts" and directly generate the DNA sequence that represents the model, thus increasing the efficiency of design and construction of synthetic networks. An application named TinkerCell has been developed in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various third-party C and Python programs that are hosted by TinkerCell via an extensive C and Python application programming interface (API). TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at http://www.tinkercell.com. An ideal CAD application for engineering biological systems would provide features such as: building and simulating networks, analyzing robustness of networks, and searching databases for components that meet the design criteria. At the current state of synthetic biology, there are no established methods for measuring robustness or identifying components that fit a design. The same is true for databases of biological parts. TinkerCell's flexible modeling framework allows it to cope with changes in the field. Such changes may involve the way parts are characterized or the way synthetic networks are modeled and analyzed computationally. TinkerCell can readily accept third-party algorithms, allowing it to serve as a platform for testing different methods relevant to synthetic biology.

TinkerCell: modular CAD tool for synthetic biology

PubMed Central

Chandran, Deepak; Bergmann, Frank T; Sauro, Herbert M

2009-01-01

Background Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. Using a CAD application, it would be possible to construct models using available biological "parts" and directly generate the DNA sequence that represents the model, thus increasing the efficiency of design and construction of synthetic networks. Results An application named TinkerCell has been developed in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various third-party C and Python programs that are hosted by TinkerCell via an extensive C and Python application programming interface (API). TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at . Conclusion An ideal CAD application for engineering biological systems would provide features such as: building and simulating networks, analyzing robustness of networks, and searching databases for components that meet the design criteria. At the current state of synthetic biology, there are no established methods for measuring robustness or identifying components that fit a design. The same is true for databases of biological parts. TinkerCell's flexible modeling framework allows it to cope with changes in the field. Such changes may involve the way parts are characterized or the way synthetic networks are modeled and analyzed computationally. TinkerCell can readily accept third-party algorithms, allowing it to serve as a platform for testing different methods relevant to synthetic biology. PMID:19874625

Automated ultrasound edge-tracking software comparable to established semi-automated reference software for carotid intima-media thickness analysis.

PubMed

Shenouda, Ninette; Proudfoot, Nicole A; Currie, Katharine D; Timmons, Brian W; MacDonald, Maureen J

2018-05-01

Many commercial ultrasound systems are now including automated analysis packages for the determination of carotid intima-media thickness (cIMT); however, details regarding their algorithms and methodology are not published. Few studies have compared their accuracy and reliability with previously established automated software, and those that have were in asymptomatic adults. Therefore, this study compared cIMT measures from a fully automated ultrasound edge-tracking software (EchoPAC PC, Version 110.0.2; GE Medical Systems, Horten, Norway) to an established semi-automated reference software (Artery Measurement System (AMS) II, Version 1.141; Gothenburg, Sweden) in 30 healthy preschool children (ages 3-5 years) and 27 adults with coronary artery disease (CAD; ages 48-81 years). For both groups, Bland-Altman plots revealed good agreement with a negligible mean cIMT difference of -0·03 mm. Software differences were statistically, but not clinically, significant for preschool images (P = 0·001) and were not significant for CAD images (P = 0·09). Intra- and interoperator repeatability was high and comparable between software for preschool images (ICC, 0·90-0·96; CV, 1·3-2·5%), but slightly higher with the automated ultrasound than the semi-automated reference software for CAD images (ICC, 0·98-0·99; CV, 1·4-2·0% versus ICC, 0·84-0·89; CV, 5·6-6·8%). These findings suggest that the automated ultrasound software produces valid cIMT values in healthy preschool children and adults with CAD. Automated ultrasound software may be useful for ensuring consistency among multisite research initiatives or large cohort studies involving repeated cIMT measures, particularly in adults with documented CAD. © 2017 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Methodologies for Development of Patient Specific Bone Models from Human Body CT Scans

NASA Astrophysics Data System (ADS)

Chougule, Vikas Narayan; Mulay, Arati Vinayak; Ahuja, Bharatkumar Bhagatraj

2016-06-01

This work deals with development of algorithm for physical replication of patient specific human bone and construction of corresponding implants/inserts RP models by using Reverse Engineering approach from non-invasive medical images for surgical purpose. In medical field, the volumetric data i.e. voxel and triangular facet based models are primarily used for bio-modelling and visualization, which requires huge memory space. On the other side, recent advances in Computer Aided Design (CAD) technology provides additional facilities/functions for design, prototyping and manufacturing of any object having freeform surfaces based on boundary representation techniques. This work presents a process to physical replication of 3D rapid prototyping (RP) physical models of human bone from various CAD modeling techniques developed by using 3D point cloud data which is obtained from non-invasive CT/MRI scans in DICOM 3.0 format. This point cloud data is used for construction of 3D CAD model by fitting B-spline curves through these points and then fitting surface between these curve networks by using swept blend techniques. This process also can be achieved by generating the triangular mesh directly from 3D point cloud data without developing any surface model using any commercial CAD software. The generated STL file from 3D point cloud data is used as a basic input for RP process. The Delaunay tetrahedralization approach is used to process the 3D point cloud data to obtain STL file. CT scan data of Metacarpus (human bone) is used as the case study for the generation of the 3D RP model. A 3D physical model of the human bone is generated on rapid prototyping machine and its virtual reality model is presented for visualization. The generated CAD model by different techniques is compared for the accuracy and reliability. The results of this research work are assessed for clinical reliability in replication of human bone in medical field.

Cardiac magnetic resonance imaging for the diagnosis of coronary artery disease: an evidence-based analysis.

PubMed

2010-01-01

In July 2009, the Medical Advisory Secretariat (MAS) began work on Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease (CAD), an evidence-based review of the literature surrounding different cardiac imaging modalities to ensure that appropriate technologies are accessed by patients suspected of having CAD. This project came about when the Health Services Branch at the Ministry of Health and Long-Term Care asked MAS to provide an evidentiary platform on effectiveness and cost-effectiveness of non-invasive cardiac imaging modalities.After an initial review of the strategy and consultation with experts, MAS identified five key non-invasive cardiac imaging technologies for the diagnosis of CAD. Evidence-based analyses have been prepared for each of these five imaging modalities: cardiac magnetic resonance imaging, single photon emission computed tomography, 64-slice computed tomographic angiography, stress echocardiography, and stress echocardiography with contrast. For each technology, an economic analysis was also completed (where appropriate). A summary decision analytic model was then developed to encapsulate the data from each of these reports (available on the OHTAC and MAS website).The Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease series is made up of the following reports, which can be publicly accessed at the MAS website at: www.health.gov.on.ca/mas or at www.health.gov.on.ca/english/providers/program/mas/mas_about.htmlSINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based AnalysisSTRESS ECHOCARDIOGRAPHY FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based AnalysisSTRESS ECHOCARDIOGRAPHY WITH CONTRAST FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based Analysis64-Slice Computed Tomographic Angiography for the Diagnosis of Coronary Artery Disease: An Evidence-Based AnalysisCARDIAC MAGNETIC RESONANCE IMAGING FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based AnalysisPease note that two related evidence-based analyses of non-invasive cardiac imaging technologies for the assessment of myocardial viability are also available on the MAS website:POSITRON EMISSION TOMOGRAPHY FOR THE ASSESSMENT OF MYOCARDIAL VIABILITY: An Evidence-Based AnalysisMAGNETIC RESONANCE IMAGING FOR THE ASSESSMENT OF MYOCARDIAL VIABILITY: an Evidence-Based AnalysisThe Toronto Health Economics and Technology Assessment Collaborative has also produced an associated economic report entitled:The Relative Cost-effectiveness of Five Non-invasive Cardiac Imaging Technologies for Diagnosing Coronary Artery Disease in Ontario [Internet]. Available from: http://theta.utoronto.ca/reports/?id=7 OBJECTIVE: The objective of this analysis was to determine the diagnostic accuracy of cardiac magnetic resonance imaging (MRI) for the diagnosis of patients with known/suspected coronary artery disease (CAD) compared to coronary angiography. Stress cardiac MRI is a non-invasive, x-ray free imaging technique that takes approximately 30 to 45 minutes to complete and can be performed using to two different methods, a) perfusion imaging following a first pass of an intravenous bolus of gadolinium contrast, or b) wall motion imaging. Stress is induced pharmacologically with either dobutamine, dipyridamole, or adenosine, as physical exercise is difficult to perform within the magnet bore and often induces motion artifacts. Alternatives to stress cardiac perfusion MRI include stress single-photon emission computed tomography (SPECT) and stress echocardiography (ECHO). The advantage of cardiac MRI is that it does not pose the radiation burden associated with SPECT. During the same sitting, cardiac MRI can also assess left and right ventricular dimensions, viability, and cardiac mass. It may also mitigate the need for invasive diagnostic coronary angiography in patients with intermediate risk factors for CAD. EVIDENCE-BASED ANALYSIS: A literature search was performed on October 9, 2009 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published from January 1, 2005 to October 9, 2008. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria, full-text articles were obtained. Reference lists were also examined for any relevant studies not identified through the search. Articles with unknown eligibility were reviewed with a second clinical epidemiologist and then a group of epidemiologists until consensus was established. The quality of evidence was assessed as high, moderate, low or very low according to GRADE methodology. Given the large amount of clinical heterogeneity of the articles meeting the inclusion criteria, as well as suggestions from an Expert Advisory Panel Meeting held on October 5, 2009, the inclusion criteria were revised to examine the effectiveness of cardiac MRI for the detection of CAD. Inclusion CriteriaExclusion CriteriaHeath technology assessments, systematic reviews, randomized controlled trials, observational studies≥20 adult patients enrolled.Published 2004-2009Licensed by Health CanadaFor diagnosis of CAD:Reference standard is coronary angiographySignificant CAD defined as ≥ 50% coronary stenosisPatients with suspected or known CADReported results by patient, not segmentNon-English studiesGrey literaturePlanar imagingMUGAPatients with recent MI (i.e., within 1 month)Patients with non-ischemic heart diseaseStudies done exclusively in special populations (e.g., women, diabetics) Sensitivity and specificityArea under the curve (AUC)Diagnostic odds ratio (DOR) SUMMARY OF FINDINGS: Stress cardiac MRI using perfusion analysis yielded a pooled sensitivity of 0.91 (95% CI: 0.89 to 0.92) and specificity of 0.79 (95% CI: 0.76 to 0.82) for the detection of CAD.Stress cardiac MRI using wall motion analysis yielded a pooled sensitivity of 0.81 (95% CI: 0.77 to 0.84) and specificity of 0.85 (95% CI: 0.81 to 0.89) for the detection of CAD.Based on DORs, there was no significant difference between pooled stress cardiac MRI using perfusion analysis and pooled stress cardiac MRI using wall motion analysis (P=0.26) for the detection of CAD.Pooled subgroup analysis of stress cardiac MRI using perfusion analysis showed no significant difference in the DORs between 1.5T and 3T MRI (P=0.72) for the detection of CAD.One study (N=60) was identified that examined stress cardiac MRI using wall motion analysis with a 3T MRI. The sensitivity and specificity of 3T MRI were 0.64 (95% CI: 0.44 to 0.81) and 1.00 (95% CI: 0.89 to 1.00), respectively, for the detection of CAD.The effectiveness of stress cardiac MRI for the detection of CAD in unstable patients with acute coronary syndrome was reported in only one study (N=35). Using perfusion analysis, the sensitivity and specificity were 0.72 (95% CI: 0.53 to 0.87) and 1.00 (95% CI: 0.54 to 1.00), respectively, for the detection of CAD. According to an expert consultant, in Ontario: Stress first pass perfusion is currently performed in small numbers in London (London Health Sciences Centre) and Toronto (University Health Network at the Toronto General Hospital site and Sunnybrook Health Sciences Centre).Stress wall motion is only performed as part of research protocols and not very often.Cardiac MRI machines use 1.5T almost exclusively, with 3T used in research for first pass perfusion.On November 25 2009, the Cardiac Imaging Expert Advisory Panel met and made the following comments about stress cardiac MRI for perfusion analysis: Accessibility to cardiac MRI is limited and generally used to assess structural abnormalities. Most MRIs in Ontario are already in 24-hour, constant use and it would thus be difficult to add cardiac MRI for CAD diagnosis as an additional indication.The performance of cardiac MRI for the diagnosis of CAD can be technically challenging. The quality of the body of evidence was assessed according to the GRADE Working Group criteria for diagnostic tests. For perfusion analysis, the overall quality was determined to be low and for wall motion analysis the overall quality was very low.

Comparative utility of gated myocardial perfusion imaging and transthoracic coronary flow reserve for the assessment of coronary artery disease in patients with left bundle branch block.

PubMed

Pavlovic, Smiljana; Sobic-Saranovic, Dragana; Djordjevic-Dikic, Ana; Beleslin, Branko; Stepanovic, Jelena; Artiko, Vera; Giga, Vojislav; Petrasinovic, Zorica; Ostojic, Miodrag; Vujisic-Tesic, Bosiljka; Obradovic, Vladimir

2010-04-01

To compare the diagnostic utility of gated single-photon emission computed tomography (SPECT) methoxy isobutyl isonitrile (MIBI) myocardial perfusion imaging and transthoracic Doppler echocardiography (TTDE) coronary flow reserve (CFR) to coronary angiography for detecting coronary artery disease (CAD) in patients with left bundle branch block (LBBB). Forty-three patients with complete LBBB and an intermediate pretest probability for CAD underwent dipyridamole stress TTDE and gated SPECT MIBI during the same session and coronary angiography within a month. The parameters of myocardial perfusion (summed stress score, summed difference scores) regional wall function (wall motion score, wall thickening score) and ejection fraction were derived using the 17-segment model and 4D-MSPECT software. TTDE variables included peak flow velocity at rest and during hyperemia in left anterior descending artery (LAD), based on which CFR was calculated (normal>2). Perfusion ischemic scores were significantly higher in group 1 with angiographic evidence of greater than 50% LAD stenosis compared with group 2 with less than 50% LAD stenosis (summed stress score 12.4+/-5.5 vs. 8.3+/-3.5, P<0.05, summed difference score 3.7+/-1.2 vs. 1.1+/-0.3, P<0.01, respectively). Left ventricular regional wall function and ejection fraction were not different between the two groups. CFR was significantly lower in group 1 than in group 2 (1.65+/-0.21 vs. 2.31+/-0.28, P<0.001). Gated SPECT MIBI and CFR had similar sensitivity (88 vs. 88%), specificity (80 vs. 84%), and accuracy (84 vs. 86%) for detecting CAD in patients with LBBB. The agreement between the two methods was 85%. Our results show comparable diagnostic utility and high agreement between gated SPECT MIBI perfusion imaging and TTDE CFR assessment for detecting CAD in patients with LBBB. The advantage of gated SPECT MIBI over TTDE CFR measurements is the ability to assess the perfusion abnormalities in multiple vascular territories during the same procedure, which is convenient for detecting multi-vessel disease in patients with LBBB.

A simplified guide for charged aerosol detection of non-chromophoric compounds-Analytical method development and validation for the HPLC assay of aerosol particle size distribution for amikacin.

PubMed

Soliven, Arianne; Haidar Ahmad, Imad A; Tam, James; Kadrichu, Nani; Challoner, Pete; Markovich, Robert; Blasko, Andrei

2017-09-05

Amikacin, an aminoglycoside antibiotic lacking a UV chromophore, was developed into a drug product for delivery by inhalation. A robust method for amikacin assay analysis and aerosol particle size distribution (aPSD) determination, with comparable performance to the conventional UV detector was developed using a charged aerosol detector (CAD). The CAD approach involved more parameters for optimization than UV detection due to its sensitivity to trace impurities, non-linear response and narrow dynamic range of signal versus concentration. Through careful selection of the power transformation function value and evaporation temperature, a wider linear dynamic range, improved signal-to-noise ratio and high repeatability were obtained. The influences of mobile phase grade and glassware binding of amikacin during sample preparation were addressed. A weighed (1/X 2 ) least square regression was used for the calibration curve. The limit of quantitation (LOQ) and limit of detection (LOD) for this method were determined to be 5μg/mL and 2μg/mL, respectively. The method was validated over a concentration range of 0.05-2mg/mL. The correlation coefficient for the peak area versus concentration was 1.00 and the y-intercept was 0.2%. The recovery accuracies of triplicate preparations at 0.05, 1.0, and 2.0mg/mL were in the range of 100-101%. The relative standard deviation (S rel ) of six replicates at 1.0mg/mL was 1%, and S rel of five injections at the limit of quantitation was 4%. A robust HPLC-CAD method was developed and validated for the determination of the aPSD for amikacin. The CAD method development produced a simplified procedure with minimal variability in results during: routine operation, transfer from one instrument to another, and between different analysts. Copyright © 2017 Elsevier B.V. All rights reserved.

Utilizing optical coherence tomography for CAD/CAM of indirect dental restorations

NASA Astrophysics Data System (ADS)

Chityala, Ravishankar; Vidal, Carola; Jones, Robert

Optical Coherence Tomography (OCT) has seen broad application in dentistry including early carious lesion detection and imaging defects in resin composite restorations. This study investigates expanding the clinical usefulness by investigating methods to use OCT for obtaining three-dimensional (3D) digital impressions, which can be integrated to CAD/CAM manufacturing of indirect restorations. 3D surface topography `before' and `after' a cavity preparation was acquired by an intraoral cross polarization swept source OCT (CP-OCT) system with a Micro-Electro-Mechanical System (MEMS) scanning mirror. Image registration and segmentation methods were used to digitally construct a replacement restoration that modeled the original surface morphology of a hydroxyapatite sample. After high resolution additive manufacturing (e.g. polymer 3D printing) of the replacement restoration, micro-CT imaging was performed to examine the marginal adaptation. This study establishes the protocol for further investigation of integrating OCT with CAD/CAM of indirect dental restorations.

Implementation Research to Inform the Use of Xpert MTB/RIF in Primary Health Care Facilities in High TB and HIV Settings in Resource Constrained Settings

PubMed Central

Muyoyeta, Monde; Moyo, Maureen; Kasese, Nkatya; Ndhlovu, Mapopa; Milimo, Deborah; Mwanza, Winfridah; Kapata, Nathan; Schaap, Albertus; Godfrey Faussett, Peter; Ayles, Helen

2015-01-01

Background The current cost of Xpert MTB RIF (Xpert) consumables is such that algorithms are needed to select which patients to prioritise for testing with Xpert. Objective To evaluate two algorithms for prioritisation of Xpert in primary health care settings in a high TB and HIV burden setting. Method Consecutive, presumptive TB patients with a cough of any duration were offered either Xpert or Fluorescence microscopy (FM) test depending on their CXR score or HIV status. In one facility, sputa from patients with an abnormal CXR were tested with Xpert and those with a normal CXR were tested with FM (“CXR algorithm”). CXR was scored automatically using a Computer Aided Diagnosis (CAD) program. In the other facility, patients who were HIV positive were tested using Xpert and those who were HIV negative were tested with FM (“HIV algorithm”). Results Of 9482 individuals pre-screened with CXR, Xpert detected TB in 2090/6568 (31.8%) with an abnormal CXR, and FM was AFB positive in 8/2455 (0.3%) with a normal CXR. Of 4444 pre-screened with HIV, Xpert detected TB in 508/2265 (22.4%) HIV positive and FM was AFB positive in 212/1920 (11.0%) in HIV negative individuals. The notification rate of new bacteriologically confirmed TB increased; from 366 to 620/ 100,000/yr and from 145 to 261/100,000/yr at the CXR and HIV algorithm sites respectively. The median time to starting TB treatment at the CXR site compared to the HIV algorithm site was; 1(IQR 1-3 days) and 3 (2-5 days) (p<0.0001) respectively. Conclusion Use of Xpert in a resource-limited setting at primary care level in conjunction with pre-screening tests reduced the number of Xpert tests performed. The routine use of Xpert resulted in additional cases of confirmed TB patients starting treatment. However, there was no increase in absolute numbers of patients starting TB treatment. Same day diagnosis and treatment commencement was achieved for both bacteriologically confirmed and empirically diagnosed patients where Xpert was used in conjunction with CXR. PMID:26030301

Design Evaluation for Personnel, Training and Human Factors (DEPTH) Final Report.

DTIC Science & Technology

1998-01-17

human activity was primarily intended to facilitate man-machine design analyses of complex systems. By importing computer aided design (CAD) data, the human figure models and analysis algorithms can help to ensure components can be seen, reached, lifted and removed by most maintainers. These simulations are also useful for logistics data capture, training, and task analysis. DEPTH was also found to be useful in obtaining task descriptions for technical

VLSI Architectures and CAD

DTIC Science & Technology

1989-11-01

considerable promise is a variation of the familiar Lempel - Ziv adaptive data compression scheme that permits a straightforward mapping to hardware...types of data . The UNIX " compress " implementation is based upon Terry Welch’s 1984 variation of the Lempel - Ziv method (LZW). One flaw lies in the fact...or more; it must effec- tively compress all types of data (i.e. the algorithm must be universal); the implementation must be contained within a small

A revised definition of the metabolic syndrome predicts coronary artery disease and ischemic stroke after adjusting for low density lipoprotein cholesterol in a 13-year cohort study of Japanese: the Suita study.

PubMed

Okamura, Tomonori; Kokubo, Yoshihiro; Watanabe, Makoto; Higashiyama, Aya; Ono, Yuu; Nishimura, Kunihiro; Okayama, Akira; Miyamoto, Yoshihiro

2011-07-01

Recently, several major organizations have proposed a unified definition for the metabolic syndrome (MetS), which should be evaluated in multiethnic groups. The effect of Mets on the incidence of cardiovascular disease needs to be assessed after adjusting for serum low density lipoprotein cholesterol (LDLC), a major risk factor for atherosclerotic diseases. This is especially needed to be evaluated in Asian populations with low incidence of coronary artery disease (CAD). We conducted a 13-year prospective study of 4939 Japanese living in an urban area. The MetS was defined using a unified classification that included cut-off points for waist circumference in Asians. The multivariable adjusted hazard ratios (HRs) of MetS for CAD and stroke were calculated using a Cox proportional model adjusted for other potential confounding factors with LDLC. During the follow-up period, there were 155 cases of CAD and 204 of stroke including 118 cerebral infarctions. In participants under 65 years old, the multivariable HRs of MetS for CAD were 1.21 (95% C.I., 0.64-2.28) in men and 4.44 (95% C.I., 1.73-11.4) in women; the HRs for ischemic stroke were 3.24 (95% C.I., 1.55-6.77) in men and 3.99 (95% C.I., 1.34-11.8) in women. In participants aged 65 years old and over, MetS only showed a significant association with CAD in men (HR 1.89, 95% C.I., 1.11-3.21). Serum LDLC was associated with increased risk of CAD in men irrespective of age group; however, it was not associated with CAD in women. There was no association between serum LDLC and ischemic stroke in any group stratified by sex and the age of 65. These results indicate that the new uniform MetS definition is useful for detecting high risk individuals, especially for middle-aged population. However, continuous screening for hypercholesterolemia is necessary to prevent CAD, especially in men, even in Asian countries such as Japan. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Non-localization and localization ROC analyses using clinically based scoring

NASA Astrophysics Data System (ADS)

Paquerault, Sophie; Samuelson, Frank W.; Myers, Kyle J.; Smith, Robert C.

2009-02-01

We are investigating the potential for differences in study conclusions when assessing the estimated impact of a computer-aided detection (CAD) system on readers' performance. The data utilized in this investigation were derived from a multi-reader multi-case observer study involving one hundred mammographic background images to which fixed-size and fixed-intensity Gaussian signals were added, generating a low- and high-intensity signal sets. The study setting allowed CAD assessment in two situations: when CAD sensitivity was 1) superior or 2) lower than the average reader. Seven readers were asked to review each set in the unaided and CAD-aided reading modes, mark and rate their findings. Using this data, we studied the effect on study conclusion of three clinically-based receiver operating characteristic (ROC) scoring definitions. These scoring definitions included both location-specific and non-location-specific rules. The results showed agreement in the estimated impact of CAD on the overall reader performance. In the study setting where CAD sensitivity is superior to the average reader, the mean difference in AUC between the CAD-aided read and unaided read was 0.049 (95%CIs: -0.027; 0.130) for the image scoring definition that is based on non-location-specific rules, and 0.104 (95%CIs: 0.036; 0.174) and 0.090 (95%CIs: 0.031; 0.155) for image scoring definitions that are based on location-specific rules. The increases in AUC were statistically significant for the location-specific scoring definitions. It was further observed that the variance on these estimates was reduced when using the location-specific scoring definitions compared to that using a non-location-specific scoring definition. In the study setting where CAD sensitivity is equivalent or lower than the average reader, the mean differences in AUC are slightly above 0.01 for all image scoring definitions. These increases in AUC were not statistical significant for any of the image scoring definitions. The results on the variance analysis differed from those observed in the other study setting. This investigation furthers our understanding of the relationships between non-localization-specific and localization-specific ROC assessment methodologies and their relevance to clinical practice.

Left ventricular mass, geometry and function in diabetic patients affected by coronary artery disease.

PubMed

Maiello, Maria; Zito, Annapaola; Carbonara, Santa; Ciccone, Marco Matteo; Palmiero, Pasquale

2017-10-01

Coronary artery disease (CAD) is quite common among diabetic patients, our study goal is to detect the prevalence of left ventricular (LV) adverse changes in geometry, mass and diastolic function on diabetic, but not hypertensive patients, with coronary artery disease(CAD) and LV ejection fraction(LVEF)>45%, actually unknown, because of current guidelines that do not include echocardiographic assessment for follow up of diabetic patients. 665 consecutive diabetic patients (443 females, mean age 66±9years), performed a complete echocardiographic assessment according to current ASE echo-guidelines: diastolic dysfunction (DD), eccentric hypertrophy (EH), concentric hypertrophy (CH) and concentric remodeling (CR) of LV were reported. CAD was assessed only by reports of bypass surgery, angioplasty or patients hospitalized for acute myocardial infarction. 218 patients (32.8%) presented LV changes: LVDD 49 (7.4%), LVEH 68 (10.2%), LVDD and EH 46 (6.9%), LVDD and CH 36 (5.4%), LVDD and CR 19 (2.9%). 447 (67.2%) had no LV changes. 81 (12.1%) patients with CAD, presented: LVDD 17 (21%), LVEH 32 (39.5%), LVDD and EH 9 (11.1%), LVDD and CH 7 (8.6%), LVDD and CR 8 (9.9%), 8 (9.9%) had no LV adverse changes. There were among CAD patients, a significantly higher prevalence of LVDD (p<0.02), LV eccentric hypertrophy (EH) (p<0.05), DD and LVEH (p<0.04), DD and LV concentric hypertrophy(CH) (p<0.03) and DD and LV concentric remodeling (p<0.02), when compared with those patients without CAD. CAD is related to all different patterns of LV adverse changes in mass, geometry and diastolic function, with a significantly higher prevalence in our population of diabetic patients with normal systolic function. These changes however remain unrecognized until they undergo to a conventional echocardiographic assessment. We support this tool need to be included into future guidelines concerning follow-up of diabetic patients. Copyright © 2017 Elsevier Inc. All rights reserved.