Proceedings of the international conference on cybernetics and societ

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

Not Available

1985-01-01

This book presents the papers given at a conference on artificial intelligence, expert systems and knowledge bases. Topics considered at the conference included automating expert system development, modeling expert systems, causal maps, data covariances, robot vision, image processing, multiprocessors, parallel processing, VLSI structures, man-machine systems, human factors engineering, cognitive decision analysis, natural language, computerized control systems, and cybernetics.

Improved operator agreement and efficiency using the minimum area contour change method for delineation of hyperintense multiple sclerosis lesions on FLAIR MRI

PubMed Central

2013-01-01

Background Activity of disease in patients with multiple sclerosis (MS) is monitored by detecting and delineating hyper-intense lesions on MRI scans. The Minimum Area Contour Change (MACC) algorithm has been created with two main goals: a) to improve inter-operator agreement on outlining regions of interest (ROIs) and b) to automatically propagate longitudinal ROIs from the baseline scan to a follow-up scan. Methods The MACC algorithm first identifies an outer bound for the solution path, forms a high number of iso-contour curves based on equally spaced contour values, and then selects the best contour value to outline the lesion. The MACC software was tested on a set of 17 FLAIR MRI images evaluated by a pair of human experts and a longitudinal dataset of 12 pairs of T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images that had lesion analysis ROIs drawn by a single expert operator. Results In the tests where two human experts evaluated the same MRI images, the MACC program demonstrated that it could markedly reduce inter-operator outline error. In the longitudinal part of the study, the MACC program created ROIs on follow-up scans that were in close agreement to the original expert’s ROIs. Finally, in a post-hoc analysis of 424 follow-up scans 91% of propagated MACC were accepted by an expert and only 9% of the final accepted ROIS had to be created or edited by the expert. Conclusion When used with an expert operator's verification of automatically created ROIs, MACC can be used to improve inter- operator agreement and decrease analysis time, which should improve data collected and analyzed in multicenter clinical trials. PMID:24004511

Adaptive segmentation of nuclei in H&S stained tendon microscopy

NASA Astrophysics Data System (ADS)

Chuang, Bo-I.; Wu, Po-Ting; Hsu, Jian-Han; Jou, I.-Ming; Su, Fong-Chin; Sun, Yung-Nien

2015-12-01

Tendiopathy is a popular clinical issue in recent years. In most cases like trigger finger or tennis elbow, the pathology change can be observed under H and E stained tendon microscopy. However, the qualitative analysis is too subjective and thus the results heavily depend on the observers. We develop an automatic segmentation procedure which segments and counts the nuclei in H and E stained tendon microscopy fast and precisely. This procedure first determines the complexity of images and then segments the nuclei from the image. For the complex images, the proposed method adopts sampling-based thresholding to segment the nuclei. While for the simple images, the Laplacian-based thresholding is employed to re-segment the nuclei more accurately. In the experiments, the proposed method is compared with the experts outlined results. The nuclei number of proposed method is closed to the experts counted, and the processing time of proposed method is much faster than the experts'.

Acral melanoma detection using a convolutional neural network for dermoscopy images.

PubMed

Yu, Chanki; Yang, Sejung; Kim, Wonoh; Jung, Jinwoong; Chung, Kee-Yang; Lee, Sang Wook; Oh, Byungho

2018-01-01

Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation. The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert's evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden's index like 0.6795, 0.6073, which were similar score with the expert. Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.

Intra- and inter-rater reliability of digital image analysis for skin color measurement

PubMed Central

Sommers, Marilyn; Beacham, Barbara; Baker, Rachel; Fargo, Jamison

2013-01-01

Background We determined the intra- and inter-rater reliability of data from digital image color analysis between an expert and novice analyst. Methods Following training, the expert and novice independently analyzed 210 randomly ordered images. Both analysts used Adobe® Photoshop lasso or color sampler tools based on the type of image file. After color correction with Pictocolor® in camera software, they recorded L*a*b* (L*=light/dark; a*=red/green; b*=yellow/blue) color values for all skin sites. We computed intra-rater and inter-rater agreement within anatomical region, color value (L*, a*, b*), and technique (lasso, color sampler) using a series of one-way intra-class correlation coefficients (ICCs). Results Results of ICCs for intra-rater agreement showed high levels of internal consistency reliability within each rater for the lasso technique (ICC ≥ 0.99) and somewhat lower, yet acceptable, level of agreement for the color sampler technique (ICC = 0.91 for expert, ICC = 0.81 for novice). Skin L*, skin b*, and labia L* values reached the highest level of agreement (ICC ≥ 0.92) and skin a*, labia b*, and vaginal wall b* were the lowest (ICC ≥ 0.64). Conclusion Data from novice analysts can achieve high levels of agreement with data from expert analysts with training and the use of a detailed, standard protocol. PMID:23551208

Intra- and inter-rater reliability of digital image analysis for skin color measurement.

PubMed

Sommers, Marilyn; Beacham, Barbara; Baker, Rachel; Fargo, Jamison

2013-11-01

We determined the intra- and inter-rater reliability of data from digital image color analysis between an expert and novice analyst. Following training, the expert and novice independently analyzed 210 randomly ordered images. Both analysts used Adobe(®) Photoshop lasso or color sampler tools based on the type of image file. After color correction with Pictocolor(®) in camera software, they recorded L*a*b* (L*=light/dark; a*=red/green; b*=yellow/blue) color values for all skin sites. We computed intra-rater and inter-rater agreement within anatomical region, color value (L*, a*, b*), and technique (lasso, color sampler) using a series of one-way intra-class correlation coefficients (ICCs). Results of ICCs for intra-rater agreement showed high levels of internal consistency reliability within each rater for the lasso technique (ICC ≥ 0.99) and somewhat lower, yet acceptable, level of agreement for the color sampler technique (ICC = 0.91 for expert, ICC = 0.81 for novice). Skin L*, skin b*, and labia L* values reached the highest level of agreement (ICC ≥ 0.92) and skin a*, labia b*, and vaginal wall b* were the lowest (ICC ≥ 0.64). Data from novice analysts can achieve high levels of agreement with data from expert analysts with training and the use of a detailed, standard protocol. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

An inter-observer agreement study of autofluorescence endoscopy in Barrett's esophagus among expert and non-expert endoscopists.

PubMed

Mannath, J; Subramanian, V; Telakis, E; Lau, K; Ramappa, V; Wireko, M; Kaye, P V; Ragunath, K

2013-02-01

Autofluorescence imaging (AFI), which is a "red flag" technique during Barrett's surveillance, is associated with significant false positive results. The aim of this study was to assess the inter-observer agreement (IOA) in identifying AFI-positive lesions and to assess the overall accuracy of AFI. Anonymized AFI and high resolution white light (HRE) images were prospectively collected. The AFI images were presented in random order, followed by corresponding AFI + HRE images. Three AFI experts and 3 AFI non-experts scored images after a training presentation. The IOA was calculated using kappa and accuracy was calculated with histology as gold standard. Seventy-four sets of images were prospectively collected from 63 patients (48 males, mean age 69 years). The IOA for number of AF positive lesions was fair when AFI images were presented. This improved to moderate with corresponding AFI and HRE images [experts 0.57 (0.44-0.70), non-experts 0.47 (0.35-0.62)]. The IOA for the site of AF lesion was moderate for experts and fair for non-experts using AF images, which improved to substantial for experts [κ = 0.62 (0.50-0.72)] but remained at fair for non-experts [κ = 0.28 (0.18-0.37)] with AFI + HRE. Among experts, the accuracy of identifying dysplasia was 0.76 (0.7-0.81) using AFI images and 0.85 (0.79-0.89) using AFI + HRE images. The accuracy was 0.69 (0.62-0.74) with AFI images alone and 0.75 (0.70-0.80) using AFI + HRE among non-experts. The IOA for AF positive lesions is fair to moderate using AFI images which improved with addition of HRE. The overall accuracy of identifying dysplasia was modest, and was better when AFI and HRE images were combined.

Evaluation of color grading impact in restoration process of archive films

NASA Astrophysics Data System (ADS)

Fliegel, Karel; Vítek, Stanislav; Páta, Petr; Janout, Petr; Myslík, Jiří; Pecák, Josef; Jícha, Marek

2016-09-01

Color grading of archive films is a very particular task in the process of their restoration. The ultimate goal of color grading here is to achieve the same look of the movie as intended at the time of its first presentation. The role of the expert restorer, expert group and a digital colorist in this complicated process is to find the optimal settings of the digital color grading system so that the resulting image look is as close as possible to the estimate of the original reference release print adjusted by the expert group of cinematographers. A methodology for subjective assessment of perceived differences between the outcomes of color grading is introduced, and results of a subjective study are presented. Techniques for objective assessment of perceived differences are discussed, and their performance is evaluated using ground truth obtained from the subjective experiment. In particular, a solution based on calibrated digital single-lens reflex camera and subsequent analysis of image features captured from the projection screen is described. The system based on our previous work is further developed so that it can be used for the analysis of projected images. It allows assessing color differences in these images and predict their impact on the perceived difference in image look.

Pattern recognition and expert image analysis systems in biomedical image processing (Invited Paper)

NASA Astrophysics Data System (ADS)

Oosterlinck, A.; Suetens, P.; Wu, Q.; Baird, M.; F. M., C.

1987-09-01

This paper gives an overview of pattern recoanition techniques (P.R.) used in biomedical image processing and problems related to the different P.R. solutions. Also the use of knowledge based systems to overcome P.R. difficulties, is described. This is illustrated by a common example ofabiomedical image processing application.

Plus Disease in Retinopathy of Prematurity: Diagnostic Trends in 2016 vs. 2007

PubMed Central

Moleta, Chace; Campbell, J. Peter; Kalpathy-Cramer, Jayashree; Chan, RV Paul; Ostmo, Susan; Jonas, Karyn; Chiang, Michael F.

2017-01-01

Purpose To identify any temporal trends in the diagnosis of plus disease in retinopathy of prematurity (ROP) by experts. Design Reliability analysis Methods ROP experts were recruited in 2007 and 2016 to classify 34 wide-field fundus images of ROP as plus, pre-plus, or normal, coded as “3,” “2,” and “1” respectively in the database. The main outcome was the average calculated score for each image in each cohort. Secondary outcomes included correlation on the relative ordering of the images in 2016 versus 2007, inter-expert agreement, and intra-expert agreement Results The average score for each image was higher for 30/34 (88%) images in 2016 compared to 2007, influenced by fewer images classified as normal (P<0.01), a similar number of pre-plus (P=0.52), and more classified as plus (P<0.01). The mean weighted kappa values in 2006 were 0.36 (range 0.21 – 0.60) compared to 0.22 (range 0 – 0.40) in 2016. There was good correlation between rankings of disease severity between the two cohorts (Spearman’s rank correlation ρ=0.94) indicating near-perfect agreement on relative disease severity. Conclusions Despite good agreement between cohorts on relative disease severity ranking, the higher average score and classifications for each image demonstrate that experts are diagnosing pre-plus and plus disease at earlier stages of disease severity in 2016, compared with 2007. This has implications for patient care, research, and teaching, and additional studies are needed to better understand this temporal trend in image-based plus disease diagnosis. PMID:28087400

Applications of artificial intelligence V; Proceedings of the Meeting, Orlando, FL, May 18-20, 1987

NASA Technical Reports Server (NTRS)

Gilmore, John F. (Editor)

1987-01-01

The papers contained in this volume focus on current trends in applications of artificial intelligence. Topics discussed include expert systems, image understanding, artificial intelligence tools, knowledge-based systems, heuristic systems, manufacturing applications, and image analysis. Papers are presented on expert system issues in automated, autonomous space vehicle rendezvous; traditional versus rule-based programming techniques; applications to the control of optional flight information; methodology for evaluating knowledge-based systems; and real-time advisory system for airborne early warning.

What defines an Expert? - Uncertainty in the interpretation of seismic data

NASA Astrophysics Data System (ADS)

Bond, C. E.

2008-12-01

Studies focusing on the elicitation of information from experts are concentrated primarily in economics and world markets, medical practice and expert witness testimonies. Expert elicitation theory has been applied in the natural sciences, most notably in the prediction of fluid flow in hydrological studies. In the geological sciences expert elicitation has been limited to theoretical analysis with studies focusing on the elicitation element, gaining expert opinion rather than necessarily understanding the basis behind the expert view. In these cases experts are defined in a traditional sense, based for example on: standing in the field, no. of years of experience, no. of peer reviewed publications, the experts position in a company hierarchy or academia. Here traditional indicators of expertise have been compared for significance on affective seismic interpretation. Polytomous regression analysis has been used to assess the relative significance of length and type of experience on the outcome of a seismic interpretation exercise. Following the initial analysis the techniques used by participants to interpret the seismic image were added as additional variables to the analysis. Specific technical skills and techniques were found to be more important for the affective geological interpretation of seismic data than the traditional indicators of expertise. The results of a seismic interpretation exercise, the techniques used to interpret the seismic and the participant's prior experience have been combined and analysed to answer the question - who is and what defines an expert?

Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN.

PubMed

Schoening, Timm; Bergmann, Melanie; Ontrup, Jörg; Taylor, James; Dannheim, Jennifer; Gutt, Julian; Purser, Autun; Nattkemper, Tim W

2012-01-01

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS.

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

PubMed Central

Schoening, Timm; Bergmann, Melanie; Ontrup, Jörg; Taylor, James; Dannheim, Jennifer; Gutt, Julian; Purser, Autun; Nattkemper, Tim W.

2012-01-01

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS. PMID:22719868

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines.

PubMed

Khan, Arif Ul Maula; Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines

PubMed Central

Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts. PMID:27764213

Fully automatic registration and segmentation of first-pass myocardial perfusion MR image sequences.

PubMed

Gupta, Vikas; Hendriks, Emile A; Milles, Julien; van der Geest, Rob J; Jerosch-Herold, Michael; Reiber, Johan H C; Lelieveldt, Boudewijn P F

2010-11-01

Derivation of diagnostically relevant parameters from first-pass myocardial perfusion magnetic resonance images involves the tedious and time-consuming manual segmentation of the myocardium in a large number of images. To reduce the manual interaction and expedite the perfusion analysis, we propose an automatic registration and segmentation method for the derivation of perfusion linked parameters. A complete automation was accomplished by first registering misaligned images using a method based on independent component analysis, and then using the registered data to automatically segment the myocardium with active appearance models. We used 18 perfusion studies (100 images per study) for validation in which the automatically obtained (AO) contours were compared with expert drawn contours on the basis of point-to-curve error, Dice index, and relative perfusion upslope in the myocardium. Visual inspection revealed successful segmentation in 15 out of 18 studies. Comparison of the AO contours with expert drawn contours yielded 2.23 ± 0.53 mm and 0.91 ± 0.02 as point-to-curve error and Dice index, respectively. The average difference between manually and automatically obtained relative upslope parameters was found to be statistically insignificant (P = .37). Moreover, the analysis time per slice was reduced from 20 minutes (manual) to 1.5 minutes (automatic). We proposed an automatic method that significantly reduced the time required for analysis of first-pass cardiac magnetic resonance perfusion images. The robustness and accuracy of the proposed method were demonstrated by the high spatial correspondence and statistically insignificant difference in perfusion parameters, when AO contours were compared with expert drawn contours. Copyright © 2010 AUR. Published by Elsevier Inc. All rights reserved.

Plus disease in retinopathy of prematurity: a continuous spectrum of vascular abnormality as basis of diagnostic variability

PubMed Central

Campbell, J. Peter; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D.; Hutcheson, Kelly; Shapiro, Michael J.; Repka, Michael X.; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E.; Chan, R.V. Paul; Chiang, Michael F.

2016-01-01

Objective To identify patterns of inter-expert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). Design We developed two datasets of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP study, and determined a consensus reference standard diagnosis (RSD) for each image, based on 3 independent image graders and the clinical exam. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Subjects, Participants, and/or Controls Images obtained during routine ROP screening in neonatal intensive care units. 8 participating experts with >10 years of clinical ROP experience and >5 peer-reviewed ROP publications. Methods, Intervention, or Testing Expert classification of images of plus disease in ROP. Main Outcome Measures Inter-expert agreement (weighted kappa statistic), and agreement and bias on ordinal classification between experts (ANOVA) and the RSD (percent agreement). Results There was variable inter-expert agreement on diagnostic classifications between the 8 experts and the RSD (weighted kappa 0 – 0.75, mean 0.30). RSD agreement ranged from 80 – 94% agreement for the dataset of 100 images, and 29 – 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and pre-plus disease. The two-way ANOVA model suggested a highly significant effect of both image and user on the average score (P<0.05, adjusted R2=0.82 for dataset A, and P< 0.05 and adjusted R2 =0.6615 for dataset B). Conclusions and Relevance There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different “cut-points” for the amounts of vascular abnormality required for presence of plus and pre-plus disease. This has important implications for research, teaching and patient care for ROP, and suggests that a continuous ROP plus disease severity score may more accurately reflect the behavior of expert ROP clinicians, and may better standardize classification in the future. PMID:27591053

DrishtiCare: a telescreening platform for diabetic retinopathy powered with fundus image analysis.

PubMed

Joshi, Gopal Datt; Sivaswamy, Jayanthi

2011-01-01

Diabetic retinopathy is the leading cause of blindness in urban populations. Early diagnosis through regular screening and timely treatment has been shown to prevent visual loss and blindness. It is very difficult to cater to this vast set of diabetes patients, primarily because of high costs in reaching out to patients and a scarcity of skilled personnel. Telescreening offers a cost-effective solution to reach out to patients but is still inadequate due to an insufficient number of experts who serve the diabetes population. Developments toward fundus image analysis have shown promise in addressing the scarcity of skilled personnel for large-scale screening. This article aims at addressing the underlying issues in traditional telescreening to develop a solution that leverages the developments carried out in fundus image analysis. We propose a novel Web-based telescreening solution (called DrishtiCare) integrating various value-added fundus image analysis components. A Web-based platform on the software as a service (SaaS) delivery model is chosen to make the service cost-effective, easy to use, and scalable. A server-based prescreening system is employed to scrutinize the fundus images of patients and to refer them to the experts. An automatic quality assessment module ensures transfer of fundus images that meet grading standards. An easy-to-use interface, enabled with new visualization features, is designed for case examination by experts. Three local primary eye hospitals have participated and used DrishtiCare's telescreening service. A preliminary evaluation of the proposed platform is performed on a set of 119 patients, of which 23% are identified with the sight-threatening retinopathy. Currently, evaluation at a larger scale is under process, and a total of 450 patients have been enrolled. The proposed approach provides an innovative way of integrating automated fundus image analysis in the telescreening framework to address well-known challenges in large-scale disease screening. It offers a low-cost, effective, and easily adoptable screening solution to primary care providers. © 2010 Diabetes Technology Society.

iDEAS: A web-based system for dry eye assessment.

PubMed

Remeseiro, Beatriz; Barreira, Noelia; García-Resúa, Carlos; Lira, Madalena; Giráldez, María J; Yebra-Pimentel, Eva; Penedo, Manuel G

2016-07-01

Dry eye disease is a public health problem, whose multifactorial etiology challenges clinicians and researchers making necessary the collaboration between different experts and centers. The evaluation of the interference patterns observed in the tear film lipid layer is a common clinical test used for dry eye diagnosis. However, it is a time-consuming task with a high degree of intra- as well as inter-observer variability, which makes the use of a computer-based analysis system highly desirable. This work introduces iDEAS (Dry Eye Assessment System), a web-based application to support dry eye diagnosis. iDEAS provides a framework for eye care experts to collaboratively work using image-based services in a distributed environment. It is composed of three main components: the web client for user interaction, the web application server for request processing, and the service module for image analysis. Specifically, this manuscript presents two automatic services: tear film classification, which classifies an image into one interference pattern; and tear film map, which illustrates the distribution of the patterns over the entire tear film. iDEAS has been evaluated by specialists from different institutions to test its performance. Both services have been evaluated in terms of a set of performance metrics using the annotations of different experts. Note that the processing time of both services has been also measured for efficiency purposes. iDEAS is a web-based application which provides a fast, reliable environment for dry eye assessment. The system allows practitioners to share images, clinical information and automatic assessments between remote computers. Additionally, it save time for experts, diminish the inter-expert variability and can be used in both clinical and research settings. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Digital Dental X-ray Database for Caries Screening

NASA Astrophysics Data System (ADS)

Rad, Abdolvahab Ehsani; Rahim, Mohd Shafry Mohd; Rehman, Amjad; Saba, Tanzila

2016-06-01

Standard database is the essential requirement to compare the performance of image analysis techniques. Hence the main issue in dental image analysis is the lack of available image database which is provided in this paper. Periapical dental X-ray images which are suitable for any analysis and approved by many dental experts are collected. This type of dental radiograph imaging is common and inexpensive, which is normally used for dental disease diagnosis and abnormalities detection. Database contains 120 various Periapical X-ray images from top to bottom jaw. Dental digital database is constructed to provide the source for researchers to use and compare the image analysis techniques and improve or manipulate the performance of each technique.

Integrated computer-aided forensic case analysis, presentation, and documentation based on multimodal 3D data.

PubMed

Bornik, Alexander; Urschler, Martin; Schmalstieg, Dieter; Bischof, Horst; Krauskopf, Astrid; Schwark, Thorsten; Scheurer, Eva; Yen, Kathrin

2018-06-01

Three-dimensional (3D) crime scene documentation using 3D scanners and medical imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI) are increasingly applied in forensic casework. Together with digital photography, these modalities enable comprehensive and non-invasive recording of forensically relevant information regarding injuries/pathologies inside the body and on its surface. Furthermore, it is possible to capture traces and items at crime scenes. Such digitally secured evidence has the potential to similarly increase case understanding by forensic experts and non-experts in court. Unlike photographs and 3D surface models, images from CT and MRI are not self-explanatory. Their interpretation and understanding requires radiological knowledge. Findings in tomography data must not only be revealed, but should also be jointly studied with all the 2D and 3D data available in order to clarify spatial interrelations and to optimally exploit the data at hand. This is technically challenging due to the heterogeneous data representations including volumetric data, polygonal 3D models, and images. This paper presents a novel computer-aided forensic toolbox providing tools to support the analysis, documentation, annotation, and illustration of forensic cases using heterogeneous digital data. Conjoint visualization of data from different modalities in their native form and efficient tools to visually extract and emphasize findings help experts to reveal unrecognized correlations and thereby enhance their case understanding. Moreover, the 3D case illustrations created for case analysis represent an efficient means to convey the insights gained from case analysis to forensic non-experts involved in court proceedings like jurists and laymen. The capability of the presented approach in the context of case analysis, its potential to speed up legal procedures and to ultimately enhance legal certainty is demonstrated by introducing a number of representative forensic cases. Copyright © 2018 The Author(s). Published by Elsevier B.V. All rights reserved.

Basic research planning in mathematical pattern recognition and image analysis

NASA Technical Reports Server (NTRS)

Bryant, J.; Guseman, L. F., Jr.

1981-01-01

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis.

Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions.

PubMed

Choi, Hongyoon

2018-04-01

Recent advances in deep learning have impacted various scientific and industrial fields. Due to the rapid application of deep learning in biomedical data, molecular imaging has also started to adopt this technique. In this regard, it is expected that deep learning will potentially affect the roles of molecular imaging experts as well as clinical decision making. This review firstly offers a basic overview of deep learning particularly for image data analysis to give knowledge to nuclear medicine physicians and researchers. Because of the unique characteristics and distinctive aims of various types of molecular imaging, deep learning applications can be different from other fields. In this context, the review deals with current perspectives of deep learning in molecular imaging particularly in terms of development of biomarkers. Finally, future challenges of deep learning application for molecular imaging and future roles of experts in molecular imaging will be discussed.

Correlation between automatic detection of malaria on thin film and experts' parasitaemia scores

NASA Astrophysics Data System (ADS)

Sunarko, Budi; Williams, Simon; Prescott, William R.; Byker, Scott M.; Bottema, Murk J.

2017-03-01

An algorithm was developed to diagnose the presence of malaria and to estimate the depth of infection by automatically counting individual normal and infected erythrocytes in images of thin blood smears. During the training stage, the parameters of the algorithm were optimized to maximize correlation with estimates of parasitaemia from expert human observers. The correlation was tested on a set of 1590 images from seven thin film blood smears. The correlation between the results from the algorithm and expert human readers was r = 0.836. Results indicate that reliable estimates of parasitaemia may be achieved by computational image analysis methods applied to images of thin film smears. Meanwhile, compared to biological experiments, the algorithm fitted well the three high parasitaemia slides and a mid-level parasitaemia slide, and overestimated the three low parasitaemia slides. To improve the parasitaemia estimation, the sources of the overestimation were identified. Emphasis is laid on the importance of further research in order to identify parasites independently of their erythrocyte hosts

Is the relationship between pattern recall and decision-making influenced by anticipatory recall?

PubMed

Gorman, Adam D; Abernethy, Bruce; Farrow, Damian

2013-01-01

The present study compared traditional measures of pattern recall to measures of anticipatory recall and decision-making to examine the underlying mechanisms of expert pattern perception and to address methodological limitations in previous studies where anticipatory recall has generally been overlooked. Recall performance in expert and novice basketball players was measured by examining the spatial error in recalling player positions both for a target image (traditional recall) and at 40-ms increments following the target image (anticipatory recall). Decision-making performance was measured by comparing the participant's response to those identified by a panel of expert coaches. Anticipatory recall was observed in the recall task and was significantly more pronounced for the experts, suggesting that traditional methods of spatial recall analysis may not have provided a completely accurate determination of the full magnitude of the experts' superiority. Accounting for anticipatory recall also increased the relative contribution of recall skill to decision-making accuracy although the gains in explained variance were modest and of debatable functional significance.

Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images.

PubMed

Prasad, Keerthana; Winter, Jan; Bhat, Udayakrishna M; Acharya, Raviraja V; Prabhu, Gopalakrishna K

2012-08-01

This paper describes development of a decision support system for diagnosis of malaria using color image analysis. A hematologist has to study around 100 to 300 microscopic views of Giemsa-stained thin blood smear images to detect malaria parasites, evaluate the extent of infection and to identify the species of the parasite. The proposed algorithm picks up the suspicious regions and detects the parasites in images of all the views. The subimages representing all these parasites are put together to form a composite image which can be sent over a communication channel to obtain the opinion of a remote expert for accurate diagnosis and treatment. We demonstrate the use of the proposed technique for use as a decision support system by developing an android application which facilitates the communication with a remote expert for the final confirmation on the decision for treatment of malaria. Our algorithm detects around 96% of the parasites with a false positive rate of 20%. The Spearman correlation r was 0.88 with a confidence interval of 0.838 to 0.923, p<0.0001.

Mapping on complex neutrosophic soft expert sets

NASA Astrophysics Data System (ADS)

Al-Quran, Ashraf; Hassan, Nasruddin

2018-04-01

We introduce the mapping on complex neutrosophic soft expert sets. Further, we investigated the basic operations and other related properties of complex neutrosophic soft expert image and complex neutrosophic soft expert inverse image of complex neutrosophic soft expert sets.

Structural MRI and Cognitive Correlates in Pest-control Personnel from Gulf War I

DTIC Science & Technology

2009-04-01

Medicine where they will be reconstructed for morphometric analyses by the study imaging expert, Dr. Killiany. All the images will be transferred to... geometric design; assess ability to organize and construct Raw Score...MRI and morphometric analysis of the images. The results of the current study will be able to compare whether brain imaging differences exist

iPixel: a visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence.

PubMed

Alor-Hernández, Giner; Pérez-Gallardo, Yuliana; Posada-Gómez, Rubén; Cortes-Robles, Guillermo; Rodríguez-González, Alejandro; Aguilar-Laserre, Alberto A

2012-09-01

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients.

PubMed

Herweh, Christian; Ringleb, Peter A; Rauch, Geraldine; Gerry, Steven; Behrens, Lars; Möhlenbruch, Markus; Gottorf, Rebecca; Richter, Daniel; Schieber, Simon; Nagel, Simon

2016-06-01

The Alberta Stroke Program Early CT score (ASPECTS) is an established 10-point quantitative topographic computed tomography scan score to assess early ischemic changes. We compared the performance of the e-ASPECTS software with those of stroke physicians at different professional levels. The baseline computed tomography scans of acute stroke patients, in whom computed tomography and diffusion-weighted imaging scans were obtained less than two hours apart, were retrospectively scored by e-ASPECTS as well as by three stroke experts and three neurology trainees blinded to any clinical information. The ground truth was defined as the ASPECTS on diffusion-weighted imaging scored by another two non-blinded independent experts on consensus basis. Sensitivity and specificity in an ASPECTS region-based and an ASPECTS score-based analysis as well as receiver-operating characteristic curves, Bland-Altman plots with mean score error, and Matthews correlation coefficients were calculated. Comparisons were made between the human scorers and e-ASPECTS with diffusion-weighted imaging being the ground truth. Two methods for clustered data were used to estimate sensitivity and specificity in the region-based analysis. In total, 34 patients were included and 680 (34 × 20) ASPECTS regions were scored. Mean time from onset to computed tomography was 172 ± 135 min and mean time difference between computed tomographyand magnetic resonance imaging was 41 ± 31 min. The region-based sensitivity (46.46% [CI: 30.8;62.1]) of e-ASPECTS was better than three trainees and one expert (p ≤ 0.01) and not statistically different from another two experts. Specificity (94.15% [CI: 91.7;96.6]) was lower than one expert and one trainee (p < 0.01) and not statistically different to the other four physicians. e-ASPECTS had the best Matthews correlation coefficient of 0.44 (experts: 0.38 ± 0.08 and trainees: 0.19 ± 0.05) and the lowest mean score error of 0.56 (experts: 1.44 ± 1.79 and trainees: 1.97 ± 2.12). e-ASPECTS showed a similar performance to that of stroke experts in the assessment of brain computed tomographys of acute ischemic stroke patients with the Alberta Stroke Program Early CT score method. © 2016 World Stroke Organization.

A game-based platform for crowd-sourcing biomedical image diagnosis and standardized remote training and education of diagnosticians

NASA Astrophysics Data System (ADS)

Feng, Steve; Woo, Minjae; Chandramouli, Krithika; Ozcan, Aydogan

2015-03-01

Over the past decade, crowd-sourcing complex image analysis tasks to a human crowd has emerged as an alternative to energy-inefficient and difficult-to-implement computational approaches. Following this trend, we have developed a mathematical framework for statistically combining human crowd-sourcing of biomedical image analysis and diagnosis through games. Using a web-based smart game (BioGames), we demonstrated this platform's effectiveness for telediagnosis of malaria from microscopic images of individual red blood cells (RBCs). After public release in early 2012 (http://biogames.ee.ucla.edu), more than 3000 gamers (experts and non-experts) used this BioGames platform to diagnose over 2800 distinct RBC images, marking them as positive (infected) or negative (non-infected). Furthermore, we asked expert diagnosticians to tag the same set of cells with labels of positive, negative, or questionable (insufficient information for a reliable diagnosis) and statistically combined their decisions to generate a gold standard malaria image library. Our framework utilized minimally trained gamers' diagnoses to generate a set of statistical labels with an accuracy that is within 98% of our gold standard image library, demonstrating the "wisdom of the crowd". Using the same image library, we have recently launched a web-based malaria training and educational game allowing diagnosticians to compare their performance with their peers. After diagnosing a set of ~500 cells per game, diagnosticians can compare their quantified scores against a leaderboard and view their misdiagnosed cells. Using this platform, we aim to expand our gold standard library with new RBC images and provide a quantified digital tool for measuring and improving diagnostician training globally.

Review of Telemicrobiology

PubMed Central

Rhoads, Daniel D.; Mathison, Blaine A.; Bishop, Henry S.; da Silva, Alexandre J.; Pantanowitz, Liron

2016-01-01

Context Microbiology laboratories are continually pursuing means to improve quality, rapidity, and efficiency of specimen analysis in the face of limited resources. One means by which to achieve these improvements is through the remote analysis of digital images. Telemicrobiology enables the remote interpretation of images of microbiology specimens. To date, the practice of clinical telemicrobiology has not been thoroughly reviewed. Objective Identify the various methods that can be employed for telemicrobiology, including emerging technologies that may provide value to the clinical laboratory. Data Sources Peer-reviewed literature, conference proceedings, meeting presentations, and expert opinions pertaining to telemicrobiology have been evaluated. Results A number of modalities have been employed for telemicroscopy including static capture techniques, whole slide imaging, video telemicroscopy, mobile devices, and hybrid systems. Telemicrobiology has been successfully implemented for applications including routine primary diagnois, expert teleconsultation, and proficiency testing. Emerging areas include digital culture plate reading, mobile health applications and computer-augmented analysis of digital images. Conclusions Static image capture techniques to date have been the most widely used modality for telemicrobiology, despite the fact that other newer technologies are available and may produce better quality interpretations. Increased adoption of telemicrobiology offers added value, quality, and efficiency to the clinical microbiology laboratory. PMID:26317376

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery.

PubMed

Belgiu, Mariana; Dr Guţ, Lucian; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

PubMed Central

Belgiu, Mariana; Drǎguţ, Lucian; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules. PMID:24623959

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Belgiu, Mariana; ǎguţ, Lucian, , Dr; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.

[Expert systems and automatic diagnostic systems in histopathology--a review].

PubMed

Tamai, S

1999-02-01

In this decade, the pathological information system has gradually been settled in many hospitals in Japan. Pathological reports and images are now digitized and managed in the database, and are referred by clinicians at the peripherals. Tele-pathology is also developing; and its users are increasing. However, in many occasions, the problem solving in diagnostic pathology is completely dependent on the solo-pathologist. Considering the need for timely and efficient supports to the solo-pathologist, I reviewed the papers on the knowledge-based interactive expert systems. The interpretations of the histopathological images are dependent on the pathologist, and these expert systems have been evaluated as "educational". With the view of the success in the cytological screening, the development of "image-analysis-based" automatic "histopathological image" classifier has been on ongoing challenges. Our 3 years experience of the development of the pathological image classifier using the artificial neural networks technology is briefly presented. This classifier provides us a "fitting rate" for the individual diagnostic pattern of the breast tumors, such as "fibroadenoma pattern". The diagnosis assisting system with computer technology should provide pathologists, especially solo-pathologists, a useful tool for the quality assurance and improvement of pathological diagnosis.

Color fundus photography versus fluorescein angiography in identification of the macular center and zone in retinopathy of prematurity.

PubMed

Patel, Samir N; Klufas, Michael A; Ryan, Michael C; Jonas, Karyn E; Ostmo, Susan; Martinez-Castellanos, Maria Ana; Berrocal, Audina M; Chiang, Michael F; Chan, R V Paul

2015-05-01

To examine the usefulness of fluorescein angiography (FA) in identifying the macular center and diagnosis of zone in patients with retinopathy of prematurity (ROP). Validity and reliability analysis of diagnostic tools. Thirty-two sets (16 color fundus photographs and 16 color fundus photographs paired with the corresponding FA images) of wide-angle retinal images obtained from 16 eyes of 8 infants with ROP were compiled on a secure web site. Nine ROP experts (3 pediatric ophthalmologists and 6 vitreoretinal surgeons) participated in the study. For each image set, experts identified the macular center and provided a diagnosis of zone. (1) Sensitivity and specificity of zone diagnosis and (2) computer-facilitated diagnosis of zone, based on precise measurement of the macular center, optic disc center, and peripheral ROP. Computer-facilitated diagnosis of zone agreed with the expert's diagnosis of zone in 28 (62%) of 45 cases using color fundus photographs and in 31 (69%) of 45 cases using FA images. Mean (95% confidence interval) sensitivity for detection of zone I by experts compared with a consensus reference standard diagnosis when interpreting the color fundus images alone versus interpreting the color fundus photographs and FA images was 47% (range, 35.3% to 59.3%) and 61.1% (range, 48.9% to 72.4%), respectively (t(9) ≥ (2.063); P = .073). There is a marginally significant difference in zone diagnosis when using color fundus photographs compared with using color fundus photographs and the corresponding FA images. There is inconsistency between traditional zone diagnosis (based on ophthalmoscopic examination and image review) compared with a computer-facilitated diagnosis of zone. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Fluorescein angiography-based diagnosis for retinopathy of prematurity: expert-non expert comparison.

PubMed

Guagliano, Rosanna; Barillà, Donatella; Bertone, Chiara; Maffia, Anna; Periti, Francesca; Spallone, Laura; Anselmetti, Giovanni; Giacosa, Elisabetta; Stronati, Mauro; Tinelli, Carmine; Bianchi, Paolo Emilio

2013-01-01

To evaluate accuracy and inter-rater reliability of RetCam fundus images and digital camera fluorangioscopic images in acute retinopathy of prematurity (ROP) by comparing diagnoses given by trainee ophthalmologists with those provided by expert ophthalmologists. 
 This is a multicenter retrospective observational study of diagnostic data from 48 eyes of 24 premature infants with classical ROP, stage II, as evaluated by RetCam 3 and fluorescein angiography (FA). Average gestational age was 25.4 weeks, average weight 804.7 g. A staging grid (with ocular fundus divided into 3 concentric zones) and 24 15° sectors centered around the optic papilla were superimposed on 360° retina photomontages (Photoshop) made from RetCam and FA images. Non expert vs expert diagnosis agreement was measured for each sector by means of Cohen kappa (Fleiss, 1981).
 A high degree of concordance was found. Inter-rater agreement between expert and non expert interpretations of retinal photomontages was greater for fluorangiographic images than for RetCam images, with κ = 0.61-1 for 120/152 (78.9%) sectors examined on the RetCam images and 
κ = 0.61-1 for 168/198 (84.8%) sectors examined on the FA images.
 The FA images appear to be easier to interpret than RetCam images, both by expert and non expert ophthalmologists. The results confirm that FA is a good examination technique with a high degree of reliability, even where trainee practitioners are involved. This suggests that retinopathy management can be improved by entrusting diagnostic responsibilities to trainee ophthalmologists, in order to extend access to correct diagnosis, recognition of threshold lesions, and prompt treatment.

Indirect detection of pulmonary nodule on low-pass filtered and original x-ray images during limited and unlimited display times

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; McEntee, Mark; Evanoff, Michael G.; Brennan, Patrick C.

2012-02-01

Aim: This study evaluates the assumption that global impression is created based on low spatial frequency components of posterior-anterior chest radiographs. Background: Expert radiologists precisely and rapidly allocate visual attention on pulmonary nodules chest radiographs. Moreover, the most frequent accurate decisions are produced in the shortest viewing time, thus, the first hundred milliseconds of image perception seems be crucial for correct interpretation. Medical image perception model assumes that during holistic analysis experts extract information based on low spatial frequency (SF) components and creates a mental map of suspicious location for further inspection. The global impression results in flagged regions for detailed inspection with foveal vision. Method: Nine chest experts and nine non-chest radiologists viewed two sets of randomly ordered chest radiographs under 2 timing conditions: (1) 300ms; (2) free search in unlimited time. The same radiographic cases of 25 normal and 25 abnormal digitalized chest films constituted two image sets: low-pass filtered and unfiltered. Subjects were asked to detect nodules and rank confidence level. MRMC ROC DBM analyses were conducted. Results: Experts had improved ROC AUC while high SF components are displayed (p=0.03) or while low SF components were viewed under unlimited time (p=0.02) compared with low SF 300mSec viewings. In contrast, non-chest radiologists showed no significant changes when high SF are displayed under flash conditions compared with free search or while low SF components were viewed under unlimited time compared with flash. Conclusion: The current medical image perception model accurately predicted performance for non-chest radiologists, however chest experts appear to benefit from high SF features during the global impression.

Improved Vote Aggregation Techniques for the Geo-Wiki Cropland Capture Crowdsourcing Game

NASA Astrophysics Data System (ADS)

Baklanov, Artem; Fritz, Steffen; Khachay, Michael; Nurmukhametov, Oleg; Salk, Carl; See, Linda; Shchepashchenko, Dmitry

2016-04-01

Crowdsourcing is a new approach for solving data processing problems for which conventional methods appear to be inaccurate, expensive, or time-consuming. Nowadays, the development of new crowdsourcing techniques is mostly motivated by so called Big Data problems, including problems of assessment and clustering for large datasets obtained in aerospace imaging, remote sensing, and even in social network analysis. By involving volunteers from all over the world, the Geo-Wiki project tackles problems of environmental monitoring with applications to flood resilience, biomass data analysis and classification of land cover. For example, the Cropland Capture Game, which is a gamified version of Geo-Wiki, was developed to aid in the mapping of cultivated land, and was used to gather 4.5 million image classifications from the Earth's surface. More recently, the Picture Pile game, which is a more generalized version of Cropland Capture, aims to identify tree loss over time from pairs of very high resolution satellite images. Despite recent progress in image analysis, the solution to these problems is hard to automate since human experts still outperform the majority of machine learning algorithms and artificial systems in this field on certain image recognition tasks. The replacement of rare and expensive experts by a team of distributed volunteers seems to be promising, but this approach leads to challenging questions such as: how can individual opinions be aggregated optimally, how can confidence bounds be obtained, and how can the unreliability of volunteers be dealt with? In this paper, on the basis of several known machine learning techniques, we propose a technical approach to improve the overall performance of the majority voting decision rule used in the Cropland Capture Game. The proposed approach increases the estimated consistency with expert opinion from 77% to 86%.

An Approach towards Ultrasound Kidney Cysts Detection using Vector Graphic Image Analysis

NASA Astrophysics Data System (ADS)

Mahmud, Wan Mahani Hafizah Wan; Supriyanto, Eko

2017-08-01

This study develops new approach towards detection of kidney ultrasound image for both with single cyst as well as multiple cysts. 50 single cyst images and 25 multiple cysts images were used to test the developed algorithm. Steps involved in developing this algorithm were vector graphic image formation and analysis, thresholding, binarization, filtering as well as roundness test. Performance evaluation to 50 single cyst images gave accuracy of 92%, while for multiple cysts images, the accuracy was about 86.89% when tested to 25 multiple cysts images. This developed algorithm may be used in developing a computerized system such as computer aided diagnosis system to help medical experts in diagnosis of kidney cysts.

Shadow analysis via the C+K Visioline: A technical note.

PubMed

Houser, T; Zerweck, C; Grove, G; Wickett, R

2017-11-01

This research investigated the ability of shadow analysis (via the Courage + Khazaka Visioline and Image Pro Premiere 9.0 software) to accurately assess the differences in skin topography associated with photo aging. Analyses were performed on impressions collected from a microfinish comparator scale (GAR Electroforming) as well a series of impressions collected from the crow's feet region of 9 women who represent each point on the Zerweck Crow's Feet classification scale. Analyses were performed using a Courage + Khazaka Visioline VL 650 as well as Image Pro Premiere 9.0 software. Shadow analysis showed an ability to accurately measure the groove depth when measuring impressions collected from grooves of known depth. Several shadow analysis parameters showed a correlation with the expert grader ratings of crow's feet when averaging measurements taken from the North and South directions. The Max Depth parameter in particular showed a strong correlation with the expert grader's ratings which improved when a more sophisticated analysis was performed using Image Pro Premiere. When used properly, shadow analysis is effective at accurately measuring skin surface impressions for differences in skin topography. Shadow analysis is shown to accurately assess the differences across a range of crow's feet severity correlating to a 0-8 grader scale. The Visioline VL 650 is a good tool for this measurement, with room for improvement in analysis which can be achieved through third party image analysis software. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery.

PubMed

Loizou, Christos P; Theofanous, Charoula; Pantziaris, Marios; Kasparis, Takis

2014-04-01

Ultrasound imaging of the common carotid artery (CCA) is a non-invasive tool used in medicine to assess the severity of atherosclerosis and monitor its progression through time. It is also used in border detection and texture characterization of the atherosclerotic carotid plaque in the CCA, the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that all are very important in the assessment of cardiovascular disease (CVD). Visual perception, however, is hindered by speckle, a multiplicative noise, that degrades the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound images. In order to facilitate this preprocessing step, we have developed in MATLAB(®) a unified toolbox that integrates image despeckle filtering (IDF), texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA images. The proposed software, is based on a graphical user interface (GUI) and incorporates image normalization, 10 different despeckle filtering techniques (DsFlsmv, DsFwiener, DsFlsminsc, DsFkuwahara, DsFgf, DsFmedian, DsFhmedian, DsFad, DsFnldif, DsFsrad), image intensity normalization, 65 texture features, 15 quantitative image quality metrics and objective image quality evaluation. The software is publicly available in an executable form, which can be downloaded from http://www.cs.ucy.ac.cy/medinfo/. It was validated on 100 ultrasound images of the CCA, by comparing its results with quantitative visual analysis performed by a medical expert. It was observed that the despeckle filters DsFlsmv, and DsFhmedian improved image quality perception (based on the expert's assessment and the image texture and quality metrics). It is anticipated that the system could help the physician in the assessment of cardiovascular image analysis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Computer Sciences and Data Systems, volume 1

NASA Technical Reports Server (NTRS)

1987-01-01

Topics addressed include: software engineering; university grants; institutes; concurrent processing; sparse distributed memory; distributed operating systems; intelligent data management processes; expert system for image analysis; fault tolerant software; and architecture research.

[Telemedicine correlation in retinopathy of prematurity between experts and non-expert observers].

PubMed

Ossandón, D; Zanolli, M; López, J P; Stevenson, R; Agurto, R; Cartes, C

2015-01-01

To study the correlation between expert and non-expert observers in the reporting images for the diagnosis of retinopathy of prematurity (ROP) in a telemedicine setting. A cross-sectional, multicenter study, consisting of 25 sets of images of patients screened for ROP. They were evaluated by two experts in ROP and 1 non-expert and classified according to telemedicine classification, zone, stage, plus disease and Ells referral criteria. The telemedicine classification was: no ROP, mild ROP, type 2 ROP, or ROP that requires treatment. Ells referral criteria is defined as the presence at least one of the following: ROP in zone I, Stage 3 in zone I or II, or plus+ For statistical analysis, SPSS 16.0 was used. For correlation, Kappa value was performed. There was a high correlation between observers for the assessment of ROP stage (0.75; 0.54-0.88) plus disease (0.85; 0.71-0.92), and Ells criteria (0.89; 0.83-1.0). However, inter-observer values were low for zone (0.41; 0.27-0.54) and telemedicine classification (0.43; 0.33-0.6). When evaluating telemedicine images by examiners with different levels of expertise in ROP, the Ells criteria gave the best correlation. In addition, stage of disease and plus disease have good correlation among observers. In contrast, the correlation between observers was low for zone and telemedicine classification. Copyright © 2014 Sociedad Española de Oftalmología. Published by Elsevier España, S.L.U. All rights reserved.

NEFI: Network Extraction From Images

PubMed Central

Dirnberger, M.; Kehl, T.; Neumann, A.

2015-01-01

Networks are amongst the central building blocks of many systems. Given a graph of a network, methods from graph theory enable a precise investigation of its properties. Software for the analysis of graphs is widely available and has been applied to study various types of networks. In some applications, graph acquisition is relatively simple. However, for many networks data collection relies on images where graph extraction requires domain-specific solutions. Here we introduce NEFI, a tool that extracts graphs from images of networks originating in various domains. Regarding previous work on graph extraction, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. NEFI provides a novel platform allowing practitioners to easily extract graphs from images by combining basic tools from image processing, computer vision and graph theory. Thus, NEFI constitutes an alternative to tedious manual graph extraction and special purpose tools. We anticipate NEFI to enable time-efficient collection of large datasets. The analysis of these novel datasets may open up the possibility to gain new insights into the structure and function of various networks. NEFI is open source and available at http://nefi.mpi-inf.mpg.de. PMID:26521675

Validity and reliability of a scale to measure genital body image.

PubMed

Zielinski, Ruth E; Kane-Low, Lisa; Miller, Janis M; Sampselle, Carolyn

2012-01-01

Women's body image dissatisfaction extends to body parts usually hidden from view--their genitals. Ability to measure genital body image is limited by lack of valid and reliable questionnaires. We subjected a previously developed questionnaire, the Genital Self Image Scale (GSIS) to psychometric testing using a variety of methods. Five experts determined the content validity of the scale. Then using four participant groups, factor analysis was performed to determine construct validity and to identify factors. Further construct validity was established using the contrasting groups approach. Internal consistency and test-retest reliability was determined. Twenty one of 29 items were considered content valid. Two items were added based on expert suggestions. Factor analysis was undertaken resulting in four factors, identified as Genital Confidence, Appeal, Function, and Comfort. The revised scale (GSIS-20) included 20 items explaining 59.4% of the variance. Women indicating an interest in genital cosmetic surgery exhibited significantly lower scores on the GSIS-20 than those who did not. The final 20 item scale exhibited internal reliability across all sample groups as well as test-retest reliability. The GSIS-20 provides a measure of genital body image demonstrating reliability and validity across several populations of women.

Melanoma Diagnosis

NASA Astrophysics Data System (ADS)

Horsch, Alexander

The chapter deals with the diagnosis of the malignant melanoma of the skin. This aggressive type of cancer with steadily growing incidence in white populations can hundred percent be cured if it is detected in an early stage. Imaging techniques, in particular dermoscopy, have contributed significantly to improvement of diagnostic accuracy in clinical settings, achieving sensitivities for melanoma experts of beyond 95% at specificities of 90% and more. Automatic computer analysis of dermoscopy images has, in preliminary studies, achieved classification rates comparable to those of experts. However, the diagnosis of melanoma requires a lot of training and experience, and at the time being, average numbers of lesions excised per histology-proven melanoma are around 30, a number which clearly is too high. Further improvements in computer dermoscopy systems and their competent use in clinical settings certainly have the potential to support efforts of improving this situation. In the chapter, medical basics, current state of melanoma diagnosis, image analysis methods, commercial dermoscopy systems, evaluation of systems, and methods and future directions are presented.

Validating Retinal Fundus Image Analysis Algorithms: Issues and a Proposal

PubMed Central

Trucco, Emanuele; Ruggeri, Alfredo; Karnowski, Thomas; Giancardo, Luca; Chaum, Edward; Hubschman, Jean Pierre; al-Diri, Bashir; Cheung, Carol Y.; Wong, Damon; Abràmoff, Michael; Lim, Gilbert; Kumar, Dinesh; Burlina, Philippe; Bressler, Neil M.; Jelinek, Herbert F.; Meriaudeau, Fabrice; Quellec, Gwénolé; MacGillivray, Tom; Dhillon, Bal

2013-01-01

This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison. PMID:23794433

Cellphone based mobile colposcope for the evaluation of women with abnormal cervical cancer screening

NASA Astrophysics Data System (ADS)

Kahn, Bruce S.; Kass, Alex J.; Waalen, Jill; Levitz, David

2015-03-01

Objective: Compare an inexpensive cell-phone based Mobile Colposcope, with a standard colposcope in the evaluation of women with abnormal Pap smear screening. Methodology: The study was a prospective, parallel noninferiority trial. Thirty women underwent colposcopy for the evaluation of an abnormal Pap smear. After application of acetic acid, images of the cervix were obtained with both a standard colposcope and the Mobile Colposcope. An additional set of images using both devices were obtained using the red-free (green filter) mode. Eight experienced gynecologists then evaluated 100 paired images (plain and green filter) from two different sites in random order using a web based assessment program. After reviewing each set of paired images, the expert would make an assessment of: 1) normal (no biopsy/ random biopsy), or 2) abnormal. For abnormal images, the expert then electronically marked the site(s) on the image where a biopsy was recommended. In image analysis, the cervical image was divided into 12 radial sectors and the marked sites for biopsy on the matched pairs were compared. Matched pairs that were considered normal, or those where biopsy site recommendations were within +/- 30° were considered equivalent; unmatched biopsy sites were considered non-equivalent. Results were compared using Wilcoxon Matched Pairs Signed Ranks Test. Expert assessment of Mobile Colposcope images compared with assessment by standard colposcope is currently onging. Conclusions: if the Mobile Colposcope demonstrates non-inferiority to imaging obtained with a standard colposcope and due to its low cost, it has the potential help improve cervical cancer screening in low resource settings.

Evaluating Alignment of Shapes by Ensemble Visualization

PubMed Central

Raj, Mukund; Mirzargar, Mahsa; Preston, J. Samuel; Kirby, Robert M.; Whitaker, Ross T.

2016-01-01

The visualization of variability in surfaces embedded in 3D, which is a type of ensemble uncertainty visualization, provides a means of understanding the underlying distribution of a collection or ensemble of surfaces. Although ensemble visualization for isosurfaces has been described in the literature, we conduct an expert-based evaluation of various ensemble visualization techniques in a particular medical imaging application: the construction of atlases or templates from a population of images. In this work, we extend contour boxplot to 3D, allowing us to evaluate it against an enumeration-style visualization of the ensemble members and other conventional visualizations used by atlas builders, namely examining the atlas image and the corresponding images/data provided as part of the construction process. We present feedback from domain experts on the efficacy of contour boxplot compared to other modalities when used as part of the atlas construction and analysis stages of their work. PMID:26186768

A Web-Based Education Program for Colorectal Lesion Diagnosis with Narrow Band Imaging Classification.

PubMed

Aihara, Hiroyuki; Kumar, Nitin; Thompson, Christopher C

2018-04-19

An education system for narrow band imaging (NBI) interpretation requires sufficient exposure to key features. However, access to didactic lectures by experienced teachers is limited in the United States. To develop and assess the effectiveness of a colorectal lesion identification tutorial. In the image analysis pretest, subjects including 9 experts and 8 trainees interpreted 50 white light (WL) and 50 NBI images of colorectal lesions. Results were not reviewed with subjects. Trainees then participated in an online tutorial emphasizing NBI interpretation in colorectal lesion analysis. A post-test was administered and diagnostic yields were compared to pre-education diagnostic yields. Under the NBI mode, experts showed higher diagnostic yields (sensitivity 91.5% [87.3-94.4], specificity 90.6% [85.1-94.2], and accuracy 91.1% [88.5-93.7] with substantial interobserver agreement [κ value 0.71]) compared to trainees (sensitivity 89.6% [84.8-93.0], specificity 80.6% [73.5-86.3], and accuracy 86.0% [82.6-89.2], with substantial interobserver agreement [κ value 0.69]). The online tutorial improved the diagnostic yields of trainees to the equivalent level of experts (sensitivity 94.1% [90.0-96.6], specificity 89.0% [83.0-93.2], and accuracy 92.0% [89.3-94.7], p < 0.001 with substantial interobserver agreement [κ value 0.78]). This short, online tutorial improved diagnostic performance and interobserver agreement. © 2018 S. Karger AG, Basel.

Evaluation of Screening for Retinopathy of Prematurity by ROPtool or a Lay Reader.

PubMed

Abbey, Ashkan M; Besirli, Cagri G; Musch, David C; Andrews, Chris A; Capone, Antonio; Drenser, Kimberly A; Wallace, David K; Ostmo, Susan; Chiang, Michael; Lee, Paul P; Trese, Michael T

2016-02-01

To determine if (1) tortuosity assessment by a computer program (ROPtool, developed at the University of North Carolina, Chapel Hill, and Duke University, and licensed by FocusROP) that traces retinal blood vessels and (2) assessment by a lay reader are comparable with assessment by a panel of 3 retinopathy of prematurity (ROP) experts for remote clinical grading of vascular abnormalities such as plus disease. Validity and reliability analysis of diagnostic tools. Three hundred thirty-five fundus images of prematurely born infants. Three hundred thirty-five fundus images of prematurely born infants were obtained by neonatal intensive care unit nurses. A panel of 3 ROP experts graded 84 images showing vascular dilatation, tortuosity, or both and 251 images showing no evidence of vascular abnormalities. These images were sent electronically to an experienced lay reader who independently graded them for vascular abnormalities. The images also were analyzed using the ROPtool, which assigns a numerical value to the level of vascular abnormality and tortuosity present in each of 4 quadrants or sectors. The ROPtool measurements of vascular abnormalities were graded and compared with expert panel grades with a receiver operating characteristic (ROC) curve. Grades between human readers were cross-tabulated. The area under the ROC curve was calculated for the ROPtool, and sensitivity and specificity were computed for the lay reader. Measurements of vascular abnormalities by ROPtool and grading of vascular abnormalities by 3 ROP experts and 1 experienced lay reader. The ROC curve for ROPtool's tortuosity assessment had an area under the ROC curve of 0.917. Using a threshold value of 4.97 for the second most tortuous quadrant, ROPtool's sensitivity was 91% and its specificity was 82%. Lay reader sensitivity and specificity were 99% and 73%, respectively, and had high reliability (κ, 0.87) in repeated measurements. ROPtool had very good accuracy for detection of vascular abnormalities suggestive of plus disease when compared with expert physician graders. The lay reader's results showed excellent sensitivity and good specificity when compared with those of the expert graders. These options for remote reading of images to detect vascular abnormalities deserve consideration in the quest to use telemedicine with remote reading for efficient delivery of high-quality care and to detect infants requiring bedside examination. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

ACR Appropriateness Criteria® Chronic Ankle Pain.

PubMed

Chang, Eric Y; Tadros, Anthony S; Amini, Behrang; Bell, Angela M; Bernard, Stephanie A; Fox, Michael G; Gorbachova, Tetyana; Ha, Alice S; Lee, Kenneth S; Metter, Darlene F; Mooar, Pekka A; Shah, Nehal A; Singer, Adam D; Smith, Stacy E; Taljanovic, Mihra S; Thiele, Ralf; Kransdorf, Mark J

2018-05-01

Chronic ankle pain is a common clinical problem whose cause is often elucidated by imaging. The ACR Appropriateness Criteria for chronic ankle pain define best practices of image ordering. Clinical scenarios are followed by the imaging choices and their appropriateness. The information is in ordered tables with an accompanying narrative explanation to guide physicians to order the right test. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

ACR Appropriateness Criteria® Chronic Hip Pain.

PubMed

Mintz, Douglas N; Roberts, Catherine C; Bencardino, Jenny T; Baccei, Steven J; Caird, Michelle S; Cassidy, R Carter; Chang, Eric Y; Fox, Michael G; Gyftopoulos, Soterios; Kransdorf, Mark J; Metter, Darlene F; Morrison, William B; Rosenberg, Zehava S; Shah, Nehal A; Small, Kirstin M; Subhas, Naveen; Tambar, Siddharth; Towers, Jeffrey D; Yu, Joseph S; Weissman, Barbara N

2017-05-01

Chronic hip pain is a common clinical problem whose cause is often elucidated by imaging. The ACR Appropriateness Criteria for chronic hip pain define best practices of image ordering. Clinical scenarios are followed by the imaging choices and their appropriateness. The information is in ordered tables with an accompanying narrative explanation to guide physicians to order the right test. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer-reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Interpretative variability and its impact on the prognostic value of myocardial fatty acid imaging in asymptomatic hemodialysis patients in a multicenter trial in Japan.

PubMed

Kiriyama, Tomonari; Kumita, Shin-Ichiro; Moroi, Masao; Nishimura, Tsunehiko; Tamaki, Nagara; Hasebe, Naoyuki; Kikuchi, Kenjiro

2015-01-01

The severity of impaired fatty acid utilization in the myocardium can predict cardiac death in asymptomatic patients on hemodialysis. However, interpretive variability and its impact on the prognostic value of myocardial fatty acid imaging are unknown. A total of 677 patients who received hemodialysis for ≥ 20 years and had one or more cardiovascular risk factors underwent (123)I-labeled β-methyl iodophenyl-pentadecanoic acid (BMIPP) single-photon emission computed tomography (SPECT) at 48 hospitals across Japan. SPECT images were interpreted by experts at the nuclear core laboratory and by readers with varying skill levels at clinical centers, based on the standard 17-segment model and 5-point scoring systems, independently. The κ values only reached fair agreement both for overall impression (κ=0.298, normal vs. abnormal) and for categorical impression (κ=0.244, normal vs. mildly abnormal vs. severely abnormal). The normalcy rate was lower in readers at the clinical centers (60.9%) than in experts (69.9%). In contrast to the results assessed by experts, a Kaplan-Meier analysis based on the interpretation by readers at the clinical centers failed to distinguish the risk of events in patients with normal scans from that of patients with mildly abnormal scans. Considerable variability and its impact on prognostic value were observed in the visual interpretation of BMIPP SPECT images between experts and readers at the clinical centers.

Ontology based decision system for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Trabelsi Ben Ameur, Soumaya; Cloppet, Florence; Wendling, Laurent; Sellami, Dorra

2018-04-01

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

The effect of image quality and forensic expertise in facial image comparisons.

PubMed

Norell, Kristin; Läthén, Klas Brorsson; Bergström, Peter; Rice, Allyson; Natu, Vaidehi; O'Toole, Alice

2015-03-01

Images of perpetrators in surveillance video footage are often used as evidence in court. In this study, identification accuracy was compared for forensic experts and untrained persons in facial image comparisons as well as the impact of image quality. Participants viewed thirty image pairs and were asked to rate the level of support garnered from their observations for concluding whether or not the two images showed the same person. Forensic experts reached their conclusions with significantly fewer errors than did untrained participants. They were also better than novices at determining when two high-quality images depicted the same person. Notably, lower image quality led to more careful conclusions by experts, but not for untrained participants. In summary, the untrained participants had more false negatives and false positives than experts, which in the latter case could lead to a higher risk of an innocent person being convicted for an untrained witness. © 2014 American Academy of Forensic Sciences.

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification

PubMed Central

Tcheng, David K.; Nayak, Ashwin K.; Fowlkes, Charless C.; Punyasena, Surangi W.

2016-01-01

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based “pollen spotting” model to segment pollen grains from the slide background. We next tested ARLO’s ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems. PMID:26867017

[The application and development of artificial intelligence in medical diagnosis systems].

PubMed

Chen, Zhencheng; Jiang, Yong; Xu, Mingyu; Wang, Hongyan; Jiang, Dazong

2002-09-01

This paper has reviewed the development of artificial intelligence in medical practice and medical diagnostic expert systems, and has summarized the application of artificial neural network. It explains that a source of difficulty in medical diagnostic system is the co-existence of multiple diseases--the potentially inter-related diseases. However, the difficulty of image expert systems is inherent in high-level vision. And it increases the complexity of expert system in medical image. At last, the prospect for the development of artificial intelligence in medical image expert systems is made.

Rapid Automated Quantification of Cerebral Leukoaraiosis on CT Images: A Multicenter Validation Study.

PubMed

Chen, Liang; Carlton Jones, Anoma Lalani; Mair, Grant; Patel, Rajiv; Gontsarova, Anastasia; Ganesalingam, Jeban; Math, Nikhil; Dawson, Angela; Aweid, Basaam; Cohen, David; Mehta, Amrish; Wardlaw, Joanna; Rueckert, Daniel; Bentley, Paul

2018-05-15

Purpose To validate a random forest method for segmenting cerebral white matter lesions (WMLs) on computed tomographic (CT) images in a multicenter cohort of patients with acute ischemic stroke, by comparison with fluid-attenuated recovery (FLAIR) magnetic resonance (MR) images and expert consensus. Materials and Methods A retrospective sample of 1082 acute ischemic stroke cases was obtained that was composed of unselected patients who were treated with thrombolysis or who were undergoing contemporaneous MR imaging and CT, and a subset of International Stroke Thrombolysis-3 trial participants. Automated delineations of WML on images were validated relative to experts' manual tracings on CT images, and co-registered FLAIR MR imaging, and ratings were performed by using two conventional ordinal scales. Analyses included correlations between CT and MR imaging volumes, and agreements between automated and expert ratings. Results Automated WML volumes correlated strongly with expert-delineated WML volumes at MR imaging and CT (r 2 = 0.85 and 0.71 respectively; P < .001). Spatial-similarity of automated maps, relative to WML MR imaging, was not significantly different to that of expert WML tracings on CT images. Individual expert WML volumes at CT correlated well with each other (r 2 = 0.85), but varied widely (range, 91% of mean estimate; median estimate, 11 mL; range of estimated ranges, 0.2-68 mL). Agreements (κ) between automated ratings and consensus ratings were 0.60 (Wahlund system) and 0.64 (van Swieten system) compared with agreements between individual pairs of experts of 0.51 and 0.67, respectively, for the two rating systems (P < .01 for Wahlund system comparison of agreements). Accuracy was unaffected by established infarction, acute ischemic changes, or atrophy (P > .05). Automated preprocessing failure rate was 4%; rating errors occurred in a further 4%. Total automated processing time averaged 109 seconds (range, 79-140 seconds). Conclusion An automated method for quantifying CT cerebral white matter lesions achieves a similar accuracy to experts in unselected and multicenter cohorts. © RSNA, 2018 Online supplemental material is available for this article.

A generalized approach for producing, quantifying, and validating citizen science data from wildlife images.

PubMed

Swanson, Alexandra; Kosmala, Margaret; Lintott, Chris; Packer, Craig

2016-06-01

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large-scale camera-trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics-level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported "nothing here" for an image that was ultimately classified as containing an animal (fraction blank)-to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert-verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post-hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large-scale monitoring of African wildlife. © 2016 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

High-grade video compression of echocardiographic studies: a multicenter validation study of selected motion pictures expert groups (MPEG)-4 algorithms.

PubMed

Barbier, Paolo; Alimento, Marina; Berna, Giovanni; Celeste, Fabrizio; Gentile, Francesco; Mantero, Antonio; Montericcio, Vincenzo; Muratori, Manuela

2007-05-01

Large files produced by standard compression algorithms slow down spread of digital and tele-echocardiography. We validated echocardiographic video high-grade compression with the new Motion Pictures Expert Groups (MPEG)-4 algorithms with a multicenter study. Seven expert cardiologists blindly scored (5-point scale) 165 uncompressed and compressed 2-dimensional and color Doppler video clips, based on combined diagnostic content and image quality (uncompressed files as references). One digital video and 3 MPEG-4 algorithms (WM9, MV2, and DivX) were used, the latter at 3 compression levels (0%, 35%, and 60%). Compressed file sizes decreased from 12 to 83 MB to 0.03 to 2.3 MB (1:1051-1:26 reduction ratios). Mean SD of differences was 0.81 for intraobserver variability (uncompressed and digital video files). Compared with uncompressed files, only the DivX mean score at 35% (P = .04) and 60% (P = .001) compression was significantly reduced. At subcategory analysis, these differences were still significant for gray-scale and fundamental imaging but not for color or second harmonic tissue imaging. Original image quality, session sequence, compression grade, and bitrate were all independent determinants of mean score. Our study supports use of MPEG-4 algorithms to greatly reduce echocardiographic file sizes, thus facilitating archiving and transmission. Quality evaluation studies should account for the many independent variables that affect image quality grading.

Standardized Uptake Value Ratio-Independent Evaluation of Brain Amyloidosis.

PubMed

Chincarini, Andrea; Sensi, Francesco; Rei, Luca; Bossert, Irene; Morbelli, Silvia; Guerra, Ugo Paolo; Frisoni, Giovanni; Padovani, Alessandro; Nobili, Flavio

2016-10-18

The assessment of in vivo18F images targeting amyloid deposition is currently carried on by visual rating with an optional quantification based on standardized uptake value ratio (SUVr) measurements. We target the difficulties of image reading and possible shortcomings of the SUVr methods by validating a new semi-quantitative approach named ELBA. ELBA involves a minimal image preprocessing and does not rely on small, specific regions of interest (ROIs). It evaluates the whole brain and delivers a geometrical/intensity score to be used for ranking and dichotomic assessment. The method was applied to adniimages 18F-florbetapir images from the ADNI database. Five expert readers provided visual assessment in blind and open sessions. The longitudinal trend and the comparison to SUVr measurements were also evaluated. ELBA performed with area under the roc curve (AUC) = 0.997 versus the visual assessment. The score was significantly correlated to the SUVr values (r = 0.86, p < 10-4). The longitudinal analysis estimated a test/retest error of ≃2.3%. Cohort and longitudinal analysis suggests that the ELBA method accurately ranks the brain amyloid burden. The expert readers confirmed its relevance in aiding the visual assessment in a significant number (85) of difficult cases. Despite the good performance, poor and uneven image quality constitutes the major limitation.

Deep Learning Localizes and Identifies Polyps in Real Time with 96% Accuracy in Screening Colonoscopy.

PubMed

Urban, Gregor; Tripathi, Priyam; Alkayali, Talal; Mittal, Mohit; Jalali, Farid; Karnes, William; Baldi, Pierre

2018-06-18

The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect adenoma prevalence rate, estimated to be greater than 50% among the screening-age population. Yet the rate of adenoma detection by colonoscopists varies from 7% to 53%. It is estimated that every 1% increase in ADR reduces the risk of interval colorectal cancers by 3-6%. New strategies are needed to increase the ADR during colonoscopy. We tested the ability of computer-assisted image analysis, with convolutional neural networks (a deep learning model for image analysis), to improve polyp detection, a surrogate of ADR. We designed and trained deep convolutional neural networks (CNN) to detect polyps using a diverse and representative set of 8641 hand labeled images from screening colonoscopies collected from over 2000 patients. We tested the models on 20 colonoscopy videos with a total duration of 5 hours. Expert colonoscopists were asked to identify all polyps in 9 de-identified colonoscopy videos, selected from archived video studies, either with or without benefit of the CNN overlay. Their findings were compared with those of the CNN, using CNN-assisted expert review as the reference. When tested on manually labeled images, the CNN identified polyps with an area under the receiver operating characteristic curve (ROC-AUC) of 0.991 and an accuracy of 96.4%. In the analysis of colonoscopy videos in which 28 polyps were removed, 4 expert reviewers identified 8 additional polyps without CNN assistance that had not been removed and identified an additional 17 polyps with CNN assistance (45 in total). All polyps removed and identified by expert review were detected by the CNN. The CNN had a false-positive rate of 7%. In a set of 8641 colonoscopy images containing 4088 unique polyps the CNN identified polyps with a cross-validation accuracy of 96.4% and ROC-AUC value of 0.991. The CNN system can detect and localize polyps well within real-time constraints using an ordinary desktop machine with a contemporary graphics processing unit. This system could increase ADR and reduce interval colorectal cancers but requires validation in large multicenter trials. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Perceptual expertise in forensic facial image comparison

PubMed Central

White, David; Phillips, P. Jonathon; Hahn, Carina A.; Hill, Matthew; O'Toole, Alice J.

2015-01-01

Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than non-expert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces. PMID:26336174

The Holistic Processing Account of Visual Expertise in Medical Image Perception: A Review

PubMed Central

Sheridan, Heather; Reingold, Eyal M.

2017-01-01

In the field of medical image perception, the holistic processing perspective contends that experts can rapidly extract global information about the image, which can be used to guide their subsequent search of the image (Swensson, 1980; Nodine and Kundel, 1987; Kundel et al., 2007). In this review, we discuss the empirical evidence supporting three different predictions that can be derived from the holistic processing perspective: Expertise in medical image perception is domain-specific, experts use parafoveal and/or peripheral vision to process large regions of the image in parallel, and experts benefit from a rapid initial glimpse of an image. In addition, we discuss a pivotal recent study (Litchfield and Donovan, 2016) that seems to contradict the assumption that experts benefit from a rapid initial glimpse of the image. To reconcile this finding with the existing literature, we suggest that global processing may serve multiple functions that extend beyond the initial glimpse of the image. Finally, we discuss future research directions, and we highlight the connections between the holistic processing account and similar theoretical perspectives and findings from other domains of visual expertise. PMID:29033865

The Holistic Processing Account of Visual Expertise in Medical Image Perception: A Review.

PubMed

Sheridan, Heather; Reingold, Eyal M

2017-01-01

In the field of medical image perception, the holistic processing perspective contends that experts can rapidly extract global information about the image, which can be used to guide their subsequent search of the image (Swensson, 1980; Nodine and Kundel, 1987; Kundel et al., 2007). In this review, we discuss the empirical evidence supporting three different predictions that can be derived from the holistic processing perspective: Expertise in medical image perception is domain-specific, experts use parafoveal and/or peripheral vision to process large regions of the image in parallel, and experts benefit from a rapid initial glimpse of an image. In addition, we discuss a pivotal recent study (Litchfield and Donovan, 2016) that seems to contradict the assumption that experts benefit from a rapid initial glimpse of the image. To reconcile this finding with the existing literature, we suggest that global processing may serve multiple functions that extend beyond the initial glimpse of the image. Finally, we discuss future research directions, and we highlight the connections between the holistic processing account and similar theoretical perspectives and findings from other domains of visual expertise.

A bird's eye view: the cognitive strategies of experts interpreting seismic profiles

NASA Astrophysics Data System (ADS)

Bond, C. E.; Butler, R.

2012-12-01

Geoscience is perhaps unique in its reliance on incomplete datasets and building knowledge from their interpretation. This interpretation basis for the science is fundamental at all levels; from creation of a geological map to interpretation of remotely sensed data. To teach and understand better the uncertainties in dealing with incomplete data we need to understand the strategies individual practitioners deploy that make them effective interpreters. The nature of interpretation is such that the interpreter needs to use their cognitive ability in the analysis of the data to propose a sensible solution in their final output that is both consistent not only with the original data but also with other knowledge and understanding. In a series of experiments Bond et al. (2007, 2008, 2011, 2012) investigated the strategies and pitfalls of expert and non-expert interpretation of seismic images. These studies focused on large numbers of participants to provide a statistically sound basis for analysis of the results. The outcome of these experiments showed that techniques and strategies are more important than expert knowledge per se in developing successful interpretations. Experts are successful because of their application of these techniques. In a new set of experiments we have focused on a small number of experts to determine how they use their cognitive and reasoning skills, in the interpretation of 2D seismic profiles. Live video and practitioner commentary were used to track the evolving interpretation and to gain insight on their decision processes. The outputs of the study allow us to create an educational resource of expert interpretation through online video footage and commentary with associated further interpretation and analysis of the techniques and strategies employed. This resource will be of use to undergraduate, post-graduate, industry and academic professionals seeking to improve their seismic interpretation skills, develop reasoning strategies for dealing with incomplete datasets, and for assessing the uncertainty in these interpretations. Bond, C.E. et al. (2012). 'What makes an expert effective at interpreting seismic images?' Geology, 40, 75-78. Bond, C. E. et al. (2011). 'When there isn't a right answer: interpretation and reasoning, key skills for 21st century geoscience'. International Journal of Science Education, 33, 629-652. Bond, C. E. et al. (2008). 'Structural models: Optimizing risk analysis by understanding conceptual uncertainty'. First Break, 26, 65-71. Bond, C. E. et al., (2007). 'What do you think this is?: "Conceptual uncertainty" In geoscience interpretation'. GSA Today, 17, 4-10.

System design and implementation of digital-image processing using computational grids

NASA Astrophysics Data System (ADS)

Shen, Zhanfeng; Luo, Jiancheng; Zhou, Chenghu; Huang, Guangyu; Ma, Weifeng; Ming, Dongping

2005-06-01

As a special type of digital image, remotely sensed images are playing increasingly important roles in our daily lives. Because of the enormous amounts of data involved, and the difficulties of data processing and transfer, an important issue for current computer and geo-science experts is developing internet technology to implement rapid remotely sensed image processing. Computational grids are able to solve this problem effectively. These networks of computer workstations enable the sharing of data and resources, and are used by computer experts to solve imbalances of network resources and lopsided usage. In China, computational grids combined with spatial-information-processing technology have formed a new technology: namely, spatial-information grids. In the field of remotely sensed images, spatial-information grids work more effectively for network computing, data processing, resource sharing, task cooperation and so on. This paper focuses mainly on the application of computational grids to digital-image processing. Firstly, we describe the architecture of digital-image processing on the basis of computational grids, its implementation is then discussed in detail with respect to the technology of middleware. The whole network-based intelligent image-processing system is evaluated on the basis of the experimental analysis of remotely sensed image-processing tasks; the results confirm the feasibility of the application of computational grids to digital-image processing.

Does a 3D Image Improve Laparoscopic Motor Skills?

PubMed

Folaranmi, Semiu Eniola; Partridge, Roland W; Brennan, Paul M; Hennessey, Iain A M

2016-08-01

To quantitatively determine whether a three-dimensional (3D) image improves laparoscopic performance compared with a two-dimensional (2D) image. This is a prospective study with two groups of participants: novices (5) and experts (5). Individuals within each group undertook a validated laparoscopic task on a box simulator, alternating between 2D and a 3D laparoscopic image until they had repeated the task five times with each imaging modality. A dedicated motion capture camera was used to determine the time taken to complete the task (seconds) and instrument distance traveled (meters). Among the experts, the mean time taken to perform the task on the 3D image was significantly quicker than on the 2D image, 40.2 seconds versus 51.2 seconds, P < .0001. Among the novices, the mean task time again was significantly quicker on the 3D image, 56.4 seconds versus 82.7 seconds, P < .0001. There was no significant difference in the mean time it took a novice to perform the task using a 3D camera compared with an expert on a 2D camera, 56.4 seconds versus 51.3 seconds, P = .3341. The use of a 3D image confers a significant performance advantage over a 2D camera in quantitatively measured laparoscopic skills for both experts and novices. The use of a 3D image appears to improve a novice's performance to the extent that it is not statistically different from an expert using a 2D image.

ACR Appropriateness Criteria® Routine Chest Radiography.

PubMed

McComb, Barbara L; Chung, Jonathan H; Crabtree, Traves D; Heitkamp, Darel E; Iannettoni, Mark D; Jokerst, Clinton; Saleh, Anthony G; Shah, Rakesh D; Steiner, Robert M; Mohammed, Tan-Lucien H; Ravenel, James G

2016-03-01

Chest radiographs are sometimes taken before surgeries and interventional procedures on hospital admissions and outpatients. This manuscript summarizes the American College of Radiology review of the literature and recommendations on routinely performed chest radiographies in these settings. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every 3 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.

Beef quality grading using machine vision

NASA Astrophysics Data System (ADS)

Jeyamkondan, S.; Ray, N.; Kranzler, Glenn A.; Biju, Nisha

2000-12-01

A video image analysis system was developed to support automation of beef quality grading. Forty images of ribeye steaks were acquired. Fat and lean meat were differentiated using a fuzzy c-means clustering algorithm. Muscle longissimus dorsi (l.d.) was segmented from the ribeye using morphological operations. At the end of each iteration of erosion and dilation, a convex hull was fitted to the image and compactness was measured. The number of iterations was selected to yield the most compact l.d. Match between the l.d. muscle traced by an expert grader and that segmented by the program was 95.9%. Marbling and color features were extracted from the l.d. muscle and were used to build regression models to predict marbling and color scores. Quality grade was predicted using another regression model incorporating all features. Grades predicted by the model were statistically equivalent to the grades assigned by expert graders.

Diagnostic discrepancies in retinopathy of prematurity classification

PubMed Central

Campbell, J. Peter; Ryan, Michael C.; Lore, Emily; Tian, Peng; Ostmo, Susan; Jonas, Karyn; Chan, R.V. Paul; Chiang, Michael F.

2016-01-01

Objective To identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts. Design Prospective cohort study. Subjects, Participants, and/or Controls 281 infants were identified as part of a multi-center, prospective, ROP cohort study from 7 participating centers. Each site had participating ophthalmologists who provided the clinical classification after routine examination using binocular indirect ophthalmoscopy (BIO), and obtained wide-angle retinal images, which were independently classified by two study experts. Methods Wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were obtained from study subjects, and two experts evaluated each image using a secure web-based module. Image-based classifications for zone, stage, plus disease, overall disease category (no ROP, mild ROP, Type II or pre-plus, and Type I) were compared between the two experts, and to the clinical classification obtained by BIO. Main Outcome Measures Inter-expert image-based agreement and image-based vs. ophthalmoscopic diagnostic agreement using absolute agreement and weighted kappa statistic. Results 1553 study eye examinations from 281 infants were included in the study. Experts disagreed on the stage classification in 620/1553 (40%) of comparisons, plus disease classification (including pre-plus) in 287/1553 (18%), zone in 117/1553 (8%), and overall ROP category in 618/1553 (40%). However, agreement for presence vs. absence of type 1 disease was >95%. There were no differences between image-based and clinical classification except for zone III disease. Conclusions The most common area of discrepancy in ROP classification is stage, although inter-expert agreement for clinically-significant disease such as presence vs. absence of type 1 and type 2 disease is high. There were no differences between image-based grading and the clinical exam in the ability to detect clinically-significant disease. This study provides additional evidence that image-based classification of ROP reliably detects clinically significant levels of ROP with high accuracy compared to the clinical exam. PMID:27238376

Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images.

PubMed

Maier-Hein, Lena; Mersmann, Sven; Kondermann, Daniel; Bodenstedt, Sebastian; Sanchez, Alexandro; Stock, Christian; Kenngott, Hannes Gotz; Eisenmann, Mathias; Speidel, Stefanie

2014-01-01

Machine learning algorithms are gaining increasing interest in the context of computer-assisted interventions. One of the bottlenecks so far, however, has been the availability of training data, typically generated by medical experts with very limited resources. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. In this work, we investigate the potential of crowdsourcing for segmenting medical instruments in endoscopic image data. Our study suggests that (1) segmentations computed from annotations of multiple anonymous non-experts are comparable to those made by medical experts and (2) training data generated by the crowd is of the same quality as that annotated by medical experts. Given the speed of annotation, scalability and low costs, this implies that the scientific community might no longer need to rely on experts to generate reference or training data for certain applications. To trigger further research in endoscopic image processing, the data used in this study will be made publicly available.

What Geoscience Experts and Novices Look At, and What They See, When Viewing Data Visualizations

ERIC Educational Resources Information Center

Kastens, Kim A.; Shipley, Thomas F.; Boone, Alexander P.; Straccia, Frances

2016-01-01

This study examines how geoscience experts and novices make meaning from an iconic type of data visualization: shaded relief images of bathymetry and topography. Participants examined, described, and interpreted a global image, two high-resolution seafloor images, and 2 high-resolution continental images, while having their gaze direction…

77 FR 59692 - 2014 Diversity Immigrant Visa Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-28

... the E-DV system. The entry will not be accepted and must be resubmitted. Group or family photographs... must be in the Joint Photographic Experts Group (JPEG) format. Image File Size: The maximum file size...). Image File Format: The image must be in the Joint Photographic Experts Group (JPEG) format. Image File...

Rotation-invariant convolutional neural networks for galaxy morphology prediction

NASA Astrophysics Data System (ADS)

Dieleman, Sander; Willett, Kyle W.; Dambre, Joni

2015-06-01

Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological analysis has traditionally been carried out mostly via visual inspection by trained experts, which is time consuming and does not scale to large (≳104) numbers of images. Although attempts have been made to build automated classification systems, these have not been able to achieve the desired level of accuracy. The Galaxy Zoo project successfully applied a crowdsourcing strategy, inviting online users to classify images by answering a series of questions. Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images. We present a deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry. It was developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project. For images with high agreement among the Galaxy Zoo participants, our model is able to reproduce their consensus with near-perfect accuracy (>99 per cent) for most questions. Confident model predictions are highly accurate, which makes the model suitable for filtering large collections of images and forwarding challenging images to experts for manual annotation. This approach greatly reduces the experts' workload without affecting accuracy. The application of these algorithms to larger sets of training data will be critical for analysing results from future surveys such as the Large Synoptic Survey Telescope.

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

NASA Astrophysics Data System (ADS)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

Automatic measurement of epidermal thickness from optical coherence tomography images using a new algorithm.

PubMed

Josse, G; George, J; Black, D

2011-08-01

Optical coherence tomography (OCT) is an imaging system that enables in vivo epidermal thickness (ET) measurement. In order to use OCT in large-scale clinical studies, automatic algorithm detection of the dermo-epidermal junction (DEJ) is needed. This may be difficult due to image noise from optical speckle, which requires specific image treatment procedures to reduce this. In the present work, a description of the position of the DEJ is given, and an algorithm for boundary detection is presented. Twenty-nine images were taken from the skin of normal healthy subjects, from five different body sites. Seven expert assessors were asked to trace the DEJ for ET measurement on each of the images. The variability between experts was compared with a new image processing method. Between-expert variability was relatively low with a mean standard deviation of 3.4 μm. However, local positioning of the DEJ between experts was often different. The described algorithm performed adequately on all images. ET was automatically measured with a precision of < 5 μm compared with the experts on all sites studied except that of the back. Moreover, the local algorithm positioning was verified. The new image processing method for measuring ET from OCT images significantly reduces calculation time for this parameter, and avoids user intervention. The main advantages of this are that data can be analyzed more rapidly and reproducibly in clinical trials. © 2011 John Wiley & Sons A/S.

Follow-up of solar lentigo depigmentation with a retinaldehyde-based cream by clinical evaluation and calibrated colour imaging.

PubMed

Questel, E; Durbise, E; Bardy, A-L; Schmitt, A-M; Josse, G

2015-05-01

To assess an objective method evaluating the effects of a retinaldehyde-based cream (RA-cream) on solar lentigines; 29 women randomly applied RA-cream on lentigines of one hand and a control cream on the other, once daily for 3 months. A specific method enabling a reliable visualisation of the lesions was proposed, using high-magnification colour-calibrated camera imaging. Assessment was performed using clinical evaluation by Physician Global Assessment score and image analysis. Luminance determination on the numeric images was performed either on the basis of 5 independent expert's consensus borders or probability map analysis via an algorithm automatically detecting the pigmented area. Both image analysis methods showed a similar lightening of ΔL* = 2 after a 3-month treatment by RA-cream, in agreement with single-blind clinical evaluation. High-magnification colour-calibrated camera imaging combined with probability map analysis is a fast and precise method to follow lentigo depigmentation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The recognition of potato varieties using of neural image analysis method

NASA Astrophysics Data System (ADS)

Przybył, K.; Górna, K.; Wojcieszak, D.; Czekała, W.; Ludwiczak, A.; Przybylak, A.; Boniecki, P.; Koszela, K.; Zaborowicz, M.; Janczak, D.; Lewicki, A.

2015-07-01

The aim of this paper was to extract the representative features and generate an appropriate neural model for classification of varieties of edible potato. Potatoes of variety the Vineta and the Denar were the empirical object of this thesis. The main concept of the project was to develop and prepare an image database using the computer image analysis software. The choice of appropriate neural model the one which will have the greatest abilities to identify the selected variety. The aim of this project is ultimately to conduct assistance and accelerate work of the expert, who classifies and keeps different varieties of potatoes in heaps.

Perceptual expertise in forensic facial image comparison.

PubMed

White, David; Phillips, P Jonathon; Hahn, Carina A; Hill, Matthew; O'Toole, Alice J

2015-09-07

Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than non-expert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces. © 2015 The Author(s).

Automated segmentation of foveal avascular zone in fundus fluorescein angiography.

PubMed

Zheng, Yalin; Gandhi, Jagdeep Singh; Stangos, Alexandros N; Campa, Claudio; Broadbent, Deborah M; Harding, Simon P

2010-07-01

PURPOSE. To describe and evaluate the performance of a computerized automated segmentation technique for use in quantification of the foveal avascular zone (FAZ). METHODS. A computerized technique for automated segmentation of the FAZ using images from fundus fluorescein angiography (FFA) was applied to 26 transit-phase images obtained from patients with various grades of diabetic retinopathy. The area containing the FAZ zone was first extracted from the original image and smoothed by a Gaussian kernel (sigma = 1.5). An initializing contour was manually placed inside the FAZ of the smoothed image and iteratively moved by the segmentation program toward the FAZ boundary. Five tests with different initializing curves were run on each of 26 images to assess reproducibility. The accuracy of the program was also validated by comparing results obtained by the program with the FAZ boundaries manually delineated by medical retina specialists. Interobserver performance was then evaluated by comparing delineations from two of the experts. RESULTS. One-way analysis of variance indicated that the disparities between different tests were not statistically significant, signifying excellent reproducibility for the computer program. There was a statistically significant linear correlation between the results obtained by automation and manual delineations by experts. CONCLUSIONS. This automated segmentation program can produce highly reproducible results that are comparable to those made by clinical experts. It has the potential to assist in the detection and management of foveal ischemia and to be integrated into automated grading systems.

Cardiovascular imaging environment: will the future be cloud-based?

PubMed

Kawel-Boehm, Nadine; Bluemke, David A

2017-07-01

In cardiovascular CT and MR imaging large datasets have to be stored, post-processed, analyzed and distributed. Beside basic assessment of volume and function in cardiac magnetic resonance imaging e.g., more sophisticated quantitative analysis is requested requiring specific software. Several institutions cannot afford various types of software and provide expertise to perform sophisticated analysis. Areas covered: Various cloud services exist related to data storage and analysis specifically for cardiovascular CT and MR imaging. Instead of on-site data storage, cloud providers offer flexible storage services on a pay-per-use basis. To avoid purchase and maintenance of specialized software for cardiovascular image analysis, e.g. to assess myocardial iron overload, MR 4D flow and fractional flow reserve, evaluation can be performed with cloud based software by the consumer or complete analysis is performed by the cloud provider. However, challenges to widespread implementation of cloud services include regulatory issues regarding patient privacy and data security. Expert commentary: If patient privacy and data security is guaranteed cloud imaging is a valuable option to cope with storage of large image datasets and offer sophisticated cardiovascular image analysis for institutions of all sizes.

Expertise under the microscope: processing histopathological slides.

PubMed

Jaarsma, Thomas; Jarodzka, Halszka; Nap, Marius; van Merrienboer, Jeroen J G; Boshuizen, Henny P A

2014-03-01

Although the obvious goal of training in clinical pathology is to bring forth capable diagnosticians, developmental stages and their characteristics are unknown. This study therefore aims to find expertise-related differences in the processing of histopathological slides using a combination of eye tracking data and verbal data. Participants in this study were 13 clinical pathologists (experts), 12 pathology residents (intermediates) and 13 medical students (novices). They diagnosed 10 microscopic images of colon tissue for 2 seconds. Eye movements, the given diagnoses, and the vocabulary used in post hoc verbal explanations were registered. Eye movements were analysed according to changes over trial time and the processing of diagnostically relevant areas. The content analysis of verbal data was based on a categorisation system developed from the literature. Although experts and intermediates showed equal levels of diagnostic accuracy, their visual and cognitive processing differed. Whereas experts relied on their first findings and checked the image further for other abnormalities, intermediates tended to double-check their first findings. In their explanations, experts focused on the typicality of the tissue, whereas intermediates mainly mentioned many specific pathologies. Novices looked less often at the relevant areas and were incomplete, incorrect and inconclusive in their explanations. Their diagnostic accuracy was correspondingly poor. This study indicates that in the case of intermediates and experts, different visual and cognitive strategies can result in equal levels of diagnostic accuracy. Lessons for training underline the relevance of the distinction between normal and abnormal tissue for novices, especially when the mental rotation of 2-D images is required. Intermediates need to be trained to see deviations in abnormalities. Feedback and an educational design that is specific to these developmental stages might improve training. © 2014 John Wiley & Sons Ltd.

Evaluation of facial expression in acute pain in cats.

PubMed

Holden, E; Calvo, G; Collins, M; Bell, A; Reid, J; Scott, E M; Nolan, A M

2014-12-01

To describe the development of a facial expression tool differentiating pain-free cats from those in acute pain. Observers shown facial images from painful and pain-free cats were asked to identify if they were in pain or not. From facial images, anatomical landmarks were identified and distances between these were mapped. Selected distances underwent statistical analysis to identify features discriminating pain-free and painful cats. Additionally, thumbnail photographs were reviewed by two experts to identify discriminating facial features between the groups. Observers (n = 68) had difficulty in identifying pain-free from painful cats, with only 13% of observers being able to discriminate more than 80% of painful cats. Analysis of 78 facial landmarks and 80 distances identified six significant factors differentiating pain-free and painful faces including ear position and areas around the mouth/muzzle. Standardised mouth and ear distances when combined showed excellent discrimination properties, correctly differentiating pain-free and painful cats in 98% of cases. Expert review supported these findings and a cartoon-type picture scale was developed from thumbnail images. Initial investigation into facial features of painful and pain-free cats suggests potentially good discrimination properties of facial images. Further testing is required for development of a clinical tool. © 2014 British Small Animal Veterinary Association.

Beef quality parameters estimation using ultrasound and color images

PubMed Central

2015-01-01

Background Beef quality measurement is a complex task with high economic impact. There is high interest in obtaining an automatic quality parameters estimation in live cattle or post mortem. In this paper we set out to obtain beef quality estimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat: rib eye area, percentage of intramuscular fat and backfat thickness or subcutaneous fat. Proposal An algorithm based on curve evolution is implemented to calculate the rib eye area. The backfat thickness is estimated from the profile of distances between two curves that limit the steak and the rib eye, previously detected. A model base in Support Vector Regression (SVR) is trained to estimate the intramuscular fat percentage. A series of features extracted on a region of interest, previously detected in both ultrasound and color images, were proposed. In all cases, a complete evaluation was performed with different databases including: color and ultrasound images acquired by a beef industry expert, intramuscular fat estimation obtained by an expert using a commercial software, and chemical analysis. Conclusions The proposed algorithms show good results to calculate the rib eye area and the backfat thickness measure and profile. They are also promising in predicting the percentage of intramuscular fat. PMID:25734452

A software tool for automatic classification and segmentation of 2D/3D medical images

NASA Astrophysics Data System (ADS)

Strzelecki, Michal; Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur

2013-02-01

Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided.

Artificial intelligence for geologic mapping with imaging spectrometers

NASA Technical Reports Server (NTRS)

Kruse, F. A.

1993-01-01

This project was a three year study at the Center for the Study of Earth from Space (CSES) within the Cooperative Institute for Research in Environmental Science (CIRES) at the University of Colorado, Boulder. The goal of this research was to develop an expert system to allow automated identification of geologic materials based on their spectral characteristics in imaging spectrometer data such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This requirement was dictated by the volume of data produced by imaging spectrometers, which prohibits manual analysis. The research described is based on the development of automated techniques for analysis of imaging spectrometer data that emulate the analytical processes used by a human observer. The research tested the feasibility of such an approach, implemented an operational system, and tested the validity of the results for selected imaging spectrometer data sets.

Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

PubMed

Brown, James M; Campbell, J Peter; Beers, Andrew; Chang, Ken; Ostmo, Susan; Chan, R V Paul; Dy, Jennifer; Erdogmus, Deniz; Ioannidis, Stratis; Kalpathy-Cramer, Jayashree; Chiang, Michael F

2018-05-02

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The decision to treat is primarily based on the presence of plus disease, defined as dilation and tortuosity of retinal vessels. However, clinical diagnosis of plus disease is highly subjective and variable. To implement and validate an algorithm based on deep learning to automatically diagnose plus disease from retinal photographs. A deep convolutional neural network was trained using a data set of 5511 retinal photographs. Each image was previously assigned a reference standard diagnosis (RSD) based on consensus of image grading by 3 experts and clinical diagnosis by 1 expert (ie, normal, pre-plus disease, or plus disease). The algorithm was evaluated by 5-fold cross-validation and tested on an independent set of 100 images. Images were collected from 8 academic institutions participating in the Imaging and Informatics in ROP (i-ROP) cohort study. The deep learning algorithm was tested against 8 ROP experts, each of whom had more than 10 years of clinical experience and more than 5 peer-reviewed publications about ROP. Data were collected from July 2011 to December 2016. Data were analyzed from December 2016 to September 2017. A deep learning algorithm trained on retinal photographs. Receiver operating characteristic analysis was performed to evaluate performance of the algorithm against the RSD. Quadratic-weighted κ coefficients were calculated for ternary classification (ie, normal, pre-plus disease, and plus disease) to measure agreement with the RSD and 8 independent experts. Of the 5511 included retinal photographs, 4535 (82.3%) were graded as normal, 805 (14.6%) as pre-plus disease, and 172 (3.1%) as plus disease, based on the RSD. Mean (SD) area under the receiver operating characteristic curve statistics were 0.94 (0.01) for the diagnosis of normal (vs pre-plus disease or plus disease) and 0.98 (0.01) for the diagnosis of plus disease (vs normal or pre-plus disease). For diagnosis of plus disease in an independent test set of 100 retinal images, the algorithm achieved a sensitivity of 93% with 94% specificity. For detection of pre-plus disease or worse, the sensitivity and specificity were 100% and 94%, respectively. On the same test set, the algorithm achieved a quadratic-weighted κ coefficient of 0.92 compared with the RSD, outperforming 6 of 8 ROP experts. This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts. This has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.

Documentation of operational protocol for the use of MAMA software

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

Schwartz, Daniel S.

2016-01-21

Image analysis of Scanning Electron Microscope (SEM) micrographs is a complex process that can vary significantly between analysts. The factors causing the variation are numerous, and the purpose of Task 2b is to develop and test a set of protocols designed to minimize variation in image analysis between different analysts and laboratories, specifically using the MAMA software package, Version 2.1. The protocols were designed to be “minimally invasive”, so that expert SEM operators will not be overly constrained in the way they analyze particle samples. The protocols will be tested using a round-robin approach where results from expert SEM usersmore » at Los Alamos National Laboratory, Lawrence Livermore National Laboratory, Pacific Northwest National Laboratory, Savannah River National Laboratory, and the National Institute of Standards and Testing will be compared. The variation of the results will be used to quantify uncertainty in the particle image analysis process. The round-robin exercise will proceed with 3 levels of rigor, each with their own set of protocols, as described below in Tasks 2b.1, 2b.2, and 2b.3. The uncertainty will be developed using NIST standard reference material SRM 1984 “Thermal Spray Powder – Particle Size Distribution, Tungsten Carbide/Cobalt (Acicular)” [Reference 1]. Full details are available in the Certificate of Analysis, posted on the NIST website (http://www.nist.gov/srm/).« less

Using Highlighting to Train Attentional Expertise

PubMed Central

Roads, Brett; Mozer, Michael C.; Busey, Thomas A.

2016-01-01

Acquiring expertise in complex visual tasks is time consuming. To facilitate the efficient training of novices on where to look in these tasks, we propose an attentional highlighting paradigm. Highlighting involves dynamically modulating the saliency of a visual image to guide attention along the fixation path of a domain expert who had previously viewed the same image. In Experiment 1, we trained naive subjects via attentional highlighting on a fingerprint-matching task. Before and after training, we asked subjects to freely inspect images containing pairs of prints and determine whether the prints matched. Fixation sequences were automatically scored for the degree of expertise exhibited using a Bayesian discriminative model of novice and expert gaze behavior. Highlighted training causes gaze behavior to become more expert-like not only on the trained images but also on transfer images, indicating generalization of learning. In Experiment 2, to control for the possibility that the increase in expertise is due to mere exposure, we trained subjects via highlighting of fixation sequences from novices, not experts, and observed no transition toward expertise. In Experiment 3, to determine the specificity of the training effect, we trained subjects with expert fixation sequences from images other than the one being viewed, which preserves coarse-scale statistics of expert gaze but provides no information about fine-grain features. Observing at least a partial transition toward expertise, we obtain only weak evidence that the highlighting procedure facilitates the learning of critical local features. We discuss possible improvements to the highlighting procedure. PMID:26744839

Using Highlighting to Train Attentional Expertise.

PubMed

Roads, Brett; Mozer, Michael C; Busey, Thomas A

2016-01-01

Acquiring expertise in complex visual tasks is time consuming. To facilitate the efficient training of novices on where to look in these tasks, we propose an attentional highlighting paradigm. Highlighting involves dynamically modulating the saliency of a visual image to guide attention along the fixation path of a domain expert who had previously viewed the same image. In Experiment 1, we trained naive subjects via attentional highlighting on a fingerprint-matching task. Before and after training, we asked subjects to freely inspect images containing pairs of prints and determine whether the prints matched. Fixation sequences were automatically scored for the degree of expertise exhibited using a Bayesian discriminative model of novice and expert gaze behavior. Highlighted training causes gaze behavior to become more expert-like not only on the trained images but also on transfer images, indicating generalization of learning. In Experiment 2, to control for the possibility that the increase in expertise is due to mere exposure, we trained subjects via highlighting of fixation sequences from novices, not experts, and observed no transition toward expertise. In Experiment 3, to determine the specificity of the training effect, we trained subjects with expert fixation sequences from images other than the one being viewed, which preserves coarse-scale statistics of expert gaze but provides no information about fine-grain features. Observing at least a partial transition toward expertise, we obtain only weak evidence that the highlighting procedure facilitates the learning of critical local features. We discuss possible improvements to the highlighting procedure.

78 FR 59743 - Bureau of Consular Affairs; Registration for the Diversity Immigrant (DV-2015) Visa Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-27

... already a U.S. citizen or a Lawful Permanent Resident, but you will not be penalized if you do. Group... specifications: Image File Format: The miage must be in the Joint Photographic Experts Group (JPEG) format. Image... in the Joint Photographic Experts Group (JPEG) format. Image File Size: The maximum image file size...

Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett's esophagus

NASA Astrophysics Data System (ADS)

Muldoon, Timothy J.; Thekkek, Nadhi; Roblyer, Darren; Maru, Dipen; Harpaz, Noam; Potack, Jonathan; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2010-03-01

Early detection of neoplasia in patients with Barrett's esophagus is essential to improve outcomes. The aim of this ex vivo study was to evaluate the ability of high-resolution microendoscopic imaging and quantitative image analysis to identify neoplastic lesions in patients with Barrett's esophagus. Nine patients with pathologically confirmed Barrett's esophagus underwent endoscopic examination with biopsies or endoscopic mucosal resection. Resected fresh tissue was imaged with fiber bundle microendoscopy; images were analyzed by visual interpretation or by quantitative image analysis to predict whether the imaged sites were non-neoplastic or neoplastic. The best performing pair of quantitative features were chosen based on their ability to correctly classify the data into the two groups. Predictions were compared to the gold standard of histopathology. Subjective analysis of the images by expert clinicians achieved average sensitivity and specificity of 87% and 61%, respectively. The best performing quantitative classification algorithm relied on two image textural features and achieved a sensitivity and specificity of 87% and 85%, respectively. This ex vivo pilot trial demonstrates that quantitative analysis of images obtained with a simple microendoscope system can distinguish neoplasia in Barrett's esophagus with good sensitivity and specificity when compared to histopathology and to subjective image interpretation.

Comparison of variability in breast density assessment by BI-RADS category according to the level of experience.

PubMed

Eom, Hye-Joung; Cha, Joo Hee; Kang, Ji-Won; Choi, Woo Jung; Kim, Han Jun; Go, EunChae

2018-05-01

Background Only few studies have assessed variability in the results obtained by the readers with different experience levels in comparison with automated volumetric breast density measurements. Purpose To examine the variations in breast density assessment according to BI-RADS categories among readers with different experience levels and to compare it with the results of automated quantitative measurements. Material and Methods Density assignment was done for 1000 screening mammograms by six readers with three different experience levels (breast-imaging experts, general radiologists, and students). Agreement level between the results obtained by the readers and the Volpara automated volumetric breast density measurements was assessed. The agreement analysis using two categories-non-dense and dense breast tissue-was also performed. Results Intra-reader agreement for experts, general radiologists, and students were almost perfect or substantial (k = 0.74-0.95). The agreement between visual assessments of the breast-imaging experts and volumetric assessments by Volpara was substantial (k = 0.77). The agreement was moderate between the experts and general radiologists (k = 0.67) and slight between the students and Volpara (k = 0.01). The agreement for the two category groups (nondense and dense) was almost perfect between the experts and Volpara (k = 0.83). The agreement was substantial between the experts and general radiologists (k = 0.78). Conclusion We observed similar high agreement levels between visual assessments of breast density performed by radiologists and the volumetric assessments. However, agreement levels were substantially lower for the untrained readers.

Development of a user-friendly system for image processing of electron microscopy by integrating a web browser and PIONE with Eos.

PubMed

Tsukamoto, Takafumi; Yasunaga, Takuo

2014-11-01

Eos (Extensible object-oriented system) is one of the powerful applications for image processing of electron micrographs. In usual cases, Eos works with only character user interfaces (CUI) under the operating systems (OS) such as OS-X or Linux, not user-friendly. Thus, users of Eos need to be expert at image processing of electron micrographs, and have a little knowledge of computer science, as well. However, all the persons who require Eos does not an expert for CUI. Thus we extended Eos to a web system independent of OS with graphical user interfaces (GUI) by integrating web browser.Advantage to use web browser is not only to extend Eos with GUI, but also extend Eos to work under distributed computational environment. Using Ajax (Asynchronous JavaScript and XML) technology, we implemented more comfortable user-interface on web browser. Eos has more than 400 commands related to image processing for electron microscopy, and the usage of each command is different from each other. Since the beginning of development, Eos has managed their user-interface by using the interface definition file of "OptionControlFile" written in CSV (Comma-Separated Value) format, i.e., Each command has "OptionControlFile", which notes information for interface and its usage generation. Developed GUI system called "Zephyr" (Zone for Easy Processing of HYpermedia Resources) also accessed "OptionControlFIle" and produced a web user-interface automatically, because its mechanism is mature and convenient,The basic actions of client side system was implemented properly and can supply auto-generation of web-form, which has functions of execution, image preview, file-uploading to a web server. Thus the system can execute Eos commands with unique options for each commands, and process image analysis. There remain problems of image file format for visualization and workspace for analysis: The image file format information is useful to check whether the input/output file is correct and we also need to provide common workspace for analysis because the client is physically separated from a server. We solved the file format problem by extension of rules of OptionControlFile of Eos. Furthermore, to solve workspace problems, we have developed two type of system. The first system is to use only local environments. The user runs a web server provided by Eos, access to a web client through a web browser, and manipulate the local files with GUI on the web browser. The second system is employing PIONE (Process-rule for Input/Output Negotiation Environment), which is our developing platform that works under heterogenic distributed environment. The users can put their resources, such as microscopic images, text files and so on, into the server-side environment supported by PIONE, and so experts can write PIONE rule definition, which defines a workflow of image processing. PIONE run each image processing on suitable computers, following the defined rule. PIONE has the ability of interactive manipulation, and user is able to try a command with various setting values. In this situation, we contribute to auto-generation of GUI for a PIONE workflow.As advanced functions, we have developed a module to log user actions. The logs include information such as setting values in image processing, procedure of commands and so on. If we use the logs effectively, we can get a lot of advantages. For example, when an expert may discover some know-how of image processing, other users can also share logs including his know-hows and so we may obtain recommendation workflow of image analysis, if we analyze logs. To implement social platform of image processing for electron microscopists, we have developed system infrastructure, as well. © The Author 2014. Published by Oxford University Press on behalf of The Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

ACR Appropriateness Criteria® Chronic Extremity Joint Pain-Suspected Inflammatory Arthritis.

PubMed

Jacobson, Jon A; Roberts, Catherine C; Bencardino, Jenny T; Appel, Marc; Arnold, Erin; Baccei, Steven J; Cassidy, R Carter; Chang, Eric Y; Fox, Michael G; Greenspan, Bennett S; Gyftopoulos, Soterios; Hochman, Mary G; Mintz, Douglas N; Newman, Joel S; Rosenberg, Zehava S; Shah, Nehal A; Small, Kirstin M; Weissman, Barbara N

2017-05-01

Evaluation for suspected inflammatory arthritis as a cause for chronic extremity joint pain often relies on imaging. This review first discusses the characteristic osseous and soft tissue abnormalities seen with inflammatory arthritis and how they may be imaged. It is essential that imaging results are interpreted in the context of clinical and serologic results to add specificity as there is significant overlap of imaging findings among the various types of arthritis. This review provides recommendations for imaging evaluation of specific types of inflammatory arthritis, including rheumatoid arthritis, seronegative spondyloarthropathy, gout, calcium pyrophosphate dihydrate disease (or pseudogout), and erosive osteoarthritis. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer-reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Crowdsourcing for error detection in cortical surface delineations.

PubMed

Ganz, Melanie; Kondermann, Daniel; Andrulis, Jonas; Knudsen, Gitte Moos; Maier-Hein, Lena

2017-01-01

With the recent trend toward big data analysis, neuroimaging datasets have grown substantially in the past years. While larger datasets potentially offer important insights for medical research, one major bottleneck is the requirement for resources of medical experts needed to validate automatic processing results. To address this issue, the goal of this paper was to assess whether anonymous nonexperts from an online community can perform quality control of MR-based cortical surface delineations derived by an automatic algorithm. So-called knowledge workers from an online crowdsourcing platform were asked to annotate errors in automatic cortical surface delineations on 100 central, coronal slices of MR images. On average, annotations for 100 images were obtained in less than an hour. When using expert annotations as reference, the crowd on average achieves a sensitivity of 82 % and a precision of 42 %. Merging multiple annotations per image significantly improves the sensitivity of the crowd (up to 95 %), but leads to a decrease in precision (as low as 22 %). Our experiments show that the detection of errors in automatic cortical surface delineations generated by anonymous untrained workers is feasible. Future work will focus on increasing the sensitivity of our method further, such that the error detection tasks can be handled exclusively by the crowd and expert resources can be focused on error correction.

Do we need annotation experts? A case study in celiac disease classification.

PubMed

Kwitt, Roland; Hegenbart, Sebastian; Rasiwasia, Nikhil; Vécsei, Andreas; Uhl, Andreas

2014-01-01

Inference of clinically-relevant findings from the visual appearance of images has become an essential part of processing pipelines for many problems in medical imaging. Typically, a sufficient amount labeled training data is assumed to be available, provided by domain experts. However, acquisition of this data is usually a time-consuming and expensive endeavor. In this work, we ask the question if, for certain problems, expert knowledge is actually required. In fact, we investigate the impact of letting non-expert volunteers annotate a database of endoscopy images which are then used to assess the absence/presence of celiac disease. Contrary to previous approaches, we are not interested in algorithms that can handle the label noise. Instead, we present compelling empirical evidence that label noise can be compensated by a sufficiently large corpus of training data, labeled by the non-experts.

ACR Appropriateness Criteria® Headache-Child.

PubMed

Hayes, Laura L; Palasis, Susan; Bartel, Twyla B; Booth, Timothy N; Iyer, Ramesh S; Jones, Jeremy Y; Kadom, Nadja; Milla, Sarah S; Myseros, John S; Pakalnis, Ann; Partap, Sonia; Robertson, Richard L; Ryan, Maura E; Saigal, Gaurav; Soares, Bruno P; Tekes, Aylin; Karmazyn, Boaz K

2018-05-01

Headaches in children are not uncommon and have various causes. Proper neuroimaging of these children is very specific to the headache type. Care must be taken to choose and perform the most appropriate initial imaging examination in order to maximize the ability to properly determine the cause with minimum risk to the child. This evidence-based report discusses the different headache types in children and provides appropriate guidelines for imaging these children. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Structural interpretation of seismic data and inherent uncertainties

NASA Astrophysics Data System (ADS)

Bond, Clare

2013-04-01

Geoscience is perhaps unique in its reliance on incomplete datasets and building knowledge from their interpretation. This interpretation basis for the science is fundamental at all levels; from creation of a geological map to interpretation of remotely sensed data. To teach and understand better the uncertainties in dealing with incomplete data we need to understand the strategies individual practitioners deploy that make them effective interpreters. The nature of interpretation is such that the interpreter needs to use their cognitive ability in the analysis of the data to propose a sensible solution in their final output that is both consistent not only with the original data but also with other knowledge and understanding. In a series of experiments Bond et al. (2007, 2008, 2011, 2012) investigated the strategies and pitfalls of expert and non-expert interpretation of seismic images. These studies focused on large numbers of participants to provide a statistically sound basis for analysis of the results. The outcome of these experiments showed that a wide variety of conceptual models were applied to single seismic datasets. Highlighting not only spatial variations in fault placements, but whether interpreters thought they existed at all, or had the same sense of movement. Further, statistical analysis suggests that the strategies an interpreter employs are more important than expert knowledge per se in developing successful interpretations. Experts are successful because of their application of these techniques. In a new set of experiments a small number of experts are focused on to determine how they use their cognitive and reasoning skills, in the interpretation of 2D seismic profiles. Live video and practitioner commentary were used to track the evolving interpretation and to gain insight on their decision processes. The outputs of the study allow us to create an educational resource of expert interpretation through online video footage and commentary with associated further interpretation and analysis of the techniques and strategies employed. This resource will be of use to undergraduate, post-graduate, industry and academic professionals seeking to improve their seismic interpretation skills, develop reasoning strategies for dealing with incomplete datasets, and for assessing the uncertainty in these interpretations. Bond, C.E. et al. (2012). 'What makes an expert effective at interpreting seismic images?' Geology, 40, 75-78. Bond, C. E. et al. (2011). 'When there isn't a right answer: interpretation and reasoning, key skills for 21st century geoscience'. International Journal of Science Education, 33, 629-652. Bond, C. E. et al. (2008). 'Structural models: Optimizing risk analysis by understanding conceptual uncertainty'. First Break, 26, 65-71. Bond, C. E. et al., (2007). 'What do you think this is?: "Conceptual uncertainty" In geoscience interpretation'. GSA Today, 17, 4-10.

Species identification by conservation practitioners using online images: accuracy and agreement between experts.

PubMed

Austen, Gail E; Bindemann, Markus; Griffiths, Richard A; Roberts, David L

2018-01-01

Emerging technologies have led to an increase in species observations being recorded via digital images. Such visual records are easily shared, and are often uploaded to online communities when help is required to identify or validate species. Although this is common practice, little is known about the accuracy of species identification from such images. Using online images of newts that are native and non-native to the UK, this study asked holders of great crested newt ( Triturus cristatus ) licences (issued by UK authorities to permit surveying for this species) to sort these images into groups, and to assign species names to those groups. All of these experts identified the native species, but agreement among these participants was low, with some being cautious in committing to definitive identifications. Individuals' accuracy was also independent of both their experience and self-assessed ability. Furthermore, mean accuracy was not uniform across species (69-96%). These findings demonstrate the difficulty of accurate identification of newts from a single image, and that expert judgements are variable, even within the same knowledgeable community. We suggest that identification decisions should be made on multiple images and verified by more than one expert, which could improve the reliability of species data.

Online evaluation of a commercial video image analysis system (Computer Vision System) to predict beef carcass red meat yield and for augmenting the assignment of USDA yield grades. United States Department of Agriculture.

PubMed

Cannell, R C; Belk, K E; Tatum, J D; Wise, J W; Chapman, P L; Scanga, J A; Smith, G C

2002-05-01

Objective quantification of differences in wholesale cut yields of beef carcasses at plant chain speeds is important for the application of value-based marketing. This study was conducted to evaluate the ability of a commercial video image analysis system, the Computer Vision System (CVS) to 1) predict commercially fabricated beef subprimal yield and 2) augment USDA yield grading, in order to improve accuracy of grade assessment. The CVS was evaluated as a fully installed production system, operating on a full-time basis at chain speeds. Steer and heifer carcasses (n = 296) were evaluated using CVS, as well as by USDA expert and online graders, before the fabrication of carcasses into industry-standard subprimal cuts. Expert yield grade (YG), online YG, CVS estimated carcass yield, and CVS measured ribeye area in conjunction with expert grader estimates of the remaining YG factors (adjusted fat thickness, percentage of kidney-pelvic-heart fat, hot carcass weight) accounted for 67, 39, 64, and 65% of the observed variation in fabricated yields of closely trimmed subprimals. The dual component CVS predicted wholesale cut yields more accurately than current online yield grading, and, in an augmentation system, CVS ribeye measurement replaced estimated ribeye area in determination of USDA yield grade, and the accuracy of cutability prediction was improved, under packing plant conditions and speeds, to a level close to that of expert graders applying grades at a comfortable rate of speed offline.

ACR appropriateness criteria jaundice.

PubMed

Lalani, Tasneem; Couto, Corey A; Rosen, Max P; Baker, Mark E; Blake, Michael A; Cash, Brooks D; Fidler, Jeff L; Greene, Frederick L; Hindman, Nicole M; Katz, Douglas S; Kaur, Harmeet; Miller, Frank H; Qayyum, Aliya; Small, William C; Sudakoff, Gary S; Yaghmai, Vahid; Yarmish, Gail M; Yee, Judy

2013-06-01

A fundamental consideration in the workup of a jaundiced patient is the pretest probability of mechanical obstruction. Ultrasound is the first-line modality to exclude biliary tract obstruction. When mechanical obstruction is present, additional imaging with CT or MRI can clarify etiology, define level of obstruction, stage disease, and guide intervention. When mechanical obstruction is absent, additional imaging can evaluate liver parenchyma for fat and iron deposition and help direct biopsy in cases where underlying parenchymal disease or mass is found. Imaging techniques are reviewed for the following clinical scenarios: (1) the patient with painful jaundice, (2) the patient with painless jaundice, and (3) the patient with a nonmechanical cause for jaundice. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every 2 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances where evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment. Copyright © 2013 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study

NASA Astrophysics Data System (ADS)

Niazi, M. Khalid Khan; Pennell, Michael; Elkins, Camille; Hemminger, Jessica; Jin, Ming; Kirby, Sean; Kurt, Habibe; Miller, Barrie; Plocharczyk, Elizabeth; Roth, Rachel; Ziegler, Rebecca; Shana'ah, Arwa; Racke, Fred; Lozanski, Gerard; Gurcan, Metin N.

2013-03-01

Presence of Ki-67, a nuclear protein, is typically used to measure cell proliferation. The quantification of the Ki-67 proliferation index is performed visually by the pathologist; however, this is subject to inter- and intra-reader variability. Automated techniques utilizing digital image analysis by computers have emerged. The large variations in specimen preparation, staining, and imaging as well as true biological heterogeneity of tumor tissue often results in variable intensities in Ki-67 stained images. These variations affect the performance of currently developed methods. To optimize the segmentation of Ki-67 stained cells, one should define a data dependent transformation that will account for these color variations instead of defining a fixed linear transformation to separate different hues. To address these issues in images of tissue stained with Ki-67, we propose a methodology that exploits the intrinsic properties of CIE L∗a∗b∗ color space to translate this complex problem into an automatic entropy based thresholding problem. The developed method was evaluated through two reader studies with pathology residents and expert hematopathologists. Agreement between the proposed method and the expert pathologists was good (CCC = 0.80).

ACR Appropriateness Criteria® Abdominal Aortic Aneurysm: Interventional Planning and Follow-Up.

PubMed

Francois, Christopher J; Skulborstad, Erik P; Majdalany, Bill S; Chandra, Ankur; Collins, Jeremy D; Farsad, Khashayar; Gerhard-Herman, Marie D; Gornik, Heather L; Kendi, A Tuba; Khaja, Minhajuddin S; Lee, Margaret H; Sutphin, Patrick D; Kapoor, Baljendra S; Kalva, Sanjeeva P

2018-05-01

Abdominal aortic aneurysms (AAAs) are a relatively common vascular problem that can be treated with either open, surgical repair or endovascular aortic aneurysm repair (EVAR). Both approaches to AAA repair require dedicated preoperative imaging to minimize adverse outcomes. After EVAR, cross-sectional imaging has an integral role in confirming the successful treatment of the AAA and early detection of complications related to EVAR. CT angiography is the primary imaging modality for both preoperative planning and follow-up after repair. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Pansharpening on the Narrow Vnir and SWIR Spectral Bands of SENTINEL-2

NASA Astrophysics Data System (ADS)

Vaiopoulos, A. D.; Karantzalos, K.

2016-06-01

In this paper results from the evaluation of several state-of-the-art pansharpening techniques are presented for the VNIR and SWIR bands of Sentinel-2. A procedure for the pansharpening is also proposed which aims at respecting the closest spectral similarities between the higher and lower resolution bands. The evaluation included 21 different fusion algorithms and three evaluation frameworks based both on standard quantitative image similarity indexes and qualitative evaluation from remote sensing experts. The overall analysis of the evaluation results indicated that remote sensing experts disagreed with the outcomes and method ranking from the quantitative assessment. The employed image quality similarity indexes and quantitative evaluation framework based on both high and reduced resolution data from the literature didn't manage to highlight/evaluate mainly the spatial information that was injected to the lower resolution images. Regarding the SWIR bands none of the methods managed to deliver significantly better results than a standard bicubic interpolation on the original low resolution bands.

The juvenile face as a suitable age indicator in child pornography cases: a pilot study on the reliability of automated and visual estimation approaches.

PubMed

Ratnayake, M; Obertová, Z; Dose, M; Gabriel, P; Bröker, H M; Brauckmann, M; Barkus, A; Rizgeliene, R; Tutkuviene, J; Ritz-Timme, S; Marasciuolo, L; Gibelli, D; Cattaneo, C

2014-09-01

In cases of suspected child pornography, the age of the victim represents a crucial factor for legal prosecution. The conventional methods for age estimation provide unreliable age estimates, particularly if teenage victims are concerned. In this pilot study, the potential of age estimation for screening purposes is explored for juvenile faces. In addition to a visual approach, an automated procedure is introduced, which has the ability to rapidly scan through large numbers of suspicious image data in order to trace juvenile faces. Age estimations were performed by experts, non-experts and the Demonstrator of a developed software on frontal facial images of 50 females aged 10-19 years from Germany, Italy, and Lithuania. To test the accuracy, the mean absolute error (MAE) between the estimates and the real ages was calculated for each examiner and the Demonstrator. The Demonstrator achieved the lowest MAE (1.47 years) for the 50 test images. Decreased image quality had no significant impact on the performance and classification results. The experts delivered slightly less accurate MAE (1.63 years). Throughout the tested age range, both the manual and the automated approach led to reliable age estimates within the limits of natural biological variability. The visual analysis of the face produces reasonably accurate age estimates up to the age of 18 years, which is the legally relevant age threshold for victims in cases of pedo-pornography. This approach can be applied in conjunction with the conventional methods for a preliminary age estimation of juveniles depicted on images.

Preliminary Experience Using Eye-Tracking Technology to Differentiate Novice and Expert Image Interpretation for Ultrasound-Guided Regional Anesthesia.

PubMed

Borg, Lindsay K; Harrison, T Kyle; Kou, Alex; Mariano, Edward R; Udani, Ankeet D; Kim, T Edward; Shum, Cynthia; Howard, Steven K

2018-02-01

Objective measures are needed to guide the novice's pathway to expertise. Within and outside medicine, eye tracking has been used for both training and assessment. We designed this study to test the hypothesis that eye tracking may differentiate novices from experts in static image interpretation for ultrasound (US)-guided regional anesthesia. We recruited novice anesthesiology residents and regional anesthesiology experts. Participants wore eye-tracking glasses, were shown 5 sonograms of US-guided regional anesthesia, and were asked a series of anatomy-based questions related to each image while their eye movements were recorded. The answer to each question was a location on the sonogram, defined as the area of interest (AOI). The primary outcome was the total gaze time in the AOI (seconds). Secondary outcomes were the total gaze time outside the AOI (seconds), total time to answer (seconds), and time to first fixation on the AOI (seconds). Five novices and 5 experts completed the study. Although the gaze time (mean ± SD) in the AOI was not different between groups (7 ± 4 seconds for novices and 7 ± 3 seconds for experts; P = .150), the gaze time outside the AOI was greater for novices (75 ± 18 versus 44 ± 4 seconds for experts; P = .005). The total time to answer and total time to first fixation in the AOI were both shorter for experts. Experts in US-guided regional anesthesia take less time to identify sonoanatomy and spend less unfocused time away from a target compared to novices. Eye tracking is a potentially useful tool to differentiate novices from experts in the domain of US image interpretation. © 2017 by the American Institute of Ultrasound in Medicine.

Computer-Assisted Digital Image Analysis of Plus Disease in Retinopathy of Prematurity.

PubMed

Kemp, Pavlina S; VanderVeen, Deborah K

2016-01-01

The objective of this study is to review the current state and role of computer-assisted analysis in diagnosis of plus disease in retinopathy of prematurity. Diagnosis and documentation of retinopathy of prematurity are increasingly being supplemented by digital imaging. The incorporation of computer-aided techniques has the potential to add valuable information and standardization regarding the presence of plus disease, an important criterion in deciding the necessity of treatment of vision-threatening retinopathy of prematurity. A review of literature found that several techniques have been published examining the process and role of computer aided analysis of plus disease in retinopathy of prematurity. These techniques use semiautomated image analysis techniques to evaluate retinal vascular dilation and tortuosity, using calculated parameters to evaluate presence or absence of plus disease. These values are then compared with expert consensus. The study concludes that computer-aided image analysis has the potential to use quantitative and objective criteria to act as a supplemental tool in evaluating for plus disease in the setting of retinopathy of prematurity.

Expert Reliability for the World Health Organization Standardized Ultrasound Classification of Cystic Echinococcosis

PubMed Central

Solomon, Nadia; Fields, Paul J.; Tamarozzi, Francesca; Brunetti, Enrico; Macpherson, Calum N. L.

2017-01-01

Cystic echinococcosis (CE), a parasitic zoonosis, results in cyst formation in the viscera. Cyst morphology depends on developmental stage. In 2003, the World Health Organization (WHO) published a standardized ultrasound (US) classification for CE, for use among experts as a standard of comparison. This study examined the reliability of this classification. Eleven international CE and US experts completed an assessment of eight WHO classification images and 88 test images representing cyst stages. Inter- and intraobserver reliability and observer performance were assessed using Fleiss' and Cohen's kappa. Interobserver reliability was moderate for WHO images (κ = 0.600, P < 0.0001) and substantial for test images (κ = 0.644, P < 0.0001), with substantial to almost perfect interobserver reliability for stages with pathognomonic signs (CE1, CE2, and CE3) for WHO (0.618 < κ < 0.904) and test images (0.642 < κ < 0.768). Comparisons of expert performances against the majority classification for each image were significant for WHO (0.413 < κ < 1.000, P < 0.005) and test images (0.718 < κ < 0.905, P < 0.0001); and intraobserver reliability was significant for WHO (0.520 < κ < 1.000, P < 0.005) and test images (0.690 < κ < 0.896, P < 0.0001). Findings demonstrate moderate to substantial interobserver and substantial to almost perfect intraobserver reliability for the WHO classification, with substantial to almost perfect interobserver reliability for pathognomonic stages. This confirms experts' abilities to reliably identify WHO-defined pathognomonic signs of CE, demonstrating that the WHO classification provides a reproducible way of staging CE. PMID:28070008

Gold-standard for computer-assisted morphological sperm analysis.

PubMed

Chang, Violeta; Garcia, Alejandra; Hitschfeld, Nancy; Härtel, Steffen

2017-04-01

Published algorithms for classification of human sperm heads are based on relatively small image databases that are not open to the public, and thus no direct comparison is available for competing methods. We describe a gold-standard for morphological sperm analysis (SCIAN-MorphoSpermGS), a dataset of sperm head images with expert-classification labels in one of the following classes: normal, tapered, pyriform, small or amorphous. This gold-standard is for evaluating and comparing known techniques and future improvements to present approaches for classification of human sperm heads for semen analysis. Although this paper does not provide a computational tool for morphological sperm analysis, we present a set of experiments for comparing sperm head description and classification common techniques. This classification base-line is aimed to be used as a reference for future improvements to present approaches for human sperm head classification. The gold-standard provides a label for each sperm head, which is achieved by majority voting among experts. The classification base-line compares four supervised learning methods (1- Nearest Neighbor, naive Bayes, decision trees and Support Vector Machine (SVM)) and three shape-based descriptors (Hu moments, Zernike moments and Fourier descriptors), reporting the accuracy and the true positive rate for each experiment. We used Fleiss' Kappa Coefficient to evaluate the inter-expert agreement and Fisher's exact test for inter-expert variability and statistical significant differences between descriptors and learning techniques. Our results confirm the high degree of inter-expert variability in the morphological sperm analysis. Regarding the classification base line, we show that none of the standard descriptors or classification approaches is best suitable for tackling the problem of sperm head classification. We discovered that the correct classification rate was highly variable when trying to discriminate among non-normal sperm heads. By using the Fourier descriptor and SVM, we achieved the best mean correct classification: only 49%. We conclude that the SCIAN-MorphoSpermGS will provide a standard tool for evaluation of characterization and classification approaches for human sperm heads. Indeed, there is a clear need for a specific shape-based descriptor for human sperm heads and a specific classification approach to tackle the problem of high variability within subcategories of abnormal sperm cells. Copyright © 2017 Elsevier Ltd. All rights reserved.

A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients.

PubMed

Pohl, Kilian M; Konukoglu, Ender; Novellas, Sebastian; Ayache, Nicholas; Fedorov, Andriy; Talos, Ion-Florin; Golby, Alexandra; Wells, William M; Kikinis, Ron; Black, Peter M

2011-03-01

Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.

Classification of microscopy images of Langerhans islets

NASA Astrophysics Data System (ADS)

Å vihlík, Jan; Kybic, Jan; Habart, David; Berková, Zuzana; Girman, Peter; Kříž, Jan; Zacharovová, Klára

2014-03-01

Evaluation of images of Langerhans islets is a crucial procedure for planning an islet transplantation, which is a promising diabetes treatment. This paper deals with segmentation of microscopy images of Langerhans islets and evaluation of islet parameters such as area, diameter, or volume (IE). For all the available images, the ground truth and the islet parameters were independently evaluated by four medical experts. We use a pixelwise linear classifier (perceptron algorithm) and SVM (support vector machine) for image segmentation. The volume is estimated based on circle or ellipse fitting to individual islets. The segmentations were compared with the corresponding ground truth. Quantitative islet parameters were also evaluated and compared with parameters given by medical experts. We can conclude that accuracy of the presented fully automatic algorithm is fully comparable with medical experts.

Radiology image perception and observer performance: How does expertise and clinical information alter interpretation? Stroke detection explored through eye-tracking

NASA Astrophysics Data System (ADS)

Cooper, Lindsey; Gale, Alastair; Darker, Iain; Toms, Andoni; Saada, Janak

2009-02-01

Historically, radiology research has been dominated by chest and breast screening. Few studies have examined complex interpretative tasks such as the reading of multidimensional brain CT or MRI scans. Additionally, no studies at the time of writing have explored the interpretation of stroke images; from novices through to experienced practitioners using eye movement analysis. Finally, there appears a lack of evidence on the clinical effects of radiology reports and their influence on image appraisal and clinical diagnosis. A computer-based, eye-tracking study was designed to assess diagnostic accuracy and interpretation in stroke CT and MR imagery. Eight predetermined clinical cases, five images per case, were presented to participants (novices, trainee, and radiologists; n=8). The presence or absence of abnormalities was rated on a five-point Likert scale and their locations reported. Half cases of the cases were accompanied by clinical information; half were not, to assess the impact of information on observer performance. Results highlight differences in visual search patterns amongst novice, trainee and expert observers; the most marked differences occurred between novice readers and experts. Experts spent more time in challenging areas of interest (AOI) than novices and trainee, and were more confident unless a lesion was large and obvious. The time to first AOI fixation differed by size, shape and clarity of lesion. 'Time to lesion' dropped significantly when recognition appeared to occur between slices. The influence of clinical information was minimal.

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.

PubMed

Mavandadi, Sam; Dimitrov, Stoyan; Feng, Steve; Yu, Frank; Sikora, Uzair; Yaglidere, Oguzhan; Padmanabhan, Swati; Nielsen, Karin; Ozcan, Aydogan

2012-01-01

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.

Intuitive ultrasonography for autonomous medical care in limited-resource environments

NASA Astrophysics Data System (ADS)

Dulchavsky, Scott A.; Sargsyan, Ashot E.; Garcia, Kathleen M.; Melton, Shannon L.; Ebert, Douglas; Hamilton, Douglas R.

2011-05-01

Management of health problems in limited resource environments, including spaceflight, faces challenges in both available equipment and personnel. The medical support for spaceflight outside Low Earth Orbit is still being defined; ultrasound (US) imaging is a candidate since trials on the International Space Station (ISS) prove that this highly informative modality performs very well in spaceflight. Considering existing estimates, authors find that US could be useful in most potential medical problems, as a powerful factor to mitigate risks and protect mission. Using outcome-oriented approach, an intuitive and adaptive US image catalog is being developed that can couple with just-in-time training methods already in use, to allow non-expert crew to autonomously acquire and interpret US data for research or diagnosis. The first objective of this work is to summarize the experience in providing imaging expertise from a central location in real time, enabling data collection by a minimally trained operator onsite. In previous investigations, just-in-time training was combined with real-time expert guidance to allow non-physician astronauts to perform over 80 h of complex US examinations on ISS, including abdominal, cardiovascular, ocular, musculoskeletal, dental/sinus, and thoracic exams. The analysis of these events shows that non-physician crew-members, after minimal training, can perform complex, quality US examinations. These training and guidance methods were also adapted for terrestrial use in professional sporting venues, the Olympic Games, and for austere locations including Mt. Everest. The second objective is to introduce a new imaging support system under development that is based on a digital catalog of existing sample images, complete with image recognition and acquisition logic and technique, and interactive multimedia reference tools, to guide and support autonomous acquisition, and possibly interpretation, of images without real-time link with a human expert. In other words, we are attempting to replace, to the extent possible, expert guidance by guidance from a digital information resource. This is a next logical phase of the authors' sustained effort to make US imaging available to sites lacking proper expertise. This effort will benefit NASA as the agency plans to develop future human exploration programs requiring increased medical autonomy. The new system will be readily adaptable to terrestrial medicine including emergency, rural, and military applications.

Development of an expert data reduction assistant

NASA Technical Reports Server (NTRS)

Miller, Glenn E.; Johnston, Mark D.; Hanisch, Robert J.

1992-01-01

We propose the development of an expert system tool for the management and reduction of complex data sets. The proposed work is an extension of a successful prototype system for the calibration of CCD images developed by Dr. Johnston in 1987. The reduction of complex multi-parameter data sets presents severe challenges to a scientist. Not only must a particular data analysis system be mastered, (e.g. IRAF/SDAS/MIDAS), large amounts of data can require many days of tedious work and supervision by the scientist for even the most straightforward reductions. The proposed Expert Data Reduction Assistant will help the scientist overcome these obstacles by developing a reduction plan based on the data at hand and producing a script for the reduction of the data in a target common language.

75 FR 60846 - Bureau of Consular Affairs; Registration for the Diversity Immigrant (DV-2012) Visa Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-01

... need to submit a photo for a child who is already a U.S. citizen or a Legal Permanent Resident. Group... Joint Photographic Experts Group (JPEG) format; it must have a maximum image file size of two hundred... (dpi); the image file format in Joint Photographic Experts Group (JPEG) format; the maximum image file...

Digital imaging biomarkers feed machine learning for melanoma screening.

PubMed

Gareau, Daniel S; Correa da Rosa, Joel; Yagerman, Sarah; Carucci, John A; Gulati, Nicholas; Hueto, Ferran; DeFazio, Jennifer L; Suárez-Fariñas, Mayte; Marghoob, Ashfaq; Krueger, James G

2017-07-01

We developed an automated approach for generating quantitative image analysis metrics (imaging biomarkers) that are then analysed with a set of 13 machine learning algorithms to generate an overall risk score that is called a Q-score. These methods were applied to a set of 120 "difficult" dermoscopy images of dysplastic nevi and melanomas that were subsequently excised/classified. This approach yielded 98% sensitivity and 36% specificity for melanoma detection, approaching sensitivity/specificity of expert lesion evaluation. Importantly, we found strong spectral dependence of many imaging biomarkers in blue or red colour channels, suggesting the need to optimize spectral evaluation of pigmented lesions. © 2016 The Authors. Experimental Dermatology Published by John Wiley & Sons Ltd.

Comparison of dogs and humans in visual scanning of social interaction.

PubMed

Törnqvist, Heini; Somppi, Sanni; Koskela, Aija; Krause, Christina M; Vainio, Outi; Kujala, Miiamaaria V

2015-09-01

Previous studies have demonstrated similarities in gazing behaviour of dogs and humans, but comparisons under similar conditions are rare, and little is known about dogs' visual attention to social scenes. Here, we recorded the eye gaze of dogs while they viewed images containing two humans or dogs either interacting socially or facing away: the results were compared with equivalent data measured from humans. Furthermore, we compared the gazing behaviour of two dog and two human populations with different social experiences: family and kennel dogs; dog experts and non-experts. Dogs' gazing behaviour was similar to humans: both species gazed longer at the actors in social interaction than in non-social images. However, humans gazed longer at the actors in dog than human social interaction images, whereas dogs gazed longer at the actors in human than dog social interaction images. Both species also made more saccades between actors in images representing non-conspecifics, which could indicate that processing social interaction of non-conspecifics may be more demanding. Dog experts and non-experts viewed the images very similarly. Kennel dogs viewed images less than family dogs, but otherwise their gazing behaviour did not differ, indicating that the basic processing of social stimuli remains similar regardless of social experiences.

Automatic computation of 2D cardiac measurements from B-mode echocardiography

NASA Astrophysics Data System (ADS)

Park, JinHyeong; Feng, Shaolei; Zhou, S. Kevin

2012-03-01

We propose a robust and fully automatic algorithm which computes the 2D echocardiography measurements recommended by America Society of Echocardiography. The algorithm employs knowledge-based imaging technologies which can learn the expert's knowledge from the training images and expert's annotation. Based on the models constructed from the learning stage, the algorithm searches initial location of the landmark points for the measurements by utilizing heart structure of left ventricle including mitral valve aortic valve. It employs the pseudo anatomic M-mode image generated by accumulating the line images in 2D parasternal long axis view along the time to refine the measurement landmark points. The experiment results with large volume of data show that the algorithm runs fast and is robust comparable to expert.

Intra- and inter-observer reliability of quantitative analysis of the infra-patellar fat pad and comparison between fat- and non-fat-suppressed imaging--Data from the osteoarthritis initiative.

PubMed

Steidle-Kloc, E; Wirth, W; Ruhdorfer, A; Dannhauer, T; Eckstein, F

2016-03-01

The infra-patellar fat pad (IPFP), as intra-articular adipose tissue represents a potential source of pro-inflammatory cytokines and its size has been suggested to be associated with osteoarthritis (OA) of the knee. This study examines inter- and intra-observer reliability of fat-suppressed (fs) and non-fat-suppressed (nfs) MR imaging for determination of IPFP morphological measurements as novel biomarkers. The IPFP of nine right knees of healthy Osteoarthritis Initiative participants was segmented by five readers, using fs and nfs baseline sagittal MRIs. The intra-observer reliability was determined from baseline and 1-year follow-up images. All segmentations were quality controlled (QC) by an expert reader. Reliability was expressed as root mean square coefficient of variation (RMS CV%). After QC, the inter-observer reliability for fs (nfs) imaging was 2.0% (1.1%) for IPFP volume, 2.1%/2.5% (1.6%/1.8%) for anterior/posterior surface areas, 1.8% (1.8%) for depth, and 2.1% (2.4%) for maximum sagittal area. The intra-observer reliability was 3.1% (5.0%) for volume, 2.3%/2.8% (2.5%/2.9%) for anterior/posterior surfaces, 1.9% (3.5%) for depth, and 3.3% (4.5%) for maximum sagittal area. IPFP volume from nfs images was systematically greater (+7.3%) than from fs images, but highly correlated (r=0.98). The results suggest that quantitative measurements of IPFP morphology can be performed with satisfactory reliability when expert QC is implemented. The IPFP is more clearly depicted in nfs images, and there is a small systematic off-set versus analysis from fs images. However, the high linear relationship between fs and nfs imaging suggests that fs images can be used to analyze IPFP morphology, when nfs images are not available. Copyright © 2015 Elsevier GmbH. All rights reserved.

Developing an ANSI standard for image quality tools for the testing of active millimeter wave imaging systems

NASA Astrophysics Data System (ADS)

Barber, Jeffrey; Greca, Joseph; Yam, Kevin; Weatherall, James C.; Smith, Peter R.; Smith, Barry T.

2017-05-01

In 2016, the millimeter wave (MMW) imaging community initiated the formation of a standard for millimeter wave image quality metrics. This new standard, American National Standards Institute (ANSI) N42.59, will apply to active MMW systems for security screening of humans. The Electromagnetic Signatures of Explosives Laboratory at the Transportation Security Laboratory is supporting the ANSI standards process via the creation of initial prototypes for round-robin testing with MMW imaging system manufacturers and experts. Results obtained for these prototypes will be used to inform the community and lead to consensus objective standards amongst stakeholders. Images collected with laboratory systems are presented along with results of preliminary image analysis. Future directions for object design, data collection and image processing are discussed.

Art Expertise and the Processing of Titled Abstract Art.

PubMed

Mullennix, John W; Robinet, Julien

2018-04-01

The effect of art expertise on viewers' processing of titled visual artwork was examined. The study extended the research of Leder, Carbon, and Ripsas by explicitly selecting art novices and art experts. The study was designed to test assumptions about how expertise modulates context in the form of titles for artworks. Viewers rated a set of abstract paintings for liking and understanding. The type of title accompanying the artwork (descriptive or elaborative) was manipulated. Viewers were allotted as much time as they wished to view each artwork. For judgments of liking, novices and experts both liked artworks with elaborative titles better, with overall rated liking similar for both groups. For judgments of understanding, type of title had no effect on ratings for both novices and experts. However, experts' rated understanding was higher than novices, with experts making their decisions faster than novices. An analysis of viewers' art expertise revealed that expertise was correlated with understanding, but not liking. Overall, the results suggest that both novices and experts integrate title with visual image in similar manner. However, expertise differentially affected liking and understanding. The results differ from those obtained by Leder et al. The differences between studies are discussed.

Trans-dimensional MCMC methods for fully automatic motion analysis in tagged MRI.

PubMed

Smal, Ihor; Carranza-Herrezuelo, Noemí; Klein, Stefan; Niessen, Wiro; Meijering, Erik

2011-01-01

Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method allowing quantitative analysis of regional heart dynamics. Its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we propose a novel probabilistic method for tag tracking, implemented by means of Bayesian particle filtering and a trans-dimensional Markov chain Monte Carlo (MCMC) approach, which efficiently combines information about the imaging process and tag appearance with prior knowledge about the heart dynamics obtained by means of non-rigid image registration. Experiments using synthetic image data (with ground truth) and real data (with expert manual annotation) from preclinical (small animal) and clinical (human) studies confirm that the proposed method yields higher consistency, accuracy, and intrinsic tag reliability assessment in comparison with other frequently used tag tracking methods.

User Directed Tools for Exploiting Expert Knowledge in an Immersive Segmentation and Visualization Environment

NASA Technical Reports Server (NTRS)

Senger, Steven O.

1998-01-01

Volumetric data sets have become common in medicine and many sciences through technologies such as computed x-ray tomography (CT), magnetic resonance (MR), positron emission tomography (PET), confocal microscopy and 3D ultrasound. When presented with 2D images humans immediately and unconsciously begin a visual analysis of the scene. The viewer surveys the scene identifying significant landmarks and building an internal mental model of presented information. The identification of features is strongly influenced by the viewers expectations based upon their expert knowledge of what the image should contain. While not a conscious activity, the viewer makes a series of choices about how to interpret the scene. These choices occur in parallel with viewing the scene and effectively change the way the viewer sees the image. It is this interaction of viewing and choice which is the basis of many familiar visual illusions. This is especially important in the interpretation of medical images where it is the expert knowledge of the radiologist which interprets the image. For 3D data sets this interaction of view and choice is frustrated because choices must precede the visualization of the data set. It is not possible to visualize the data set with out making some initial choices which determine how the volume of data is presented to the eye. These choices include, view point orientation, region identification, color and opacity assignments. Further compounding the problem is the fact that these visualization choices are defined in terms of computer graphics as opposed to language of the experts knowledge. The long term goal of this project is to develop an environment where the user can interact with volumetric data sets using tools which promote the utilization of expert knowledge by incorporating visualization and choice into a tight computational loop. The tools will support activities involving the segmentation of structures, construction of surface meshes and local filtering of the data set. To conform to this environment tools should have several key attributes. First, they should be only rely on computations over a local neighborhood of the probe position. Second, they should operate iteratively over time converging towards a limit behavior. Third, they should adapt to user input modifying they operational parameters with time.

Automated Dermoscopy Image Analysis of Pigmented Skin Lesions

PubMed Central

Baldi, Alfonso; Quartulli, Marco; Murace, Raffaele; Dragonetti, Emanuele; Manganaro, Mario; Guerra, Oscar; Bizzi, Stefano

2010-01-01

Dermoscopy (dermatoscopy, epiluminescence microscopy) is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions (PSLs), allowing a better visualization of surface and subsurface structures (from the epidermis to the papillary dermis). This diagnostic tool permits the recognition of morphologic structures not visible by the naked eye, thus opening a new dimension in the analysis of the clinical morphologic features of PSLs. In order to reduce the learning-curve of non-expert clinicians and to mitigate problems inherent in the reliability and reproducibility of the diagnostic criteria used in pattern analysis, several indicative methods based on diagnostic algorithms have been introduced in the last few years. Recently, numerous systems designed to provide computer-aided analysis of digital images obtained by dermoscopy have been reported in the literature. The goal of this article is to review these systems, focusing on the most recent approaches based on content-based image retrieval systems (CBIR). PMID:24281070

High resolution microendoscopy for classification of colorectal polyps.

PubMed

Chang, S S; Shukla, R; Polydorides, A D; Vila, P M; Lee, M; Han, H; Kedia, P; Lewis, J; Gonzalez, S; Kim, M K; Harpaz, N; Godbold, J; Richards-Kortum, R; Anandasabapathy, S

2013-07-01

It can be difficult to distinguish adenomas from benign polyps during routine colonoscopy. High resolution microendoscopy (HRME) is a novel method for imaging colorectal mucosa with subcellular detail. HRME criteria for the classification of colorectal neoplasia have not been previously described. Study goals were to develop criteria to characterize HRME images of colorectal mucosa (normal, hyperplastic polyps, adenomas, cancer) and to determine the accuracy and interobserver variability for the discrimination of neoplastic from non-neoplastic polyps when these criteria were applied by novice and expert microendoscopists. Two expert pathologists created consensus HRME image criteria using images from 68 patients with polyps who had undergone colonoscopy plus HRME. Using these criteria, HRME expert and novice microendoscopists were shown a set of training images and then tested to determine accuracy and interobserver variability. Expert microendoscopists identified neoplasia with sensitivity, specificity, and accuracy of 67 % (95 % confidence interval [CI] 58 % - 75 %), 97 % (94 % - 100 %), and 87 %, respectively. Nonexperts achieved sensitivity, specificity, and accuracy of 73 % (66 % - 80 %), 91 % (80 % - 100 %), and 85 %, respectively. Overall, neoplasia were identified with sensitivity 70 % (65 % - 76 %), specificity 94 % (87 % - 100 %), and accuracy 85 %. Kappa values were: experts 0.86; nonexperts 0.72; and overall 0.78. Using the new criteria, observers achieved high specificity and substantial interobserver agreement for distinguishing benign polyps from neoplasia. Increased expertise in HRME imaging improves accuracy. This low-cost microendoscopic platform may be an alternative to confocal microendoscopy in lower-resource or community-based settings.

Classifying Facial Actions

PubMed Central

Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.

2010-01-01

The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284

Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.

PubMed

Chen, Peng-Jen; Lin, Meng-Chiung; Lai, Mei-Ju; Lin, Jung-Chun; Lu, Henry Horng-Shing; Tseng, Vincent S

2018-02-01

Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps. We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test. In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as neoplastic or hyperplastic in 0.45 ± 0.07 seconds-shorter than the time required by experts (1.54 ± 1.30 seconds) and nonexperts (1.77 ± 1.37 seconds) (both P < .001). DNN-CAD classified polyps with perfect intra-observer agreement (kappa score of 1). There was a low level of intra-observer and inter-observer agreement in classification among endoscopists. We developed a system called DNN-CAD to identify neoplastic or hyperplastic colorectal polyps less than 5 mm. The system classified polyps with a PPV of 89.6%, and a NPV of 91.5%, and in a shorter time than endoscopists. This deep-learning model has potential for not only endoscopic image recognition but for other forms of medical image analysis, including sonography, computed tomography, and magnetic resonance images. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Structural MRI and Cognitive Correlates in Pest-Control Personnel from Gulf War I

DTIC Science & Technology

2010-04-01

Figure (ROCFT; Corwin & Blysma, 1993) Copying a complex geometric design; assess ability to organize and construct Raw Score...workstations at Boston University School of Medicine where they were reconstructed for morphometric analyses by the study imaging expert, Dr. Killiany...conventional structural MRI and morphometric analysis of K. Sullivan, Ph.D

Localizing Target Structures in Ultrasound Video

PubMed Central

Kwitt, R.; Vasconcelos, N.; Razzaque, S.; Aylward, S.

2013-01-01

The problem of localizing specific anatomic structures using ultrasound (US) video is considered. This involves automatically determining when an US probe is acquiring images of a previously defined object of interest, during the course of an US examination. Localization using US is motivated by the increased availability of portable, low-cost US probes, which inspire applications where inexperienced personnel and even first-time users acquire US data that is then sent to experts for further assessment. This process is of particular interest for routine examinations in underserved populations as well as for patient triage after natural disasters and large-scale accidents, where experts may be in short supply. The proposed localization approach is motivated by research in the area of dynamic texture analysis and leverages several recent advances in the field of activity recognition. For evaluation, we introduce an annotated and publicly available database of US video, acquired on three phantoms. Several experiments reveal the challenges of applying video analysis approaches to US images and demonstrate that good localization performance is possible with the proposed solution. PMID:23746488

Urban, Indoor and Subterranean Navigation Sensors and Systems (Capteurs et systemes de navigation urbains, interieurs et souterrains)

DTIC Science & Technology

2010-11-01

3-10 Multiple Images of an Image Sequence Figure 3-10 A Digital Magnetic Compass from KVH Industries 3-11 Figure 3-11 Earth’s Magnetic Field 3-11...ARINO SENER – Ingenieria y Sistemas S.A Aerospace Division Parque Tecnologico de Madrid Calle Severo Ocho 4 28760 Tres Cantos Madrid Email...experts from government, academia, industry and the military produced an analysis of future navigation sensors and systems whose performance

A game-based crowdsourcing platform for rapidly training middle and high school students to perform biomedical image analysis

NASA Astrophysics Data System (ADS)

Feng, Steve; Woo, Min-jae; Kim, Hannah; Kim, Eunso; Ki, Sojung; Shao, Lei; Ozcan, Aydogan

2016-03-01

We developed an easy-to-use and widely accessible crowd-sourcing tool for rapidly training humans to perform biomedical image diagnostic tasks and demonstrated this platform's ability on middle and high school students in South Korea to diagnose malaria infected red-blood-cells (RBCs) using Giemsa-stained thin blood smears imaged under light microscopes. We previously used the same platform (i.e., BioGames) to crowd-source diagnostics of individual RBC images, marking them as malaria positive (infected), negative (uninfected), or questionable (insufficient information for a reliable diagnosis). Using a custom-developed statistical framework, we combined the diagnoses from both expert diagnosticians and the minimally trained human crowd to generate a gold standard library of malaria-infection labels for RBCs. Using this library of labels, we developed a web-based training and educational toolset that provides a quantified score for diagnosticians/users to compare their performance against their peers and view misdiagnosed cells. We have since demonstrated the ability of this platform to quickly train humans without prior training to reach high diagnostic accuracy as compared to expert diagnosticians. Our initial trial group of 55 middle and high school students has collectively played more than 170 hours, each demonstrating significant improvements after only 3 hours of training games, with diagnostic scores that match expert diagnosticians'. Next, through a national-scale educational outreach program in South Korea we recruited >1660 students who demonstrated a similar performance level after 5 hours of training. We plan to further demonstrate this tool's effectiveness for other diagnostic tasks involving image labeling and aim to provide an easily-accessible and quickly adaptable framework for online training of new diagnosticians.

Intra- and inter-observer reliability of quantitative analysis of the infra-patellar fat pad and comparison between fat- and non-fat-suppressed imaging—Data from the osteoarthritis initiative

PubMed Central

Steidle-Kloc, E.; Wirth, W.; Ruhdorfer, A.; Dannhauer, T.; Eckstein, F.

2015-01-01

The infra-patellar fat pad (IPFP), as intra-articular adipose tissue represents a potential source of pro-inflammatory cytokines and its size has been suggested to be associated with osteoarthritis (OA) of the knee. This study examines inter- and intra-observer reliability of fat-suppressed (fs) and non-fat-suppressed (nfs) MR imaging for determination of IPFP morphological measurements as novel biomarkers. The IPFP of nine right knees of healthy Osteoarthritis Initiative participants was segmented by five readers, using fs and nfs baseline sagittal MRIs. The intra-observer reliability was determined from baseline and 1-year follow-up images. All segmentations were quality controlled (QC) by an expert reader. Reliability was expressed as root mean square coefficient of variation (RMS CV%). After QC, the inter-observer reliability for fs (nfs) imaging was 2.0% (1.1%) for IPFP volume, 2.1%/2.5% (1.6%/1.8%) for anterior/posterior surface areas, 1.8% (1.8%) for depth, and 2.1% (2.4%) for maximum sagittal area. The intra-observer reliability was 3.1% (5.0%) for volume, 2.3%/2.8% (2.5%/2.9%) for anterior/posterior surfaces, 1.9% (3.5%) for depth, and 3.3% (4.5%) for maximum sagittal area. IPFP volume from nfs images was systematically greater (+7.3%) than from fs images, but highly correlated (r = 0.98). The results suggest that quantitative measurements of IPFP morphology can be performed with satisfactory reliability when expert QC is implemented. The IPFP is more clearly depicted in nfs images, and there is a small systematic off-set versus analysis from fs images. However, the high linear relationship between fs and nfs imaging suggests that fs images can be used to analyze IPFP morphology, when nfs images are not available. PMID:26569532

An Image Retrieval and Processing Expert System for the World Wide Web

NASA Technical Reports Server (NTRS)

Rodriguez, Ricardo; Rondon, Angelica; Bruno, Maria I.; Vasquez, Ramon

1998-01-01

This paper presents a system that is being developed in the Laboratory of Applied Remote Sensing and Image Processing at the University of P.R. at Mayaguez. It describes the components that constitute its architecture. The main elements are: a Data Warehouse, an Image Processing Engine, and an Expert System. Together, they provide a complete solution to researchers from different fields that make use of images in their investigations. Also, since it is available to the World Wide Web, it provides remote access and processing of images.

A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images

NASA Astrophysics Data System (ADS)

Wang, Zhihua; Yang, Xiaomei; Lu, Chen; Yang, Fengshuo

2018-07-01

Automatic updating of land use/cover change (LUCC) databases using high spatial resolution images (HSRI) is important for environmental monitoring and policy making, especially for coastal areas that connect the land and coast and that tend to change frequently. Many object-based change detection methods are proposed, especially those combining historical LUCC with HSRI. However, the scale parameter(s) segmenting the serial temporal images, which directly determines the average object size, is hard to choose without experts' intervention. And the samples transferred from historical LUCC also need experts' intervention to avoid insufficient or wrong samples. With respect to the scale parameter(s) choosing, a Scale Self-Adapting Segmentation (SSAS) approach based on the exponential sampling of a scale parameter and location of the local maximum of a weighted local variance was proposed to determine the scale selection problem when segmenting images constrained by LUCC for detecting changes. With respect to the samples transferring, Knowledge Transfer (KT), a classifier trained on historical images with LUCC and applied in the classification of updated images, was also proposed. Comparison experiments were conducted in a coastal area of Zhujiang, China, using SPOT 5 images acquired in 2005 and 2010. The results reveal that (1) SSAS can segment images more effectively without intervention of experts. (2) KT can also reach the maximum accuracy of samples transfer without experts' intervention. Strategy SSAS + KT would be a good choice if the temporal historical image and LUCC match, and the historical image and updated image are obtained from the same resource.

PIZZARO: Forensic analysis and restoration of image and video data.

PubMed

Kamenicky, Jan; Bartos, Michal; Flusser, Jan; Mahdian, Babak; Kotera, Jan; Novozamsky, Adam; Saic, Stanislav; Sroubek, Filip; Sorel, Michal; Zita, Ales; Zitova, Barbara; Sima, Zdenek; Svarc, Petr; Horinek, Jan

2016-07-01

This paper introduces a set of methods for image and video forensic analysis. They were designed to help to assess image and video credibility and origin and to restore and increase image quality by diminishing unwanted blur, noise, and other possible artifacts. The motivation came from the best practices used in the criminal investigation utilizing images and/or videos. The determination of the image source, the verification of the image content, and image restoration were identified as the most important issues of which automation can facilitate criminalists work. Novel theoretical results complemented with existing approaches (LCD re-capture detection and denoising) were implemented in the PIZZARO software tool, which consists of the image processing functionality as well as of reporting and archiving functions to ensure the repeatability of image analysis procedures and thus fulfills formal aspects of the image/video analysis work. Comparison of new proposed methods with the state of the art approaches is shown. Real use cases are presented, which illustrate the functionality of the developed methods and demonstrate their applicability in different situations. The use cases as well as the method design were solved in tight cooperation of scientists from the Institute of Criminalistics, National Drug Headquarters of the Criminal Police and Investigation Service of the Police of the Czech Republic, and image processing experts from the Czech Academy of Sciences. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An active learning approach for rapid characterization of endothelial cells in human tumors.

PubMed

Padmanabhan, Raghav K; Somasundar, Vinay H; Griffith, Sandra D; Zhu, Jianliang; Samoyedny, Drew; Tan, Kay See; Hu, Jiahao; Liao, Xuejun; Carin, Lawrence; Yoon, Sam S; Flaherty, Keith T; Dipaola, Robert S; Heitjan, Daniel F; Lal, Priti; Feldman, Michael D; Roysam, Badrinath; Lee, William M F

2014-01-01

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.

A deep learning framework to discern and count microscopic nematode eggs.

PubMed

Akintayo, Adedotun; Tylka, Gregory L; Singh, Asheesh K; Ganapathysubramanian, Baskar; Singh, Arti; Sarkar, Soumik

2018-06-14

In order to identify and control the menace of destructive pests via microscopic image-based identification state-of-the art deep learning architecture is demonstrated on the parasitic worm, the soybean cyst nematode (SCN), Heterodera glycines. Soybean yield loss is negatively correlated with the density of SCN eggs that are present in the soil. While there has been progress in automating extraction of egg-filled cysts and eggs from soil samples counting SCN eggs obtained from soil samples using computer vision techniques has proven to be an extremely difficult challenge. Here we show that a deep learning architecture developed for rare object identification in clutter-filled images can identify and count the SCN eggs. The architecture is trained with expert-labeled data to effectively build a machine learning model for quantifying SCN eggs via microscopic image analysis. We show dramatic improvements in the quantification time of eggs while maintaining human-level accuracy and avoiding inter-rater and intra-rater variabilities. The nematode eggs are correctly identified even in complex, debris-filled images that are often difficult for experts to identify quickly. Our results illustrate the remarkable promise of applying deep learning approaches to phenotyping for pest assessment and management.

Analysis of Visual Interpretation of Satellite Data

NASA Astrophysics Data System (ADS)

Svatonova, H.

2016-06-01

Millions of people of all ages and expertise are using satellite and aerial data as an important input for their work in many different fields. Satellite data are also gradually finding a new place in education, especially in the fields of geography and in environmental issues. The article presents the results of an extensive research in the area of visual interpretation of image data carried out in the years 2013 - 2015 in the Czech Republic. The research was aimed at comparing the success rate of the interpretation of satellite data in relation to a) the substrates (to the selected colourfulness, the type of depicted landscape or special elements in the landscape) and b) to selected characteristics of users (expertise, gender, age). The results of the research showed that (1) false colour images have a slightly higher percentage of successful interpretation than natural colour images, (2) colourfulness of an element expected or rehearsed by the user (regardless of the real natural colour) increases the success rate of identifying the element (3) experts are faster in interpreting visual data than non-experts, with the same degree of accuracy of solving the task, and (4) men and women are equally successful in the interpretation of visual image data.

[The future of telepathology. An Internet "distributed system" with "open standards"].

PubMed

Brauchli, K; Helfrich, M; Christen, H; Jundt, G; Haroske, G; Mihatsch, M; Oberli, H; Oberholzer, M

2002-05-01

With the availability of Internet, the interest in the possibilities of telepathology has increased considerably. In the foreground is thereby the need of the non-expert to bring in the opinions of experts on morphological findings by means of a fast and simple procedure. The new telepathology system iPath is in compliance with these needs. The system is based on small, but when possible independently working modules. This concept allows a simple adaptation of the system to the individual environment of the user (e.g. for different cameras, frame-grabbers, microscope steering tables etc.) and for individual needs. iPath has been in use for 6 months with various working groups. In telepathology a distinction is made between "passive" and "active" consultations but for both forms a non-expert brings in the opinion of an expert. In an active consultation both are in direct connection with each other (orally or via a chat-function), this is however not the case with a passive consultation. An active consultation can include the interactive discussion of the expert with the non-expert on images in an image database or the direct interpretation of images from a microscope by the expert. Four software modules are available for a free and as fast as possible application: (1) the module "Microscope control", (2) the module "Connector" (insertion of images directly from the microscope without a motorized microscope), (3) the module "Client-application" via the web-browser and (4) the module "Server" with a database. The server is placed in the internet and not behind a firewall. The server permanently receives information from the periphery and returns the information to the periphery on request. The only thing which the expert, the non-expert and the microscope have to know is how contact can made with the server.

ProstateAnalyzer: Web-based medical application for the management of prostate cancer using multiparametric MR imaging.

PubMed

Mata, Christian; Walker, Paul M; Oliver, Arnau; Brunotte, François; Martí, Joan; Lalande, Alain

2016-01-01

In this paper, we present ProstateAnalyzer, a new web-based medical tool for prostate cancer diagnosis. ProstateAnalyzer allows the visualization and analysis of magnetic resonance images (MRI) in a single framework. ProstateAnalyzer recovers the data from a PACS server and displays all the associated MRI images in the same framework, usually consisting of 3D T2-weighted imaging for anatomy, dynamic contrast-enhanced MRI for perfusion, diffusion-weighted imaging in the form of an apparent diffusion coefficient (ADC) map and MR Spectroscopy. ProstateAnalyzer allows annotating regions of interest in a sequence and propagates them to the others. From a representative case, the results using the four visualization platforms are fully detailed, showing the interaction among them. The tool has been implemented as a Java-based applet application to facilitate the portability of the tool to the different computer architectures and software and allowing the possibility to work remotely via the web. ProstateAnalyzer enables experts to manage prostate cancer patient data set more efficiently. The tool allows delineating annotations by experts and displays all the required information for use in diagnosis. According to the current European Society of Urogenital Radiology guidelines, it also includes the PI-RADS structured reporting scheme.

Inconsistency in the analysis of morphological deformities in chironomidae (Insecta: Diptera) larvae.

PubMed

Salmelin, Johanna; Vuori, Kari-Matti; Hämäläinen, Heikki

2015-08-01

The incidence of morphological deformities of chironomid larvae as an indicator of sediment toxicity has been studied for decades. However, standards for deformity analysis are lacking. The authors evaluated whether 25 experts diagnosed larval deformities in a similar manner. Based on high-quality digital images, the experts rated 211 menta of Chironomus spp. larvae as normal or deformed. The larvae were from a site with polluted sediments or from a reference site. The authors revealed this to a random half of the experts, and the rest conducted the assessment blind. The authors quantified the interrater agreement by kappa coefficient, tested whether open and blind assessments differed in deformity incidence and in differentiation between the sites, and identified those deformity types rated most consistently or inconsistently. The total deformity incidence varied greatly, from 10.9% to 66.4% among experts. Kappa coefficient across rater pairs averaged 0.52, indicating insufficient agreement. The deformity types rated most consistently were those missing teeth or with extra teeth. The open and blind assessments did not differ, but differentiation between sites was clearest for raters who counted primarily absolute deformities such as missing and extra teeth and excluded apparent mechanical aberrations or deviations in tooth size or symmetry. The highly differing criteria in deformity assignment have likely led to inconsistent results in midge larval deformity studies and indicate an urgent need for standardization of the analysis. © 2015 SETAC.

Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation

PubMed Central

Beijbom, Oscar; Edmunds, Peter J.; Roelfsema, Chris; Smith, Jennifer; Kline, David I.; Neal, Benjamin P.; Dunlap, Matthew J.; Moriarty, Vincent; Fan, Tung-Yung; Tan, Chih-Jui; Chan, Stephen; Treibitz, Tali; Gamst, Anthony; Mitchell, B. Greg; Kriegman, David

2015-01-01

Global climate change and other anthropogenic stressors have heightened the need to rapidly characterize ecological changes in marine benthic communities across large scales. Digital photography enables rapid collection of survey images to meet this need, but the subsequent image annotation is typically a time consuming, manual task. We investigated the feasibility of using automated point-annotation to expedite cover estimation of the 17 dominant benthic categories from survey-images captured at four Pacific coral reefs. Inter- and intra- annotator variability among six human experts was quantified and compared to semi- and fully- automated annotation methods, which are made available at coralnet.ucsd.edu. Our results indicate high expert agreement for identification of coral genera, but lower agreement for algal functional groups, in particular between turf algae and crustose coralline algae. This indicates the need for unequivocal definitions of algal groups, careful training of multiple annotators, and enhanced imaging technology. Semi-automated annotation, where 50% of the annotation decisions were performed automatically, yielded cover estimate errors comparable to those of the human experts. Furthermore, fully-automated annotation yielded rapid, unbiased cover estimates but with increased variance. These results show that automated annotation can increase spatial coverage and decrease time and financial outlay for image-based reef surveys. PMID:26154157

Ontology-based classification of remote sensing images using spectral rules

NASA Astrophysics Data System (ADS)

Andrés, Samuel; Arvor, Damien; Mougenot, Isabelle; Libourel, Thérèse; Durieux, Laurent

2017-05-01

Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for "domain" experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images

PubMed Central

Varando, Gherardo; Benavides-Piccione, Ruth; Muñoz, Alberto; Kastanauskaite, Asta; Bielza, Concha; Larrañaga, Pedro; DeFelipe, Javier

2018-01-01

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections. PMID:29875639

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images.

PubMed

Varando, Gherardo; Benavides-Piccione, Ruth; Muñoz, Alberto; Kastanauskaite, Asta; Bielza, Concha; Larrañaga, Pedro; DeFelipe, Javier

2018-01-01

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.

Object-based image analysis and data mining for building ontology of informal urban settlements

NASA Astrophysics Data System (ADS)

Khelifa, Dejrriri; Mimoun, Malki

2012-11-01

During recent decades, unplanned settlements have been appeared around the big cities in most developing countries and as consequence, numerous problems have emerged. Thus the identification of different kinds of settlements is a major concern and challenge for authorities of many countries. Very High Resolution (VHR) Remotely Sensed imagery has proved to be a very promising way to detect different kinds of settlements, especially through the using of new objectbased image analysis (OBIA). The most important key is in understanding what characteristics make unplanned settlements differ from planned ones, where most experts characterize unplanned urban areas by small building sizes at high densities, no orderly road arrangement and Lack of green spaces. Knowledge about different kinds of settlements can be captured as a domain ontology that has the potential to organize knowledge in a formal, understandable and sharable way. In this work we focus on extracting knowledge from VHR images and expert's knowledge. We used an object based strategy by segmenting a VHR image taken over urban area into regions of homogenous pixels at adequate scale level and then computing spectral, spatial and textural attributes for each region to create objects. A genetic-based data mining was applied to generate high predictive and comprehensible classification rules based on selected samples from the OBIA result. Optimized intervals of relevant attributes are found, linked with land use types for forming classification rules. The unplanned areas were separated from the planned ones, through analyzing of the line segments detected from the input image. Finally a simple ontology was built based on the previous processing steps. The approach has been tested to VHR images of one of the biggest Algerian cities, that has grown considerably in recent decades.

Extreme Tele-Echocardiography: Methodology for Remote Guidance of In-Flight Echocardiography Aboard the International Space Station

NASA Technical Reports Server (NTRS)

Martin, David S.; Borowski, Allan; Bungo, Michael W.; Gladding, Patrick; Greenberg, Neil; Hamilton, Doug; Levine, Benjamin D.; Lee, Stuart M.; Norwood, Kelly; Platts, Steven H.;

Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images

NASA Technical Reports Server (NTRS)

Shekhar, R.; Cothren, R. M.; Vince, D. G.; Chandra, S.; Thomas, J. D.; Cornhill, J. F.

1999-01-01

Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83-0.97) and William's index values (range 0.37-0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements.

Radiology's Achilles' heel: error and variation in the interpretation of the Röntgen image.

PubMed

Robinson, P J

1997-11-01

The performance of the human eye and brain has failed to keep pace with the enormous technical progress in the first full century of radiology. Errors and variations in interpretation now represent the weakest aspect of clinical imaging. Those interpretations which differ from the consensus view of a panel of "experts" may be regarded as errors; where experts fail to achieve consensus, differing reports are regarded as "observer variation". Errors arise from poor technique, failures of perception, lack of knowledge and misjudgments. Observer variation is substantial and should be taken into account when different diagnostic methods are compared; in many cases the difference between observers outweighs the difference between techniques. Strategies for reducing error include attention to viewing conditions, training of the observers, availability of previous films and relevant clinical data, dual or multiple reporting, standardization of terminology and report format, and assistance from computers. Digital acquisition and display will probably not affect observer variation but the performance of radiologists, as measured by receiver operating characteristic (ROC) analysis, may be improved by computer-directed search for specific image features. Other current developments show that where image features can be comprehensively described, computer analysis can replace the perception function of the observer, whilst the function of interpretation can in some cases be performed better by artificial neural networks. However, computer-assisted diagnosis is still in its infancy and complete replacement of the human observer is as yet a remote possibility.

Fast automated analysis of strong gravitational lenses with convolutional neural networks.

PubMed

Hezaveh, Yashar D; Levasseur, Laurence Perreault; Marshall, Philip J

2017-08-30

Quantifying image distortions caused by strong gravitational lensing-the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures-and estimating the corresponding matter distribution of these structures (the 'gravitational lens') has primarily been performed using maximum likelihood modelling of observations. This procedure is typically time- and resource-consuming, requiring sophisticated lensing codes, several data preparation steps, and finding the maximum likelihood model parameters in a computationally expensive process with downhill optimizers. Accurate analysis of a single gravitational lens can take up to a few weeks and requires expert knowledge of the physical processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. Here we report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light can be made fast and automated using independent component analysis of multi-filter imaging data. Our networks can recover the parameters of the 'singular isothermal ellipsoid' density profile, which is commonly used to model strong lensing systems, with an accuracy comparable to the uncertainties of sophisticated models but about ten million times faster: 100 systems in approximately one second on a single graphics processing unit. These networks can provide a way for non-experts to obtain estimates of lensing parameters for large samples of data.

New approach to gallbladder ultrasonic images analysis and lesions recognition.

PubMed

Bodzioch, Sławomir; Ogiela, Marek R

2009-03-01

This paper presents a new approach to gallbladder ultrasonic image processing and analysis towards detection of disease symptoms on processed images. First, in this paper, there is presented a new method of filtering gallbladder contours from USG images. A major stage in this filtration is to segment and section off areas occupied by the said organ. In most cases this procedure is based on filtration that plays a key role in the process of diagnosing pathological changes. Unfortunately ultrasound images present among the most troublesome methods of analysis owing to the echogenic inconsistency of structures under observation. This paper provides for an inventive algorithm for the holistic extraction of gallbladder image contours. The algorithm is based on rank filtration, as well as on the analysis of histogram sections on tested organs. The second part concerns detecting lesion symptoms of the gallbladder. Automating a process of diagnosis always comes down to developing algorithms used to analyze the object of such diagnosis and verify the occurrence of symptoms related to given affection. Usually the final stage is to make a diagnosis based on the detected symptoms. This last stage can be carried out through either dedicated expert systems or more classic pattern analysis approach like using rules to determine illness basing on detected symptoms. This paper discusses the pattern analysis algorithms for gallbladder image interpretation towards classification of the most frequent illness symptoms of this organ.

Blind image deconvolution using the Fields of Experts prior

NASA Astrophysics Data System (ADS)

Dong, Wende; Feng, Huajun; Xu, Zhihai; Li, Qi

2012-11-01

In this paper, we present a method for single image blind deconvolution. To improve its ill-posedness, we formulate the problem under Bayesian probabilistic framework and use a prior named Fields of Experts (FoE) which is learnt from natural images to regularize the latent image. Furthermore, due to the sparse distribution of the point spread function (PSF), we adopt a Student-t prior to regularize it. An improved alternating minimization (AM) approach is proposed to solve the resulted optimization problem. Experiments on both synthetic and real world blurred images show that the proposed method can achieve results of high quality.

Web-based health services and clinical decision support.

PubMed

Jegelevicius, Darius; Marozas, Vaidotas; Lukosevicius, Arunas; Patasius, Martynas

2004-01-01

The purpose of this study was the development of a Web-based e-health service for comprehensive assistance and clinical decision support. The service structure consists of a Web server, a PHP-based Web interface linked to a clinical SQL database, Java applets for interactive manipulation and visualization of signals and a Matlab server linked with signal and data processing algorithms implemented by Matlab programs. The service ensures diagnostic signal- and image analysis-sbased clinical decision support. By using the discussed methodology, a pilot service for pathology specialists for automatic calculation of the proliferation index has been developed. Physicians use a simple Web interface for uploading the pictures under investigation to the server; subsequently a Java applet interface is used for outlining the region of interest and, after processing on the server, the requested proliferation index value is calculated. There is also an "expert corner", where experts can submit their index estimates and comments on particular images, which is especially important for system developers. These expert evaluations are used for optimization and verification of automatic analysis algorithms. Decision support trials have been conducted for ECG and ophthalmology ultrasonic investigations of intraocular tumor differentiation. Data mining algorithms have been applied and decision support trees constructed. These services are under implementation by a Web-based system too. The study has shown that the Web-based structure ensures more effective, flexible and accessible services compared with standalone programs and is very convenient for biomedical engineers and physicians, especially in the development phase.

Physics-based deformable organisms for medical image analysis

NASA Astrophysics Data System (ADS)

Hamarneh, Ghassan; McIntosh, Chris

2005-04-01

Previously, "Deformable organisms" were introduced as a novel paradigm for medical image analysis that uses artificial life modelling concepts. Deformable organisms were designed to complement the classical bottom-up deformable models methodologies (geometrical and physical layers), with top-down intelligent deformation control mechanisms (behavioral and cognitive layers). However, a true physical layer was absent and in order to complete medical image segmentation tasks, deformable organisms relied on pure geometry-based shape deformations guided by sensory data, prior structural knowledge, and expert-generated schedules of behaviors. In this paper we introduce the use of physics-based shape deformations within the deformable organisms framework yielding additional robustness by allowing intuitive real-time user guidance and interaction when necessary. We present the results of applying our physics-based deformable organisms, with an underlying dynamic spring-mass mesh model, to segmenting and labelling the corpus callosum in 2D midsagittal magnetic resonance images.

Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials

DOEpatents

Boucheron, Laura E

2013-07-16

Quantitative object and spatial arrangement-level analysis of tissue are detailed using expert (pathologist) input to guide the classification process. A two-step method is disclosed for imaging tissue, by classifying one or more biological materials, e.g. nuclei, cytoplasm, and stroma, in the tissue into one or more identified classes on a pixel-by-pixel basis, and segmenting the identified classes to agglomerate one or more sets of identified pixels into segmented regions. Typically, the one or more biological materials comprises nuclear material, cytoplasm material, and stromal material. The method further allows a user to markup the image subsequent to the classification to re-classify said materials. The markup is performed via a graphic user interface to edit designated regions in the image.

Utilisation de la teledetection, des SIG et de l'intelligence artificielle pour determiner le niveau de susceptibilite aux mouvements de terrain: Application dans les Andes de la Bolivie

NASA Astrophysics Data System (ADS)

Peloquin, Stephane

1999-11-01

The socio-economic impact of mass movements for our society is getting more and more serious. The loss of lives and economic losses are now ten times greater than they were at the beginning of the decade. In the hope of reducing these impacts, it is essential to adopt a preventive policy that will encourage mapping of mass movement susceptibility level (MMSL) in critical zones. However, this task is complex and only experts using present techniques can provide satisfactory results. To make possible the production of these maps by a larger number of individuals, we have developed an expert system called EXPERIM that uses remote sensing data and geographic information systems to facilitate the complex tasks without requiring the user to be highly competent in this field of study. This thesis presents the results obtained from a complete strategy developed for a region surrounding Cochabamba, Bolivia. The operational expert system prototype will soon be integrated within the watershed management program directed by the local executing organisation PROMIC. The knowledge acquisition and its expression in concrete terms constitute the principal axis of this research, while the results obtained are the heart of the EXPERIM expert system. These strategic steps aim to establish a knowledge base of data and rules that describe field conditions for each MMSL. We have been able to extract this information by using binary discriminant analysis of a MMSL map produced by an expert for a pilot zone called Cuenca Taquina, which is geoecologically representative of the 38 neighbouring watersheds. Using this technique, we were able to establish a sensitivity model that recreates the expert's map with a success rate of 89% and 78% when two or three MMS levels are used. Based on a detailed analysis of the susceptibility model it was evident that stability conditions are the result of the topographic, geologic and geomorphologic environments. The level of susceptibility was found to be independent of the vegetation condition. In order to apply the model to the surrounding watersheds, we integrated remotely sensed data within the spatial database to map the presence/absence of five essential geoecological units required by the susceptibility model. This was done using a hierarchical classification method. Three sensors were evaluated: Landsat, SPOT and RADARSAT. In the elaboration of this specific step, we evaluated the most efficient spectral band combinations within each image and between images for each of the five geoecological units. For each of the land cover types, the analysis shows that LANDSAT constitutes the most powerful sensor to map these units and that image fusion does not provide significantly better results when compared to the extra amount of work that this requires. Using remote sensing data instead of field data or airphotograph interpretation in watersheds where only topographic data are available decreases the level of accuracy by less than 10%.

Mechanisms and neural basis of object and pattern recognition: a study with chess experts.

PubMed

Bilalić, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-11-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and novices performing chess-related and -unrelated (visual) search tasks. As expected, the superiority of experts was limited to the chess-specific task, as there were no differences in a control task that used the same chess stimuli but did not require chess-specific recognition. The analysis of eye movements showed that experts immediately and exclusively focused on the relevant aspects in the chess task, whereas novices also examined irrelevant aspects. With random chess positions, when pattern knowledge could not be used to guide perception, experts nevertheless maintained an advantage. Experts' superior domain-specific parafoveal vision, a consequence of their knowledge about individual domain-specific symbols, enabled improved object recognition. Functional magnetic resonance imaging corroborated this differentiation between object and pattern recognition and showed that chess-specific object recognition was accompanied by bilateral activation of the occipitotemporal junction, whereas chess-specific pattern recognition was related to bilateral activations in the middle part of the collateral sulci. Using the expertise approach together with carefully chosen controls and multiple dependent measures, we identified object and pattern recognition as two essential cognitive processes in expert visual cognition, which may also help to explain the mechanisms of everyday perception.

[Concordance among analysts from Latin-American laboratories for rice grain appearance determination using a gallery of digital images].

PubMed

Avila, Manuel; Graterol, Eduardo; Alezones, Jesús; Criollo, Beisy; Castillo, Dámaso; Kuri, Victoria; Oviedo, Norman; Moquete, Cesar; Romero, Marbella; Hanley, Zaida; Taylor, Margie

2012-06-01

The appearance of rice grain is a key aspect in quality determination. Mainly, this analysis is performed by expert analysts through visual observation; however, due to the subjective nature of the analysis, the results may vary among analysts. In order to evaluate the concordance between analysts from Latin-American rice quality laboratories for rice grain appearance through digital images, an inter-laboratory test was performed with ten analysts and images of 90 grains captured with a high resolution scanner. Rice grains were classified in four categories including translucent, chalky, white belly, and damaged grain. Data was categorized using statistic parameters like mode and its frequency, the relative concordance, and the reproducibility parameter kappa. Additionally, a referential image gallery of typical grain for each category was constructed based on mode frequency. Results showed a Kappa value of 0.49, corresponding to a moderate reproducibility, attributable to subjectivity in the visual analysis of grain images. These results reveal the need for standardize the evaluation criteria among analysts to improve the confidence of the determination of rice grain appearance.

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

PubMed Central

Maji, Pradipta; Roy, Shaswati

2015-01-01

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961

Simulation of bright-field microscopy images depicting pap-smear specimen

PubMed Central

Malm, Patrik; Brun, Anders; Bengtsson, Ewert

2015-01-01

As digital imaging is becoming a fundamental part of medical and biomedical research, the demand for computer-based evaluation using advanced image analysis is becoming an integral part of many research projects. A common problem when developing new image analysis algorithms is the need of large datasets with ground truth on which the algorithms can be tested and optimized. Generating such datasets is often tedious and introduces subjectivity and interindividual and intraindividual variations. An alternative to manually created ground-truth data is to generate synthetic images where the ground truth is known. The challenge then is to make the images sufficiently similar to the real ones to be useful in algorithm development. One of the first and most widely studied medical image analysis tasks is to automate screening for cervical cancer through Pap-smear analysis. As part of an effort to develop a new generation cervical cancer screening system, we have developed a framework for the creation of realistic synthetic bright-field microscopy images that can be used for algorithm development and benchmarking. The resulting framework has been assessed through a visual evaluation by experts with extensive experience of Pap-smear images. The results show that images produced using our described methods are realistic enough to be mistaken for real microscopy images. The developed simulation framework is very flexible and can be modified to mimic many other types of bright-field microscopy images. © 2015 The Authors. Published by Wiley Periodicals, Inc. on behalf of ISAC PMID:25573002

Inaccuracy of age assessment from images of postpubescent subjects in cases of alleged child pornography.

PubMed

Rosenbloom, Arlan L

2013-03-01

Despite frequent medical expert testimony authoritatively stating that images of individuals who are postpubescent indicate age less than 18 and therefore, child pornography, developmental experts have noted that a scientific basis for such estimation is lacking. In fact, recent studies have demonstrated a high degree of inaccuracy in such estimates, and that the stage of breast development often used as indicative of age under 18 years is present in a substantial percentage of adult women. Ten images of adult women from legitimate pornographic sites promoting youthful images were shown to 16 pediatric endocrinologists expert in evaluating maturation, who determined whether or not the individuals represented were under 18 years of age. They also provided information about what features were most important in their evaluations. Sixty-nine percent of the 160 estimates were that the images represented females under 18 years of age. There was wide variability in the designation of importance of the various features of maturation in reaching conclusions, with breast development and facial appearance considered most important. This study confirms that medical testimony, even by experts in adolescent development, can deem images of adult women selected for their youthful appearance to be under age 18 two thirds of the time. Thus, important as prosecuting users of child pornographic material may be, justice requires the avoidance of testimony that is not scientifically based.

Development of an expert data reduction assistant

NASA Technical Reports Server (NTRS)

Miller, Glenn E.; Johnston, Mark D.; Hanisch, Robert J.

1993-01-01

We propose the development of an expert system tool for the management and reduction of complex datasets. the proposed work is an extension of a successful prototype system for the calibration of CCD (charge coupled device) images developed by Dr. Johnston in 1987. (ref.: Proceedings of the Goddard Conference on Space Applications of Artificial Intelligence). The reduction of complex multi-parameter data sets presents severe challenges to a scientist. Not only must a particular data analysis system be mastered, (e.g. IRAF/SDAS/MIDAS), large amounts of data can require many days of tedious work and supervision by the scientist for even the most straightforward reductions. The proposed Expert Data Reduction Assistant will help the scientist overcome these obstacles by developing a reduction plan based on the data at hand and producing a script for the reduction of the data in a target common language.

Spatial information technologies for remote sensing today and tomorrow; Proceedings of the Ninth Pecora Symposium, Sioux Falls, SD, October 2-4, 1984

NASA Technical Reports Server (NTRS)

1984-01-01

Topics discussed at the symposium include hardware, geographic information system (GIS) implementation, processing remotely sensed data, spatial data structures, and NASA programs in remote sensing information systems. Attention is also given GIS applications, advanced techniques, artificial intelligence, graphics, spatial navigation, and classification. Papers are included on the design of computer software for geographic image processing, concepts for a global resource information system, algorithm development for spatial operators, and an application of expert systems technology to remotely sensed image analysis.

Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images.

PubMed

Cunefare, David; Cooper, Robert F; Higgins, Brian; Katz, David F; Dubra, Alfredo; Carroll, Joseph; Farsiu, Sina

2016-05-01

Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice's coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice's coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images.

Enhancement of chest radiographs using eigenimage processing

NASA Astrophysics Data System (ADS)

Bones, Philip J.; Butler, Anthony P. H.; Hurrell, Michael

2006-08-01

Frontal chest radiographs ("chest X-rays") are routinely used by medical personnel to assess patients for a wide range of suspected disorders. Often large numbers of images need to be analyzed. Furthermore, at times the images need to analyzed ("reported") when no radiological expert is available. A system which enhances the images in such a way that abnormalities are more obvious is likely to reduce the chance that an abnormality goes unnoticed. The authors previously reported the use of principal components analysis to derive a basis set of eigenimages from a training set made up of images from normal subjects. The work is here extended to investigate how best to emphasize the abnormalities in chest radiographs. Results are also reported for various forms of image normalizing transformations used in performing the eigenimage processing.

Expert system for controlling plant growth in a contained environment

NASA Technical Reports Server (NTRS)

May, George A. (Inventor); Lanoue, Mark Allen (Inventor); Bethel, Matthew (Inventor); Ryan, Robert E. (Inventor)

2011-01-01

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an "expert system" which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the "expert system" remotely, to assess activity within the growth chamber, and can override the "expert system".

Expert system for controlling plant growth in a contained environment

NASA Technical Reports Server (NTRS)

May, George A. (Inventor); Lanoue, Mark Allen (Inventor); Bethel, Matthew (Inventor); Ryan, Robert E. (Inventor)

2009-01-01

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an ''expert system'' which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the ''expert system'' remotely, to assess activity within the growth chamber, and can override the ''expert system''.

Color Fundus Photography versus Fluorescein Angiography in Identification of the Macular Center and Zone in Retinopathy of Prematurity

PubMed Central

Patel, Samir N.; Klufas, Michael A.; Ryan, Michael C.; Jonas, Karyn E.; Ostmo, Susan; Martinez-Castellanos, Maria Ana; Berrocal, Audina M.; Chiang, Michael F.; Chan, R.V. Paul

2016-01-01

Purpose To examine the utility of fluorescein angiography (FA) in identification of the macular center and the diagnosis of zone in patients with retinopathy of prematurity (ROP). Design Validity and reliability analysis of diagnostic tools Methods 32 sets (16 color fundus photographs; 16 color fundus photographs paired with the corresponding FA) of wide-angle retinal images obtained from 16 eyes of eight infants with ROP were compiled on a secure web site. 9 ROP experts (3 pediatric ophthalmologists; 6 vitreoretinal surgeons) participated in the study. For each image set, experts identified the macular center and provided a diagnosis of zone. Main Outcome Measures (1) Sensitivity and specificity of zone diagnosis (2) “Computer facilitated diagnosis of zone,” based on precise measurement of the macular center, optic disc center, and peripheral ROP. Results Computer facilitated diagnosis of zone agreed with the expert’s diagnosis of zone in 28/45 (62%) cases using color fundus photographs and in 31/45 (69%) cases using FA. Mean (95% CI) sensitivity for detection of zone I by experts as compared to a consensus reference standard diagnosis when interpreting the color fundus images alone versus interpreting the color fundus photographs and FA was 47% (35.3% – 59.3%) and 61.1% (48.9% – 72.4%), respectively, (t(9) ≥ (2.063), p = 0.073). Conclusions There is a marginally significant difference in zone diagnosis when using color fundus photographs compared to using color fundus photographs and the corresponding fluorescein angiograms. There is inconsistency between traditional zone diagnosis (based on ophthalmoscopic exam and image review) compared to a computer-facilitated diagnosis of zone. PMID:25637180

Cellscope Aquatic: a Lab Quality, Portable Cellphone-Based Microscope for On-Site Collection of Algae Images

NASA Astrophysics Data System (ADS)

Steinberg, S. J.; Howard, M. D.

2016-02-01

Collecting algae samples from the field presents issues of specimen damage or degradation caused by preservation methods, handling and transport to laboratory facilities for identification. Traditionally, in-field collection of high quality microscopic images has not been possible due to the size, weight and fragility of high quality instruments and training of field staff in species identification. Scientists at the Southern California Coastal Water Research Project (SCCWRP) in collaboration with the Fletcher Lab, University of California Berkeley, Department of Bioengineering, tested and translated Fletcher's original medical CellScope for use in environmental monitoring applications. Field tests conducted by SCCWRP in 2014 led to modifications of the clinical CellScope to one better suited to in-field microscopic imaging for aquatic organisms. SCCWRP subsequently developed a custom cell-phone application to acquire microscopic imagery using the "CellScope Aquatic "in combination with other cell-phone derived field data (e.g. GPS location, date, time and other field observations). Data and imagery collected in-field may be transmitted in real-time to a web-based data system for tele-taxonomy evaluation and assessment by experts in the office. These hardware and software tools was tested in field in a variety of conditions and settings by multiple algae experts during the spring and summer of 2015 to further test and refine the CellScope Aquatic platform. The CellScope Aquatic provides an easy-to-use, affordable, lightweight, professional quality, data collection platform for environmental monitoring. Our ongoing efforts will focus on development of real-time expert systems for data analysis and image processing, to provide onsite feedback to field scientists.

Determination of the ecological connectivity between landscape patches obtained using the knowledge engineer (expert) classification technique

NASA Astrophysics Data System (ADS)

Selim, Serdar; Sonmez, Namik Kemal; Onur, Isin; Coslu, Mesut

2017-10-01

Connection of similar landscape patches with ecological corridors supports habitat quality of these patches, increases urban ecological quality, and constitutes an important living and expansion area for wild life. Furthermore, habitat connectivity provided by urban green areas is supporting biodiversity in urban areas. In this study, possible ecological connections between landscape patches, which were achieved by using Expert classification technique and modeled with probabilistic connection index. Firstly, the reflection responses of plants to various bands are used as data in hypotheses. One of the important features of this method is being able to use more than one image at the same time in the formation of the hypothesis. For this reason, before starting the application of the Expert classification, the base images are prepared. In addition to the main image, the hypothesis conditions were also created for each class with the NDVI image which is commonly used in the vegetation researches. Besides, the results of the previously conducted supervised classification were taken into account. We applied this classification method by using the raster imagery with user-defined variables. Hereupon, to provide ecological connections of the tree cover which was achieved from the classification, we used Probabilistic Connection (PC) index. The probabilistic connection model which is used for landscape planning and conservation studies via detecting and prioritization critical areas for ecological connection characterizes the possibility of direct connection between habitats. As a result we obtained over % 90 total accuracy in accuracy assessment analysis. We provided ecological connections with PC index and we created inter-connected green spaces system. Thus, we offered and implicated green infrastructure system model takes place in the agenda of recent years.

Image Quality Assessment of JPEG Compressed Mars Science Laboratory Mastcam Images using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Kerner, H. R.; Bell, J. F., III; Ben Amor, H.

2017-12-01

The Mastcam color imaging system on the Mars Science Laboratory Curiosity rover acquires images within Gale crater for a variety of geologic and atmospheric studies. Images are often JPEG compressed before being downlinked to Earth. While critical for transmitting images on a low-bandwidth connection, this compression can result in image artifacts most noticeable as anomalous brightness or color changes within or near JPEG compression block boundaries. In images with significant high-frequency detail (e.g., in regions showing fine layering or lamination in sedimentary rocks), the image might need to be re-transmitted losslessly to enable accurate scientific interpretation of the data. The process of identifying which images have been adversely affected by compression artifacts is performed manually by the Mastcam science team, costing significant expert human time. To streamline the tedious process of identifying which images might need to be re-transmitted, we present an input-efficient neural network solution for predicting the perceived quality of a compressed Mastcam image. Most neural network solutions require large amounts of hand-labeled training data for the model to learn the target mapping between input (e.g. distorted images) and output (e.g. quality assessment). We propose an automatic labeling method using joint entropy between a compressed and uncompressed image to avoid the need for domain experts to label thousands of training examples by hand. We use automatically labeled data to train a convolutional neural network to estimate the probability that a Mastcam user would find the quality of a given compressed image acceptable for science analysis. We tested our model on a variety of Mastcam images and found that the proposed method correlates well with image quality perception by science team members. When assisted by our proposed method, we estimate that a Mastcam investigator could reduce the time spent reviewing images by a minimum of 70%.

Human Systems Center Products and Progress.

DTIC Science & Technology

1993-10-01

and (CASHE:PVS). CASHE:PVS version 1.0 is a CD-ROM- As a precursor to developing collaborative based hypermedia- ergonomic information base design...computer-generated image to determine if the Crew System Ergonomics Information Analysis activity is physically possible. Expert system Center known as...and facility issues relative Federal Drug Administration, and Centers for to dentistry . The scope includes technical Disease Control to establish

Sweet-spot training for early esophageal cancer detection

NASA Astrophysics Data System (ADS)

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

2016-03-01

Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled physicians to visually inspect the intestinal tissue for early signs of malignant lesions. Besides this, recent studies show the feasibility of supportive image analysis for endoscopists, but the analysis problem is typically approached as a segmentation task where binary ground truth is employed. In this study, we show that the detection of early cancerous tissue in the gastrointestinal tract cannot be approached as a binary segmentation problem and it is crucial and clinically relevant to involve multiple experts for annotating early lesions. By employing the so-called sweet spot for training purposes as a metric, a much better detection performance can be achieved. Furthermore, a multi-expert-based ground truth, i.e. a golden standard, enables an improved validation of the resulting delineations. For this purpose, besides the sweet spot we also propose another novel metric, the Jaccard Golden Standard (JIGS) that can handle multiple ground-truth annotations. Our experiments involving these new metrics and based on the golden standard show that the performance of a detection algorithm of early neoplastic lesions in Barrett's esophagus can be increased significantly, demonstrating a 10 percent point increase in the resulting F1 detection score.

Image-based teleconsultation using smartphones or tablets: qualitative assessment of medical experts.

PubMed

Boissin, Constance; Blom, Lisa; Wallis, Lee; Laflamme, Lucie

2017-02-01

Mobile health has promising potential in improving healthcare delivery by facilitating access to expert advice. Enabling experts to review images on their smartphone or tablet may save valuable time. This study aims at assessing whether images viewed by medical specialists on handheld devices such as smartphones and tablets are perceived to be of comparable quality as when viewed on a computer screen. This was a prospective study comparing the perceived quality of 18 images on three different display devices (smartphone, tablet and computer) by 27 participants (4 burn surgeons and 23 emergency medicine specialists). The images, presented in random order, covered clinical (dermatological conditions, burns, ECGs and X-rays) and non-clinical subjects and their perceived quality was assessed using a 7-point Likert scale. Differences in devices' quality ratings were analysed using linear regression models for clustered data adjusting for image type and participants' characteristics (age, gender and medical specialty). Overall, the images were rated good or very good in most instances and more so for the smartphone (83.1%, mean score 5.7) and tablet (78.2%, mean 5.5) than for a standard computer (70.6%, mean 5.2). Both handheld devices had significantly higher ratings than the computer screen, even after controlling for image type and participants' characteristics. Nearly all experts expressed that they would be comfortable using smartphones (n=25) or tablets (n=26) for image-based teleconsultation. This study suggests that handheld devices could be a substitute for computer screens for teleconsultation by physicians working in emergency settings. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

ACR Appropriateness Criteria® Suspected Liver Metastases.

PubMed

Kaur, Harmeet; Hindman, Nicole M; Al-Refaie, Waddah B; Arif-Tiwari, Hina; Cash, Brooks D; Chernyak, Victoria; Farrell, James; Grajo, Joseph R; Horowitz, Jeanne M; McNamara, Michelle M; Noto, Richard B; Qayyum, Aliya; Lalani, Tasneem; Kamel, Ihab R

2017-05-01

Liver metastases are the most common malignant liver tumors. The accurate and early detection and characterization of liver lesions is the key to successful treatment strategies. Increasingly, surgical resection in combination with chemotherapy is effective in significantly improving survival if all metastases are successfully resected. MRI and multiphase CT are the primary imaging modalities in the assessment of liver metastasis, with the relative preference toward multiphase CT or MRI depending upon the clinical setting (ie, surveillance or presurgical planning). The optimization of imaging parameters is a vital factor in the success of either modality. PET/CT, intraoperative ultrasound are used to supplement CT and MRI. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer-reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Texture analysis of high-resolution FLAIR images for TLE

NASA Astrophysics Data System (ADS)

Jafari-Khouzani, Kourosh; Soltanian-Zadeh, Hamid; Elisevich, Kost

2005-04-01

This paper presents a study of the texture information of high-resolution FLAIR images of the brain with the aim of determining the abnormality and consequently the candidacy of the hippocampus for temporal lobe epilepsy (TLE) surgery. Intensity and volume features of the hippocampus from FLAIR images of the brain have been previously shown to be useful in detecting the abnormal hippocampus in TLE. However, the small size of the hippocampus may limit the texture information. High-resolution FLAIR images show more details of the abnormal intensity variations of the hippocampi and therefore are more suitable for texture analysis. We study and compare the low and high-resolution FLAIR images of six epileptic patients. The hippocampi are segmented manually by an expert from T1-weighted MR images. Then the segmented regions are mapped on the corresponding FLAIR images for texture analysis. The 2-D wavelet transforms of the hippocampi are employed for feature extraction. We compare the ability of the texture features from regular and high-resolution FLAIR images to distinguish normal and abnormal hippocampi. Intracranial EEG results as well as surgery outcome are used as gold standard. The results show that the intensity variations of the hippocampus are related to the abnormalities in the TLE.

TU-A-17A-02: In Memoriam of Ben Galkin: Virtual Tools for Validation of X-Ray Breast Imaging Systems

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

Myers, K; Bakic, P; Abbey, C

2014-06-15

This symposium will explore simulation methods for the preclinical evaluation of novel 3D and 4D x-ray breast imaging systems – the subject of AAPM taskgroup TG234. Given the complex design of modern imaging systems, simulations offer significant advantages over long and costly clinical studies in terms of reproducibility, reduced radiation exposures, a known reference standard, and the capability for studying patient and disease subpopulations through appropriate choice of simulation parameters. Our focus will be on testing the realism of software anthropomorphic phantoms and virtual clinical trials tools developed for the optimization and validation of breast imaging systems. The symposium willmore » review the stateof- the-science, as well as the advantages and limitations of various approaches to testing realism of phantoms and simulated breast images. Approaches based upon the visual assessment of synthetic breast images by expert observers will be contrasted with approaches based upon comparing statistical properties between synthetic and clinical images. The role of observer models in the assessment of realism will be considered. Finally, an industry perspective will be presented, summarizing the role and importance of virtual tools and simulation methods in product development. The challenges and conditions that must be satisfied in order for computational modeling and simulation to play a significantly increased role in the design and evaluation of novel breast imaging systems will be addressed. Learning Objectives: Review the state-of-the science in testing realism of software anthropomorphic phantoms and virtual clinical trials tools; Compare approaches based upon the visual assessment by expert observers vs. the analysis of statistical properties of synthetic images; Discuss the role of observer models in the assessment of realism; Summarize the industry perspective to virtual methods for breast imaging.« less

A generalized approach for producing, quantifying, and validating citizen science data from wildlife images

PubMed Central

Kosmala, Margaret; Lintott, Chris; Packer, Craig

2016-01-01

Abstract Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife. PMID:27111678

On the reproducibility of expert-operated and robotic ultrasound acquisitions.

PubMed

Kojcev, Risto; Khakzar, Ashkan; Fuerst, Bernhard; Zettinig, Oliver; Fahkry, Carole; DeJong, Robert; Richmon, Jeremy; Taylor, Russell; Sinibaldi, Edoardo; Navab, Nassir

2017-06-01

We present the evaluation of the reproducibility of measurements performed using robotic ultrasound imaging in comparison with expert-operated sonography. Robotic imaging for interventional procedures may be a valuable contribution, but requires reproducibility for its acceptance in clinical routine. We study this by comparing repeated measurements based on robotic and expert-operated ultrasound imaging. Robotic ultrasound acquisition is performed in three steps under user guidance: First, the patient is observed using a 3D camera on the robot end effector, and the user selects the region of interest. This allows for automatic planning of the robot trajectory. Next, the robot executes a sweeping motion following the planned trajectory, during which the ultrasound images and tracking data are recorded. As the robot is compliant, deviations from the path are possible, for instance due to patient motion. Finally, the ultrasound slices are compounded to create a volume. Repeated acquisitions can be performed automatically by comparing the previous and current patient surface. After repeated image acquisitions, the measurements based on acquisitions performed by the robotic system and expert are compared. Within our case series, the expert measured the anterior-posterior, longitudinal, transversal lengths of both of the left and right thyroid lobes on each of the 4 healthy volunteers 3 times, providing 72 measurements. Subsequently, the same procedure was performed using the robotic system resulting in a cumulative total of 144 clinically relevant measurements. Our results clearly indicated that robotic ultrasound enables more repeatable measurements. A robotic ultrasound platform leads to more reproducible data, which is of crucial importance for planning and executing interventions.

ACR Appropriateness Criteria® management of locoregionally advanced squamous cell carcinoma of the vulva.

PubMed

Kidd, Elizabeth; Moore, David; Varia, Mahesh A; Gaffney, David K; Elshaikh, Mohamed A; Erickson, Beth; Jhingran, Anuja; Lee, Larissa J; Mayr, Nina A; Puthawala, Ajmel A; Rao, Gautam G; Small, William; Wahl, Andrew O; Wolfson, Aaron H; Yashar, Catheryn M; Yuh, William; Cardenes, Higinia Rosa

2013-08-01

Locoregionally advanced vulvar cancer (LRAVC) is a rare disease that presents many challenging medical decisions. An expert panel was convened to reach consensus on the most appropriate pretreatment assessment and therapeutic interventions in LRAVC patients. The American College of Radiology Appropriateness Criteria are evidenced-based guidelines for specific clinical conditions that are reviewed every 2 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journal and the application of a well-established consensus methodology (modified Delphi) to rate appropriateness of imaging and treatment procedures by the panel. In those instances where evidence is lacking or not definitive, expert opinion may be used to formulate recommendations. Three clinical variants were developed to address common scenarios in the management of LRAVC. Group members reached consensus on the appropriateness of specific evaluation and treatment approaches, with numerical ratings and descriptive commentary. In combining available medical literature and expert opinion, this manuscript may serve as an aid for other practitioners in the appropriate management of patients with LRAVC.

Supervised interpretation of echocardiograms with a psychological model of expert supervision

NASA Astrophysics Data System (ADS)

Revankar, Shriram V.; Sher, David B.; Shalin, Valerie L.; Ramamurthy, Maya

1993-07-01

We have developed a collaborative scheme that facilitates active human supervision of the binary segmentation of an echocardiogram. The scheme complements the reliability of a human expert with the precision of segmentation algorithms. In the developed system, an expert user compares the computer generated segmentation with the original image in a user friendly graphics environment, and interactively indicates the incorrectly classified regions either by pointing or by circling. The precise boundaries of the indicated regions are computed by studying original image properties at that region, and a human visual attention distribution map obtained from the published psychological and psychophysical research. We use the developed system to extract contours of heart chambers from a sequence of two dimensional echocardiograms. We are currently extending this method to incorporate a richer set of inputs from the human supervisor, to facilitate multi-classification of image regions depending on their functionality. We are integrating into our system the knowledge related constraints that cardiologists use, to improve the capabilities of our existing system. This extension involves developing a psychological model of expert reasoning, functional and relational models of typical views in echocardiograms, and corresponding interface modifications to map the suggested actions to image processing algorithms.

Automated image alignment and segmentation to follow progression of geographic atrophy in age-related macular degeneration.

PubMed

Ramsey, David J; Sunness, Janet S; Malviya, Poorva; Applegate, Carol; Hager, Gregory D; Handa, James T

2014-07-01

To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA). The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30° color fundus photographs for 10 patients. The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 ± 5% sensitivity and 98 ± 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 ± 26% and specificity of 98 ± 2%. The segmentation algorithm identified 75 ± 16% of the GA border correctly in fundus autofluorescence images compared with just 42 ± 25% for color fundus photographs. The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.

Large-Scale medical image analytics: Recent methodologies, applications and Future directions.

PubMed

Zhang, Shaoting; Metaxas, Dimitris

2016-10-01

Despite the ever-increasing amount and complexity of annotated medical image data, the development of large-scale medical image analysis algorithms has not kept pace with the need for methods that bridge the semantic gap between images and diagnoses. The goal of this position paper is to discuss and explore innovative and large-scale data science techniques in medical image analytics, which will benefit clinical decision-making and facilitate efficient medical data management. Particularly, we advocate that the scale of image retrieval systems should be significantly increased at which interactive systems can be effective for knowledge discovery in potentially large databases of medical images. For clinical relevance, such systems should return results in real-time, incorporate expert feedback, and be able to cope with the size, quality, and variety of the medical images and their associated metadata for a particular domain. The design, development, and testing of the such framework can significantly impact interactive mining in medical image databases that are growing rapidly in size and complexity and enable novel methods of analysis at much larger scales in an efficient, integrated fashion. Copyright © 2016. Published by Elsevier B.V.

Uncertainty Quantification and Statistical Convergence Guidelines for PIV Data

NASA Astrophysics Data System (ADS)

Stegmeir, Matthew; Kassen, Dan

2016-11-01

As Particle Image Velocimetry has continued to mature, it has developed into a robust and flexible technique for velocimetry used by expert and non-expert users. While historical estimates of PIV accuracy have typically relied heavily on "rules of thumb" and analysis of idealized synthetic images, recently increased emphasis has been placed on better quantifying real-world PIV measurement uncertainty. Multiple techniques have been developed to provide per-vector instantaneous uncertainty estimates for PIV measurements. Often real-world experimental conditions introduce complications in collecting "optimal" data, and the effect of these conditions is important to consider when planning an experimental campaign. The current work utilizes the results of PIV Uncertainty Quantification techniques to develop a framework for PIV users to utilize estimated PIV confidence intervals to compute reliable data convergence criteria for optimal sampling of flow statistics. Results are compared using experimental and synthetic data, and recommended guidelines and procedures leveraging estimated PIV confidence intervals for efficient sampling for converged statistics are provided.

An automatic multi-atlas prostate segmentation in MRI using a multiscale representation and a label fusion strategy

NASA Astrophysics Data System (ADS)

Álvarez, Charlens; Martínez, Fabio; Romero, Eduardo

2015-01-01

The pelvic magnetic Resonance images (MRI) are used in Prostate cancer radiotherapy (RT), a process which is part of the radiation planning. Modern protocols require a manual delineation, a tedious and variable activity that may take about 20 minutes per patient, even for trained experts. That considerable time is an important work ow burden in most radiological services. Automatic or semi-automatic methods might improve the efficiency by decreasing the measure times while conserving the required accuracy. This work presents a fully automatic atlas- based segmentation strategy that selects the more similar templates for a new MRI using a robust multi-scale SURF analysis. Then a new segmentation is achieved by a linear combination of the selected templates, which are previously non-rigidly registered towards the new image. The proposed method shows reliable segmentations, obtaining an average DICE Coefficient of 79%, when comparing with the expert manual segmentation, under a leave-one-out scheme with the training database.

How do scientists respond to anomalies? Different strategies used in basic and applied science.

PubMed

Trickett, Susan Bell; Trafton, J Gregory; Schunn, Christian D

2009-10-01

We conducted two in vivo studies to explore how scientists respond to anomalies. Based on prior research, we identify three candidate strategies: mental simulation, mental manipulation of an image, and comparison between images. In Study 1, we compared experts in basic and applied domains (physics and meteorology). We found that the basic scientists used mental simulation to resolve an anomaly, whereas applied science practitioners mentally manipulated the image. In Study 2, we compared novice and expert meteorologists. We found that unlike experts, novices used comparison to address anomalies. We discuss the nature of expertise in the two kinds of science, the relationship between the type of science and the task performed, and the relationship of the strategies investigated to scientific creativity. Copyright © 2009 Cognitive Science Society, Inc.

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

Jurrus, Elizabeth R.; Hodas, Nathan O.; Baker, Nathan A.

Forensic analysis of nanoparticles is often conducted through the collection and identifi- cation of electron microscopy images to determine the origin of suspected nuclear material. Each image is carefully studied by experts for classification of materials based on texture, shape, and size. Manually inspecting large image datasets takes enormous amounts of time. However, automatic classification of large image datasets is a challenging problem due to the complexity involved in choosing image features, the lack of training data available for effective machine learning methods, and the availability of user interfaces to parse through images. Therefore, a significant need exists for automatedmore » and semi-automated methods to help analysts perform accurate image classification in large image datasets. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web based canvas framework. Images are partitioned using small sets of example images, chosen by users, and presented in an optimal layout based on features derived from convolutional neural networks.« less

Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening.

PubMed

Chen, C; Li, H; Zhou, X; Wong, S T C

2008-05-01

Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.

Expert systems in civil engineering

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

Kostem, C.N.; Maher, M.L.

1986-01-01

This book presents the papers given at a symposium on expert systems in civil engineering. Topics considered at the symposium included problem solving using expert system techniques, construction schedule analysis, decision making and risk analysis, seismic risk analysis systems, an expert system for inactive hazardous waste site characterization, an expert system for site selection, knowledge engineering, and knowledge-based expert systems in seismic analysis.

What Do Geoscience Experts and Novices Look At and What Do They See When Viewing and Interpreting Data Visualizations?

NASA Astrophysics Data System (ADS)

Kastens, K. A.; Shipley, T. F.; Boone, A.

2012-12-01

When geoscience experts look at data visualizations, they can "see" structures, and processes and traces of Earth history. When students look at those same visualizations, they may see only blotches of color, dots or squiggles. What are those experts doing, and how can students learn to do the same? We report on a study in which experts (>10 years of geoscience research experience) and novices (undergrad psychology students) examine shaded-relief/color-coded images of topography/bathymetry, while answering questions aloud and being eye-tracked. Images were a global map, two high-res images of continental terrain and two of oceanic terrain, with hi-res localities chosen to display distinctive traces of important earth processes. The differences in what they look at as recorded by eye-tracking are relatively subtle. On the global image, novices tend to focus on continents, whereas experts distribute their attention more evenly across continents and oceans. Experts universally access the available scale information (distance scale, lat/long axes), whereas most students do not. Novices do attend substantially and spontaneously to the salient geomorphological features in the high-res images: seamounts, mid-ocean ridge/transform intersection, erosional river channels, and compressional ridges and valley system. The more marked differences come in what respondents see, as captured in video recordings of their words and gestures in response to experimenter's questions. When their attention is directed to a small and distinctive part of a high-res image and they are asked to "….describe what you see…", experts typically produce richly detailed descriptions that may include the regional depth/altitude, local relief, shape and spatial distribution of major features, symmetry or lack thereof, cross-cutting relationships, presence of lineations and their orientations, and similar geomorphological details. Following or interwoven with these rich descriptions, some experts also offer interpretations of causal Earth processes. We identified four types of novice answers: (a) "flat" answers, in which the student describes the patches of color on the screen with no mention of shape or relief; (b) "thing" answers, in which the student mentions an inappropriate object, such as "the Great Wall of China," (c) geomorphology answers, in which the student talks about depth/altitude, relief, or shapes of landforms, and (d) process answers, in which student talks about earth processes, such as earthquakes, erosion, or plate tectonics. Novice "geomorphology" (c) answers resemble expert responses, but lack the rich descriptive detail. The "process" (d) category includes many interpretations that lack any grounding in the evidentiary base available in the viewed data. These findings suggest that instruction around earth data should include an emphasis on thoroughly and accurately describing the features that are present in the data--a skill that our experts display and our novices mostly lack. It is unclear, though, how best to sequence the teaching of descriptive and interpretive skills, since the experts' attention to empirical features in the data is steered by their knowledge of which features have causal significance.

Highly dynamic biological seabed alterations revealed by side scan sonar tracking of Lanice conchilega beds offshore the island of Sylt (German Bight)

NASA Astrophysics Data System (ADS)

Heinrich, C.; Feldens, P.; Schwarzer, K.

2017-06-01

Hydroacoustic surveys are common tools for habitat investigation and monitoring that aid in the realisation of the aims of the EU Marine Directives. However, the creation of habitat maps is difficult, especially when benthic organisms densely populate the seafloor. This study assesses the sensitivity of entropy and homogeneity image texture parameters derived from backscatter strength data to benthic habitats dominated by the tubeworm Lanice conchilega. Side scan sonar backscatter surveys were carried out in 2010 and 2011 in the German Bight (southern North Sea) at two sites approx. 20 km offshore of the island of Sylt. Abiotic and biotic seabed facies, such as sorted bedforms, areas of fine to medium sand and L. conchilega beds with different tube densities, were identified and characterised based on manual expert analysis and image texture analysis. Ground truthing was performed by grab sampling and underwater video observations. Compared to the manual expert analysis, the k- means classification of image textures proves to be a semi-automated method to investigate small-scale differences in a biologically altered seabed from backscatter data. The texture parameters entropy and homogeneity appear linearly interrelated with tube density, the former positively and the latter negatively. Reinvestigation of one site after 1 year showed an extensive change in the distribution of the L. conchilega-altered seabed. Such marked annual fluctuations in L. conchilega tube cover demonstrate the need for dense time series and high spatial coverage to meaningfully monitor ecological patterns on the seafloor with acoustic backscatter methods in the study region and similar settings worldwide, particularly because the sand mason plays a pivotal role in promoting biodiversity. In this context, image texture analysis provides a cost-effective and reproducible method to track biologically altered seabeds from side scan sonar backscatter signatures.

Fast automated analysis of strong gravitational lenses with convolutional neural networks

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

Hezaveh, Yashar D.; Levasseur, Laurence Perreault; Marshall, Philip J.

Quantifying image distortions caused by strong gravitational lensing—the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures—and estimating the corresponding matter distribution of these structures (the ‘gravitational lens’) has primarily been performed using maximum likelihood modelling of observations. Our procedure is typically time- and resource-consuming, requiring sophisticated lensing codes, several data preparation steps, and finding the maximum likelihood model parameters in a computationally expensive process with downhill optimizers. Accurate analysis of a single gravitational lens can take up to a few weeks and requires expert knowledge of the physicalmore » processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. We report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light can be made fast and automated using independent component analysis of multi-filter imaging data. Our networks can recover the parameters of the ‘singular isothermal ellipsoid’ density profile, which is commonly used to model strong lensing systems, with an accuracy comparable to the uncertainties of sophisticated models but about ten million times faster: 100 systems in approximately one second on a single graphics processing unit. These networks can provide a way for non-experts to obtain estimates of lensing parameters for large samples of data.« less

Fast automated analysis of strong gravitational lenses with convolutional neural networks

DOE PAGES

Hezaveh, Yashar D.; Levasseur, Laurence Perreault; Marshall, Philip J.

2017-08-30

Quantifying image distortions caused by strong gravitational lensing—the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures—and estimating the corresponding matter distribution of these structures (the ‘gravitational lens’) has primarily been performed using maximum likelihood modelling of observations. Our procedure is typically time- and resource-consuming, requiring sophisticated lensing codes, several data preparation steps, and finding the maximum likelihood model parameters in a computationally expensive process with downhill optimizers. Accurate analysis of a single gravitational lens can take up to a few weeks and requires expert knowledge of the physicalmore » processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. We report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light can be made fast and automated using independent component analysis of multi-filter imaging data. Our networks can recover the parameters of the ‘singular isothermal ellipsoid’ density profile, which is commonly used to model strong lensing systems, with an accuracy comparable to the uncertainties of sophisticated models but about ten million times faster: 100 systems in approximately one second on a single graphics processing unit. These networks can provide a way for non-experts to obtain estimates of lensing parameters for large samples of data.« less

Fast automated analysis of strong gravitational lenses with convolutional neural networks

NASA Astrophysics Data System (ADS)

Hezaveh, Yashar D.; Levasseur, Laurence Perreault; Marshall, Philip J.

2017-08-01

Quantifying image distortions caused by strong gravitational lensing—the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures—and estimating the corresponding matter distribution of these structures (the ‘gravitational lens’) has primarily been performed using maximum likelihood modelling of observations. This procedure is typically time- and resource-consuming, requiring sophisticated lensing codes, several data preparation steps, and finding the maximum likelihood model parameters in a computationally expensive process with downhill optimizers. Accurate analysis of a single gravitational lens can take up to a few weeks and requires expert knowledge of the physical processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. Here we report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light can be made fast and automated using independent component analysis of multi-filter imaging data. Our networks can recover the parameters of the ‘singular isothermal ellipsoid’ density profile, which is commonly used to model strong lensing systems, with an accuracy comparable to the uncertainties of sophisticated models but about ten million times faster: 100 systems in approximately one second on a single graphics processing unit. These networks can provide a way for non-experts to obtain estimates of lensing parameters for large samples of data.

A Neural Network Based Workstation for Automated Cell Proliferation Analysis

DTIC Science & Technology

2001-10-25

work was supported by the Programa de Apoyo a Proyectos de Desarrollo e Investigacíon en Informática REDII 2000. We thank Blanca Itzel Taboada for...Meléndez1, G. Corkidi.2 1Centro de Instrumentos, UNAM. P.O. Box 70-186, México 04510, D.F. 2Instituto de Biotecnología, UNAM. P.O. Box 510-3, 62250...proliferation analysis, of cytological microscope images. The software of the system assists the expert biotechnologist during cell proliferation and

Digital immunohistochemistry wizard: image analysis-assisted stereology tool to produce reference data set for calibration and quality control.

PubMed

Plancoulaine, Benoît; Laurinaviciene, Aida; Meskauskas, Raimundas; Baltrusaityte, Indra; Besusparis, Justinas; Herlin, Paulette; Laurinavicius, Arvydas

2014-01-01

Digital image analysis (DIA) enables better reproducibility of immunohistochemistry (IHC) studies. Nevertheless, accuracy of the DIA methods needs to be ensured, demanding production of reference data sets. We have reported on methodology to calibrate DIA for Ki67 IHC in breast cancer tissue based on reference data obtained by stereology grid count. To produce the reference data more efficiently, we propose digital IHC wizard generating initial cell marks to be verified by experts. Digital images of proliferation marker Ki67 IHC from 158 patients (one tissue microarray spot per patient) with an invasive ductal carcinoma of the breast were used. Manual data (mD) were obtained by marking Ki67-positive and negative tumour cells, using a stereological method for 2D object enumeration. DIA was used as an initial step in stereology grid count to generate the digital data (dD) marks by Aperio Genie and Nuclear algorithms. The dD were collected into XML files from the DIA markup images and overlaid on the original spots along with the stereology grid. The expert correction of the dD marks resulted in corrected data (cD). The percentages of Ki67 positive tumour cells per spot in the mD, dD, and cD sets were compared by single linear regression analysis. Efficiency of cD production was estimated based on manual editing effort. The percentage of Ki67-positive tumor cells was in very good agreement in the mD, dD, and cD sets: regression of cD from dD (R2=0.92) reflects the impact of the expert editing the dD as well as accuracy of the DIA used; regression of the cD from the mD (R2=0.94) represents the consistency of the DIA-assisted ground truth (cD) with the manual procedure. Nevertheless, the accuracy of detection of individual tumour cells was much lower: in average, 18 and 219 marks per spot were edited due to the Genie and Nuclear algorithm errors, respectively. The DIA-assisted cD production in our experiment saved approximately 2/3 of manual marking. Digital IHC wizard enabled DIA-assisted stereology to produce reference data in a consistent and efficient way. It can provide quality control measure for appraising accuracy of the DIA steps.

Digital immunohistochemistry wizard: image analysis-assisted stereology tool to produce reference data set for calibration and quality control

PubMed Central

2014-01-01

Background Digital image analysis (DIA) enables better reproducibility of immunohistochemistry (IHC) studies. Nevertheless, accuracy of the DIA methods needs to be ensured, demanding production of reference data sets. We have reported on methodology to calibrate DIA for Ki67 IHC in breast cancer tissue based on reference data obtained by stereology grid count. To produce the reference data more efficiently, we propose digital IHC wizard generating initial cell marks to be verified by experts. Methods Digital images of proliferation marker Ki67 IHC from 158 patients (one tissue microarray spot per patient) with an invasive ductal carcinoma of the breast were used. Manual data (mD) were obtained by marking Ki67-positive and negative tumour cells, using a stereological method for 2D object enumeration. DIA was used as an initial step in stereology grid count to generate the digital data (dD) marks by Aperio Genie and Nuclear algorithms. The dD were collected into XML files from the DIA markup images and overlaid on the original spots along with the stereology grid. The expert correction of the dD marks resulted in corrected data (cD). The percentages of Ki67 positive tumour cells per spot in the mD, dD, and cD sets were compared by single linear regression analysis. Efficiency of cD production was estimated based on manual editing effort. Results The percentage of Ki67-positive tumor cells was in very good agreement in the mD, dD, and cD sets: regression of cD from dD (R2=0.92) reflects the impact of the expert editing the dD as well as accuracy of the DIA used; regression of the cD from the mD (R2=0.94) represents the consistency of the DIA-assisted ground truth (cD) with the manual procedure. Nevertheless, the accuracy of detection of individual tumour cells was much lower: in average, 18 and 219 marks per spot were edited due to the Genie and Nuclear algorithm errors, respectively. The DIA-assisted cD production in our experiment saved approximately 2/3 of manual marking. Conclusions Digital IHC wizard enabled DIA-assisted stereology to produce reference data in a consistent and efficient way. It can provide quality control measure for appraising accuracy of the DIA steps. PMID:25565221

Facilitated Diagnosis of Pneumothoraces in Newborn Mice Using X-ray Dark-Field Radiography.

PubMed

Hellbach, Katharina; Yaroshenko, Andre; Willer, Konstantin; Pritzke, Tina; Baumann, Alena; Hesse, Nina; Auweter, Sigrid; Reiser, Maximilian F; Eickelberg, Oliver; Pfeiffer, Franz; Hilgendorff, Anne; Meinel, Felix G

2016-10-01

The aim of this study was to evaluate the diagnostic value of x-ray dark-field imaging in projection radiography-based depiction of pneumothoraces in the neonatal murine lung, a potentially life-threatening medical condition that requires a timely and correct diagnosis. By the use of a unique preclinical model, 7-day-old C57Bl/6N mice received mechanical ventilation for 2 or 8 hours with oxygen-rich gas (FIO2 = 0.4; n = 24). Unventilated mice either spontaneously breathed oxygen-rich gas (FIO2 = 0.4) for 2 or 8 hours or room air (n = 22). At the end of the experiment, lungs were inflated with a standardized volume of air after a lethal dose of pentobarbital was administered to the pups. All lungs were imaged with a prototype grating-based small-animal scanner to acquire x-ray transmission and dark-field radiographs. Image contrast between the air-filled pleural space and lung tissue was quantified for both transmission and dark-field radiograms. After the independent expert's assessment, 2 blinded readers evaluated all dark-field and transmission images for the presence or absence of pneumothoraces. Contrast ratios, diagnostic accuracy, as well as reader's confidence and interreader agreement were recorded for both imaging modalities. Evaluation of both x-ray transmission and dark-field radiographs by independent experts revealed the development of a total of 10 pneumothoraces in 8 mice. Here, the contrast ratio between the air-filled pleural space of the pneumothoraces and the lung tissue was significantly higher in the dark field (8.4 ± 3.5) when compared with the transmission images (5.1 ± 2.8; P < 0.05). Accordingly, the readers' diagnostic confidence for the diagnosis of pneumothoraces was significantly higher for dark-field compared with transmission images (P = 0.001). Interreader agreement improved from moderate for the analysis of transmission images alone (κ = 0.41) to very good when analyzing dark-field images alone (κ = 0.90) or in combination with transmission images (κ = 0.88). Diagnostic accuracy significantly improved for the analysis of dark-field images alone (P = 0.04) or in combination with transmission images (P = 0.02), compared with the analysis of transmission radiographs only. The significant improvement in contrast ratios between lung parenchyma and free air in the dark-field images allows the facilitated detection of pneumothoraces in the newborn mouse. These preclinical experiments indicate the potential of the technique for future clinical applications.

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

PubMed Central

Janowczyk, Andrew; Madabhushi, Anant

2016-01-01

Background: Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific “handcrafted” features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a) selecting appropriate magnification, (b) managing errors in annotations in the training (or learning) dataset, and (c) identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i) DL experts with minimal digital histology experience, and (ii) DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial. Aims: This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches. Results: Specifically, in this tutorial on DL for DP image analysis, we show how an open source framework (Caffe), with a singular network architecture, can be used to address: (a) nuclei segmentation (F-score of 0.83 across 12,000 nuclei), (b) epithelium segmentation (F-score of 0.84 across 1735 regions), (c) tubule segmentation (F-score of 0.83 from 795 tubules), (d) lymphocyte detection (F-score of 0.90 across 3064 lymphocytes), (e) mitosis detection (F-score of 0.53 across 550 mitotic events), (f) invasive ductal carcinoma detection (F-score of 0.7648 on 50 k testing patches), and (g) lymphoma classification (classification accuracy of 0.97 across 374 images). Conclusion: This paper represents the largest comprehensive study of DL approaches in DP to date, with over 1200 DP images used during evaluation. The supplemental online material that accompanies this paper consists of step-by-step instructions for the usage of the supplied source code, trained models, and input data. PMID:27563488

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.

PubMed

Janowczyk, Andrew; Madabhushi, Anant

2016-01-01

Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific "handcrafted" features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a) selecting appropriate magnification, (b) managing errors in annotations in the training (or learning) dataset, and (c) identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i) DL experts with minimal digital histology experience, and (ii) DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial. This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches. Specifically, in this tutorial on DL for DP image analysis, we show how an open source framework (Caffe), with a singular network architecture, can be used to address: (a) nuclei segmentation (F-score of 0.83 across 12,000 nuclei), (b) epithelium segmentation (F-score of 0.84 across 1735 regions), (c) tubule segmentation (F-score of 0.83 from 795 tubules), (d) lymphocyte detection (F-score of 0.90 across 3064 lymphocytes), (e) mitosis detection (F-score of 0.53 across 550 mitotic events), (f) invasive ductal carcinoma detection (F-score of 0.7648 on 50 k testing patches), and (g) lymphoma classification (classification accuracy of 0.97 across 374 images). This paper represents the largest comprehensive study of DL approaches in DP to date, with over 1200 DP images used during evaluation. The supplemental online material that accompanies this paper consists of step-by-step instructions for the usage of the supplied source code, trained models, and input data.

Deep learning based tissue analysis predicts outcome in colorectal cancer.

PubMed

Bychkov, Dmitrii; Linder, Nina; Turkki, Riku; Nordling, Stig; Kovanen, Panu E; Verrill, Clare; Walliander, Margarita; Lundin, Mikael; Haglund, Caj; Lundin, Johan

2018-02-21

Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.

What good is SWIR? Passive day comparison of VIS, NIR, and SWIR

NASA Astrophysics Data System (ADS)

Driggers, Ronald G.; Hodgkin, Van; Vollmerhausen, Richard

2013-06-01

This paper is the first of three papers associated with the military benefits of SWIR imaging. This paper describes the benefits associated with passive daytime operations with comparisons of SWIR, NIR, and VIS bands and sensors. This paper includes quantitative findings from previously published papers, analysis of open source data, summaries of various expert analyses, and calculations of notional system performance. We did not accept anecdotal findings as acceptable benefits. Topics include haze and fog penetration, atmospheric transmission, cloud and smoke penetration, target and background contrasts, spectral discrimination, turbulence degradation, and long range target identification. The second and third papers will address passive night imaging and active night imaging.

[Experts consensus of dental esthetic photography].

PubMed

2017-05-09

Clinical photography in esthetic dentistry is an essential skill in clinical practice. It is widely applied clinically in multiple fields related to esthetic dentistry. Society of Esthetic Dentistry of Chinese Stomatological Association established a consensus for clinical photography and standards for images in esthetic dentistry in order to standardize domestic dental practitioners' procedure, and meet the demands of diagnosis and design in modern esthetic dentistry. It was also developed to facilitate domestic and international academic communication. Sixteen commonly used images in practice, which are of apparent importance in guiding esthetic analysis, design and implementation, are proposed in the standards. This consensus states the clinical significance of these images and the standard protocol of acquiring them.

Discus: investigating subjective judgment of optic disc damage.

PubMed

Denniss, Jonathan; Echendu, Damian; Henson, David B; Artes, Paul H

2011-01-01

To describe a software package (Discus) for investigating clinicians' subjective assessment of optic disc damage [diagnostic accuracy in detecting visual field (VF) damage, decision criteria, and agreement with a panel of experts] and to provide reference data from a group of expert observers. Optic disc images were selected from patients with manifest or suspected glaucoma or ocular hypertension who attended the Manchester Royal Eye Hospital. Eighty images came from eyes without evidence of VF loss in at least four consecutive tests (VF negatives), and 20 images from eyes with repeatable VF loss (VF positives). Software was written to display these images in randomized order, for up to 60 s. Expert observers (n = 12) rated optic disc damage on a 5-point scale (definitely healthy, probably healthy, not sure, probably damaged, and definitely damaged). Optic disc damage as determined by the expert observers predicted VF loss with less than perfect accuracy (mean area under receiver-operating characteristic curve, 0.78; range, 0.72 to 0.85). When the responses were combined across the panel of experts, the area under receiver-operating characteristic curve reached 0.87, corresponding to a sensitivity of ∼60% at 90% specificity. Although the observers' performances were similar, there were large differences between the criteria they adopted (p < 0.001), even though all observers had been given identical instructions. Discus provides a simple and rapid means for assessing important aspects of optic disc interpretation. The data from the panel of expert observers provide a reference against which students, trainees, and clinicians may compare themselves. The program and the analyses described in this article are freely accessible from http://www.discusproject.blogspot.com/.

Leveraging the crowd for annotation of retinal images.

PubMed

Leifman, George; Swedish, Tristan; Roesch, Karin; Raskar, Ramesh

2015-01-01

Medical data presents a number of challenges. It tends to be unstructured, noisy and protected. To train algorithms to understand medical images, doctors can label the condition associated with a particular image, but obtaining enough labels can be difficult. We propose an annotation approach which starts with a small pool of expertly annotated images and uses their expertise to rate the performance of crowd-sourced annotations. In this paper we demonstrate how to apply our approach for annotation of large-scale datasets of retinal images. We introduce a novel data validation procedure which is designed to cope with noisy ground-truth data and with non-consistent input from both experts and crowd-workers.

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

PubMed

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

The use of neural networks and texture analysis for rapid objective selection of regions of interest in cytoskeletal images.

PubMed

Derkacs, Amanda D Felder; Ward, Samuel R; Lieber, Richard L

2012-02-01

Understanding cytoskeletal dynamics in living tissue is prerequisite to understanding mechanisms of injury, mechanotransduction, and mechanical signaling. Real-time visualization is now possible using transfection with plasmids that encode fluorescent cytoskeletal proteins. Using this approach with the muscle-specific intermediate filament protein desmin, we found that a green fluorescent protein-desmin chimeric protein was unevenly distributed throughout the muscle fiber, resulting in some image areas that were saturated as well as others that lacked any signal. Our goal was to analyze the muscle fiber cytoskeletal network quantitatively in an unbiased fashion. To objectively select areas of the muscle fiber that are suitable for analysis, we devised a method that provides objective classification of regions of images of striated cytoskeletal structures into "usable" and "unusable" categories. This method consists of a combination of spatial analysis of the image using Fourier methods along with a boosted neural network that "decides" on the quality of the image based on previous training. We trained the neural network using the expert opinion of three scientists familiar with these types of images. We found that this method was over 300 times faster than manual classification and that it permitted objective and accurate classification of image regions.

Interdisciplinary cognitive task analysis: a strategy to develop a comprehensive endoscopic retrograde cholangiopancreatography protocol for use in fellowship training.

PubMed

Canopy, Erin; Evans, Matt; Boehler, Margaret; Roberts, Nicole; Sanfey, Hilary; Mellinger, John

2015-10-01

Endoscopic retrograde cholangiopancreatography is a challenging procedure performed by surgeons and gastroenterologists. We employed cognitive task analysis to identify steps and decision points for this procedure. Standardized interviews were conducted with expert gastroenterologists (7) and surgeons (4) from 4 institutions. A procedural step and cognitive decision point protocol was created from audio-taped transcriptions and was refined by 5 additional surgeons. Conceptual elements, sequential actions, and decision points were iterated for 5 tasks: patient preparation, duodenal intubation, selective cannulation, imaging interpretation with related therapeutic intervention, and complication management. A total of 180 steps were identified. Gastroenterologists identified 34 steps not identified by surgeons, and surgeons identified 20 steps not identified by gastroenterologists. The findings suggest that for complex procedures performed by diverse practitioners, more experts may help delineate distinctive emphases differentiated by training background and type of practice. Copyright © 2015 Elsevier Inc. All rights reserved.

Evaluation of a new imaging tool for use with major trauma cases in the emergency department.

PubMed

Crönlein, Moritz; Holzapfel, Konstantin; Beirer, Marc; Postl, Lukas; Kanz, Karl-Georg; Pförringer, Dominik; Huber-Wagner, Stefan; Biberthaler, Peter; Kirchhoff, Chlodwig

2016-11-17

The aim of this study was to evaluate potential benefits of a new diagnostic software prototype (Trauma Viewer, TV) automatically reformatting computed tomography (CT) data on diagnostic speed and quality, compared to CT-image data evaluation using a conventional CT console. Multiple trauma CT data sets were analysed by one expert radiology and one expert traumatology fellow independently twice, once using the TV and once using the secondary conventional CT console placed in the CT control room. Actual analysis time and precision of diagnoses assessment were evaluated. The TV and CT-console results were compared respectively, but also a comparison to the initial multiple trauma CT reports assessed by emergency radiology fellows considered as the gold standard was performed. Finally, design and function of the Trauma Viewer were evaluated in a descriptive manner. CT data sets of 30 multiple trauma patients were enrolled. Mean time needed for analysis of one CT dataset was 2.43 min using the CT console and 3.58 min using the TV respectively. Thus, secondary conventional CT console analysis was on average 1.15 min shorter compared to the TV analysis. Both readers missed a total of 11 diagnoses using the secondary conventional CT console compared to 12 missed diagnoses using the TV. However, none of these overlooked diagnoses resulted in an Abbreviated Injury Scale (AIS) > 2 corresponding to life threatening injuries. Even though it took the two expert fellows a little longer to analyse the CT scans on the prototype TV compared to the CT console, which can be explained by the new user interface of the TV, our preliminary results demonstrate that, after further development, the TV might serve as a new diagnostic feature in the trauma room management. Its high potential to improve time and quality of CT-based diagnoses might help in fast decision making regarding treatment of severely injured patients.

Automated analysis of time-lapse fluorescence microscopy images: from live cell images to intracellular foci.

PubMed

Dzyubachyk, Oleh; Essers, Jeroen; van Cappellen, Wiggert A; Baldeyron, Céline; Inagaki, Akiko; Niessen, Wiro J; Meijering, Erik

2010-10-01

Complete, accurate and reproducible analysis of intracellular foci from fluorescence microscopy image sequences of live cells requires full automation of all processing steps involved: cell segmentation and tracking followed by foci segmentation and pattern analysis. Integrated systems for this purpose are lacking. Extending our previous work in cell segmentation and tracking, we developed a new system for performing fully automated analysis of fluorescent foci in single cells. The system was validated by applying it to two common tasks: intracellular foci counting (in DNA damage repair experiments) and cell-phase identification based on foci pattern analysis (in DNA replication experiments). Experimental results show that the system performs comparably to expert human observers. Thus, it may replace tedious manual analyses for the considered tasks, and enables high-content screening. The described system was implemented in MATLAB (The MathWorks, Inc., USA) and compiled to run within the MATLAB environment. The routines together with four sample datasets are available at http://celmia.bigr.nl/. The software is planned for public release, free of charge for non-commercial use, after publication of this article.

Automatic analysis of diabetic peripheral neuropathy using multi-scale quantitative morphology of nerve fibres in corneal confocal microscopy imaging.

PubMed

Dabbah, M A; Graham, J; Petropoulos, I N; Tavakoli, M; Malik, R A

2011-10-01

Diabetic peripheral neuropathy (DPN) is one of the most common long term complications of diabetes. Corneal confocal microscopy (CCM) image analysis is a novel non-invasive technique which quantifies corneal nerve fibre damage and enables diagnosis of DPN. This paper presents an automatic analysis and classification system for detecting nerve fibres in CCM images based on a multi-scale adaptive dual-model detection algorithm. The algorithm exploits the curvilinear structure of the nerve fibres and adapts itself to the local image information. Detected nerve fibres are then quantified and used as feature vectors for classification using random forest (RF) and neural networks (NNT) classifiers. We show, in a comparative study with other well known curvilinear detectors, that the best performance is achieved by the multi-scale dual model in conjunction with the NNT classifier. An evaluation of clinical effectiveness shows that the performance of the automated system matches that of ground-truth defined by expert manual annotation. Copyright © 2011 Elsevier B.V. All rights reserved.

Automated discrimination of lower and higher grade gliomas based on histopathological image analysis.

PubMed

Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, Arvind U K

2015-01-01

Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease. We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates. The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction.

History and structures of telecommunication in pathology, focusing on open access platforms.

PubMed

Kayser, Klaus; Borkenfeld, Stephan; Djenouni, Amina; Kayser, Gian

2011-11-07

Telecommunication has matured to a broadly applied tool in diagnostic pathology. Contemporary with the development of fast electronic communication lines (Integrated digital network services (ISDN), broad band connections, and fibre optics, as well as the digital imaging technology (digital camera), telecommunication in tissue--based diagnosis (telepathology) has matured. Open access (internet) and server--based communication have induced the development of specific medical information platforms, such as iPATH, UICC-TPCC (telepathology consultation centre of the Union International against Cancer), or the Armed Forces Institute of Pathology (AFIP) teleconsultation system. They have been closed, and are subject to be replaced by specific open access forums (Medical Electronic Expert Communication System (MECES) with embedded virtual slide (VS) technology). MECES uses php language, data base driven mySqL architecture, X/L-AMPP infrastructure, and browser friendly W3C conform standards. The server--based medical communication systems (AFIP, iPATH, UICC-TPCC) have been reported to be a useful and easy to handle tool for expert consultation. Correct sampling and evaluation of transmitted still images by experts reported revealed no or only minor differences to the original images and good practice of the involved experts. β tests with the new generation medical expert consultation systems (MECES) revealed superior results in terms of performance, still image viewing, and system handling, especially as this is closely related to the use of so--called social forums (facebook, youtube, etc.). In addition to the acknowledged advantages of the former established systems (assistance of pathologists working in developing countries, diagnosis confirmation, international information exchange, etc.), the new generation offers additional benefits such as acoustic information transfer, assistance in image screening, VS technology, and teaching in diagnostic sampling, judgement, and verification.

Australian sea-floor survey data, with images and expert annotations.

PubMed

Bewley, Michael; Friedman, Ariell; Ferrari, Renata; Hill, Nicole; Hovey, Renae; Barrett, Neville; Marzinelli, Ezequiel M; Pizarro, Oscar; Figueira, Will; Meyer, Lisa; Babcock, Russ; Bellchambers, Lynda; Byrne, Maria; Williams, Stefan B

2015-01-01

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia's Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research.

Australian sea-floor survey data, with images and expert annotations

PubMed Central

Bewley, Michael; Friedman, Ariell; Ferrari, Renata; Hill, Nicole; Hovey, Renae; Barrett, Neville; Pizarro, Oscar; Figueira, Will; Meyer, Lisa; Babcock, Russ; Bellchambers, Lynda; Byrne, Maria; Williams, Stefan B.

2015-01-01

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia's Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research. PMID:26528396

Australian sea-floor survey data, with images and expert annotations

NASA Astrophysics Data System (ADS)

Bewley, Michael; Friedman, Ariell; Ferrari, Renata; Hill, Nicole; Hovey, Renae; Barrett, Neville; Pizarro, Oscar; Figueira, Will; Meyer, Lisa; Babcock, Russ; Bellchambers, Lynda; Byrne, Maria; Williams, Stefan B.

2015-10-01

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia's Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research.

Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System

NASA Technical Reports Server (NTRS)

Penaloza, Mauel A.; Welch, Ronald M.

1996-01-01

Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.

Spatial and spectral analysis of corneal epithelium injury using hyperspectral images

NASA Astrophysics Data System (ADS)

Md Noor, Siti Salwa; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-12-01

Eye assessment is essential in preventing blindness. Currently, the existing methods to assess corneal epithelium injury are complex and require expert knowledge. Hence, we have introduced a non-invasive technique using hyperspectral imaging (HSI) and an image analysis algorithm of corneal epithelium injury. Three groups of images were compared and analyzed, namely healthy eyes, injured eyes, and injured eyes with stain. Dimensionality reduction using principal component analysis (PCA) was applied to reduce massive data and redundancies. The first 10 principal components (PCs) were selected for further processing. The mean vector of 10 PCs with 45 pairs of all combinations was computed and sent to two classifiers. A quadratic Bayes normal classifier (QDC) and a support vector classifier (SVC) were used in this study to discriminate the eleven eyes into three groups. As a result, the combined classifier of QDC and SVC showed optimal performance with 2D PCA features (2DPCA-QDSVC) and was utilized to classify normal and abnormal tissues, using color image segmentation. The result was compared with human segmentation. The outcome showed that the proposed algorithm produced extremely promising results to assist the clinician in quantifying a cornea injury.

High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis

NASA Astrophysics Data System (ADS)

MacFaden, Sean W.; O'Neil-Dunne, Jarlath P. M.; Royar, Anna R.; Lu, Jacqueline W. T.; Rundle, Andrew G.

2012-01-01

Urban tree canopy is widely believed to have myriad environmental, social, and human-health benefits, but a lack of precise canopy estimates has hindered quantification of these benefits in many municipalities. This problem was addressed for New York City using object-based image analysis (OBIA) to develop a comprehensive land-cover map, including tree canopy to the scale of individual trees. Mapping was performed using a rule-based expert system that relied primarily on high-resolution LIDAR, specifically its capacity for evaluating the height and texture of aboveground features. Multispectral imagery was also used, but shadowing and varying temporal conditions limited its utility. Contextual analysis was a key part of classification, distinguishing trees according to their physical and spectral properties as well as their relationships to adjacent, nonvegetated features. The automated product was extensively reviewed and edited via manual interpretation, and overall per-pixel accuracy of the final map was 96%. Although manual editing had only a marginal effect on accuracy despite requiring a majority of project effort, it maximized aesthetic quality and ensured the capture of small, isolated trees. Converting high-resolution LIDAR and imagery into usable information is a nontrivial exercise, requiring significant processing time and labor, but an expert system-based combination of OBIA and manual review was an effective method for fine-scale canopy mapping in a complex urban environment.

Automated processing of webcam images for phenological classification.

PubMed

Bothmann, Ludwig; Menzel, Annette; Menze, Bjoern H; Schunk, Christian; Kauermann, Göran

2017-01-01

Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels' time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the statistical software package R and publicly available in the R package phenofun. Executable example code is provided as supplementary material.

Automated processing of webcam images for phenological classification

PubMed Central

Bothmann, Ludwig; Menzel, Annette; Menze, Bjoern H.; Schunk, Christian; Kauermann, Göran

2017-01-01

Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels’ time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the statistical software package R and publicly available in the R package phenofun. Executable example code is provided as supplementary material. PMID:28235092

Comparing experts and novices in Martian surface feature change detection and identification

NASA Astrophysics Data System (ADS)

Wardlaw, Jessica; Sprinks, James; Houghton, Robert; Muller, Jan-Peter; Sidiropoulos, Panagiotis; Bamford, Steven; Marsh, Stuart

2018-02-01

Change detection in satellite images is a key concern of the Earth Observation field for environmental and climate change monitoring. Satellite images also provide important clues to both the past and present surface conditions of other planets, which cannot be validated on the ground. With the volume of satellite imagery continuing to grow, the inadequacy of computerised solutions to manage and process imagery to the required professional standard is of critical concern. Whilst studies find the crowd sourcing approach suitable for the counting of impact craters in single images, images of higher resolution contain a much wider range of features, and the performance of novices in identifying more complex features and detecting change, remains unknown. This paper presents a first step towards understanding whether novices can identify and annotate changes in different geomorphological features. A website was developed to enable visitors to flick between two images of the same location on Mars taken at different times and classify 1) if a surface feature changed and if so, 2) what feature had changed from a pre-defined list of six. Planetary scientists provided ;expert; data against which classifications made by novices could be compared when the project subsequently went public. Whilst no significant difference was found in images identified with surface changes by expert and novices, results exhibited differences in consensus within and between experts and novices when asked to classify the type of change. Experts demonstrated higher levels of agreement in classification of changes as dust devil tracks, slope streaks and impact craters than other features, whilst the consensus of novices was consistent across feature types; furthermore, the level of consensus amongst regardless of feature type. These trends are secondary to the low levels of consensus found, regardless of feature type or classifier expertise. These findings demand the attention of researchers who want to use crowd-sourcing for similar scientific purposes, particularly for the supervised training of computer algorithms, and inform the scope and design of future projects.

Potential application of machine vision technology to saffron (Crocus sativus L.) quality characterization.

PubMed

Kiani, Sajad; Minaei, Saeid

2016-12-01

Saffron quality characterization is an important issue in the food industry and of interest to the consumers. This paper proposes an expert system based on the application of machine vision technology for characterization of saffron and shows how it can be employed in practical usage. There is a correlation between saffron color and its geographic location of production and some chemical attributes which could be properly used for characterization of saffron quality and freshness. This may be accomplished by employing image processing techniques coupled with multivariate data analysis for quantification of saffron properties. Expert algorithms can be made available for prediction of saffron characteristics such as color as well as for product classification. Copyright © 2016. Published by Elsevier Ltd.

Medical image segmentation based on SLIC superpixels model

NASA Astrophysics Data System (ADS)

Chen, Xiang-ting; Zhang, Fan; Zhang, Ruo-ya

2017-01-01

Medical imaging has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, medical images have many unstable factors such as complex imaging mechanism, the target displacement will cause constructed defect and the partial volume effect will lead to error and equipment wear, which increases the complexity of subsequent image processing greatly. The segmentation algorithm which based on SLIC (Simple Linear Iterative Clustering, SLIC) superpixels is used to eliminate the influence of constructed defect and noise by means of the feature similarity in the preprocessing stage. At the same time, excellent clustering effect can reduce the complexity of the algorithm extremely, which provides an effective basis for the rapid diagnosis of experts.

ACR Appropriateness Criteria® Tinnitus.

PubMed

Kessler, Marcus M; Moussa, Marwan; Bykowski, Julie; Kirsch, Claudia F E; Aulino, Joseph M; Berger, Kevin L; Choudhri, Asim F; Fife, Terry D; Germano, Isabelle M; Kendi, A Tuba; Kim, Jeffrey H; Luttrull, Michael D; Nunez, Diego; Shah, Lubdha M; Sharma, Aseem; Shetty, Vilaas S; Symko, Sophia C; Cornelius, Rebecca S

2017-11-01

Tinnitus is the perception of sound in the absence of an external source. It is a common symptom that can be related to hearing loss and other benign causes. However, tinnitus may be disabling and can be the only symptom in a patient with a central nervous system process disorder. History and physical examination are crucial first steps to determine the need for imaging. CT and MRI are useful in the setting of pulsatile tinnitus to evaluate for an underlying vascular anomaly or abnormality. If there is concomitant asymmetric hearing loss, neurologic deficit, or head trauma, imaging should be guided by those respective ACR Appropriateness Criteria ® documents, rather than the presence of tinnitus. Imaging is not usually appropriate in the evaluation of subjective, nonpulsatile tinnitus that does not localize to one ear. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Expert system-based mineral mapping using AVIRIS

NASA Technical Reports Server (NTRS)

Kruse, Fred A.; Lefkoff, A. B.; Dietz, J. B.

1992-01-01

Integrated analysis of imaging spectrometer data and field spectral measurements were used in conjunction with conventional geologic field mapping to characterize bedrock and surficial geology at the northern end of Death Valley, California and Nevada. A knowledge-based expert system was used to automatically produce image maps from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data showing the principal surface mineralogy. The imaging spectrometer data show the spatial distribution of spectrally distinct minerals occurring both as primary rock-forming minerals and as alteration and weathering products. Field spectral measurements were used to verify the mineral maps and field mapping was used to extend the remote sensing results. Geographically referenced image-maps produced from these data form new base maps from which to develop improved understanding of the processes of deposition and erosion affecting the present land surface. The 'northern Grapevine Mountains' (NGM) study area was reported on in numerous papers. This area is an unnamed northwestward extension of the range. Most of the research here has concentrated on mapping of Jurassic-age plutons and associated hydrothermal alteration, however, the nature and scope of these studies is much broader, pertaining to the geologic history and development of the entire Death Valley region. AVIRIS data for the NGM site were obtained during May 1989. Additional AVIRIS data were acquired during September 1989 as part of the Geologic Remote Sensing Field Experiment (GRSFE). The area covered by these data overlaps slightly with the May 1989 data. Three and one-half AVIRIS scenes total were analyzed.

A semi-automated image analysis procedure for in situ plankton imaging systems.

PubMed

Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M

2015-01-01

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups.

A Semi-Automated Image Analysis Procedure for In Situ Plankton Imaging Systems

PubMed Central

Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C.; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M.

2015-01-01

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups. PMID:26010260

A Neuroimaging Web Services Interface as a Cyber Physical System for Medical Imaging and Data Management in Brain Research: Design Study

PubMed Central

2018-01-01

Background Structural and functional brain images are essential imaging modalities for medical experts to study brain anatomy. These images are typically visually inspected by experts. To analyze images without any bias, they must be first converted to numeric values. Many software packages are available to process the images, but they are complex and difficult to use. The software packages are also hardware intensive. The results obtained after processing vary depending on the native operating system used and its associated software libraries; data processed in one system cannot typically be combined with data on another system. Objective The aim of this study was to fulfill the neuroimaging community’s need for a common platform to store, process, explore, and visualize their neuroimaging data and results using Neuroimaging Web Services Interface: a series of processing pipelines designed as a cyber physical system for neuroimaging and clinical data in brain research. Methods Neuroimaging Web Services Interface accepts magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and functional magnetic resonance imaging. These images are processed using existing and custom software packages. The output is then stored as image files, tabulated files, and MySQL tables. The system, made up of a series of interconnected servers, is password-protected and is securely accessible through a Web interface and allows (1) visualization of results and (2) downloading of tabulated data. Results All results were obtained using our processing servers in order to maintain data validity and consistency. The design is responsive and scalable. The processing pipeline started from a FreeSurfer reconstruction of Structural magnetic resonance imaging images. The FreeSurfer and regional standardized uptake value ratio calculations were validated using Alzheimer’s Disease Neuroimaging Initiative input images, and the results were posted at the Laboratory of Neuro Imaging data archive. Notable leading researchers in the field of Alzheimer’s Disease and epilepsy have used the interface to access and process the data and visualize the results. Tabulated results with unique visualization mechanisms help guide more informed diagnosis and expert rating, providing a truly unique multimodal imaging platform that combines magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and resting state functional magnetic resonance imaging. A quality control component was reinforced through expert visual rating involving at least 2 experts. Conclusions To our knowledge, there is no validated Web-based system offering all the services that Neuroimaging Web Services Interface offers. The intent of Neuroimaging Web Services Interface is to create a tool for clinicians and researchers with keen interest on multimodal neuroimaging. More importantly, Neuroimaging Web Services Interface significantly augments the Alzheimer’s Disease Neuroimaging Initiative data, especially since our data contain a large cohort of Hispanic normal controls and Alzheimer’s Disease patients. The obtained results could be scrutinized visually or through the tabulated forms, informing researchers on subtle changes that characterize the different stages of the disease. PMID:29699962

Brain tumor classification of microscopy images using deep residual learning

NASA Astrophysics Data System (ADS)

Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

2016-12-01

The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

Recent Updates in the Endoscopic Diagnosis of Barrett's Oesophagus.

PubMed

Sharma, Neel; Ho, Khek Yu

2016-10-01

Barrett's oesophagus (BO) is a premalignant condition associated with the development of oesophageal adenocarcinoma (OAC). Despite the low risk of progression per annum, OAC is associated with significant morbidity and mortality, with an estimated 5-year survival of 10%. Furthermore, the incidence of OAC continues to rise globally. Therefore, it is imperative to detect the premalignant phase of BO and follow up such patients accordingly. The mainstay diagnosis of BO is endoscopy and biopsy sampling. However, limitations with white light endoscopy (WLE) and undertaking biopsies have shifted the current focus towards real-time image analysis. Utilization of additional tools such as chromoendoscopy, narrow-band imaging (NBI), confocal laser endomicroscopy (CLE), and optical coherence tomography (OCT) are proving beneficial. Furthermore, it is also becoming more apparent that often these tools are utilized by experts in the field. Therefore, for the non-expert, training in these systems is key. Currently as yet, the methodologies used for training optimization require further inquiry. (1) Real-time imaging can serve to minimize excess biopsies. (2) Tools such as chromoendoscopy, NBI, CLE, and OCT can help to compliment WLE. WLE is associated with limited sensitivity. Biopsy sampling is cost-ineffective and associated with sampling error. Hence, from a practical perspective, endoscopists should aim to utilize additional tools to help in real-time image interpretation and minimize an overreliance on histology.

Recent Updates in the Endoscopic Diagnosis of Barrett's Oesophagus

PubMed Central

Sharma, Neel; Ho, Khek Yu

2016-01-01

Background Barrett's oesophagus (BO) is a premalignant condition associated with the development of oesophageal adenocarcinoma (OAC). Despite the low risk of progression per annum, OAC is associated with significant morbidity and mortality, with an estimated 5-year survival of 10%. Furthermore, the incidence of OAC continues to rise globally. Therefore, it is imperative to detect the premalignant phase of BO and follow up such patients accordingly. Summary The mainstay diagnosis of BO is endoscopy and biopsy sampling. However, limitations with white light endoscopy (WLE) and undertaking biopsies have shifted the current focus towards real-time image analysis. Utilization of additional tools such as chromoendoscopy, narrow-band imaging (NBI), confocal laser endomicroscopy (CLE), and optical coherence tomography (OCT) are proving beneficial. Furthermore, it is also becoming more apparent that often these tools are utilized by experts in the field. Therefore, for the non-expert, training in these systems is key. Currently as yet, the methodologies used for training optimization require further inquiry. Key Message (1) Real-time imaging can serve to minimize excess biopsies. (2) Tools such as chromoendoscopy, NBI, CLE, and OCT can help to compliment WLE. Practical Implications WLE is associated with limited sensitivity. Biopsy sampling is cost-ineffective and associated with sampling error. Hence, from a practical perspective, endoscopists should aim to utilize additional tools to help in real-time image interpretation and minimize an overreliance on histology. PMID:27904863

Assessment of Sentinel Node Biopsies With Full-Field Optical Coherence Tomography.

PubMed

Grieve, Kate; Mouslim, Karima; Assayag, Osnath; Dalimier, Eugénie; Harms, Fabrice; Bruhat, Alexis; Boccara, Claude; Antoine, Martine

2016-04-01

Current techniques for the intraoperative analysis of sentinel lymph nodes during breast cancer surgery present drawbacks such as time and tissue consumption. Full-field optical coherence tomography is a novel noninvasive, high-resolution, fast imaging technique. This study investigated the use of full-field optical coherence tomography as an alternative technique for the intraoperative analysis of sentinel lymph nodes. Seventy-one axillary lymph nodes from 38 patients at Tenon Hospital were imaged minutes after excision with full-field optical coherence tomography in the pathology laboratory, before being handled for histological analysis. A pathologist performed a blind diagnosis (benign/malignant), based on the full-field optical coherence tomography images alone, which resulted in a sensitivity of 92% and a specificity of 83% (n = 65 samples). Regular feedback was given during the blind diagnosis, with thorough analysis of the images, such that features of normal and suspect nodes were identified in the images and compared with histology. A nonmedically trained imaging expert also performed a blind diagnosis aided by the reading criteria defined by the pathologist, which resulted in 85% sensitivity and 90% specificity (n = 71 samples). The number of false positives of the pathologist was reduced by 3 in a second blind reading a few months later. These results indicate that following adequate training, full-field optical coherence tomography can be an effective noninvasive diagnostic tool for extemporaneous sentinel node biopsy qualification. © The Author(s) 2015.

ACR Appropriateness Criteria Crohn Disease.

PubMed

Kim, David H; Carucci, Laura R; Baker, Mark E; Cash, Brooks D; Dillman, Jonathan R; Feig, Barry W; Fowler, Kathryn J; Gage, Kenneth L; Noto, Richard B; Smith, Martin P; Yaghmai, Vahid; Yee, Judy; Lalani, Tasneem

2015-10-01

Crohn disease is a chronic inflammatory disorder involving the gastrointestinal tract, characterized by episodic flares and times of remission. Underlying structural damage occurs progressively, with recurrent bouts of inflammation. The diagnosis and management of this disease process is dependent on several clinical, laboratory, imaging, endoscopic, and histologic factors. In recent years, with the maturation of CT enterography, and MR enterography, imaging has played an increasingly important role in relation to Crohn Disease. In addition to these specialized examination modalities, ultrasound and routine CT have potential uses. Fluoroscopy, radiography, and nuclear medicine may be less beneficial depending on the clinical scenario. The imaging modality best suited to evaluating this disease may change, depending on the target population, severity of presentation, and specific clinical situation. This document presents seven clinical scenarios (variants) in both the adult and pediatric populations and rates the appropriateness of the available imaging options. They are summarized in a consolidated table, and the underlying rationale and supporting literature are presented in the accompanying narrative. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every three years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach.

PubMed

Krishna Kumar, P; Araki, Tadashi; Rajan, Jeny; Saba, Luca; Lavra, Francesco; Ikeda, Nobutaka; Sharma, Aditya M; Shafique, Shoaib; Nicolaides, Andrew; Laird, John R; Gupta, Ajay; Suri, Jasjit S

2017-08-01

Monitoring of cerebrovascular diseases via carotid ultrasound has started to become a routine. The measurement of image-based lumen diameter (LD) or inter-adventitial diameter (IAD) is a promising approach for quantification of the degree of stenosis. The manual measurements of LD/IAD are not reliable, subjective and slow. The curvature associated with the vessels along with non-uniformity in the plaque growth poses further challenges. This study uses a novel and generalized approach for automated LD and IAD measurement based on a combination of spatial transformation and scale-space. In this iterative procedure, the scale-space is first used to get the lumen axis which is then used with spatial image transformation paradigm to get a transformed image. The scale-space is then reapplied to retrieve the lumen region and boundary in the transformed framework. Then, inverse transformation is applied to display the results in original image framework. Two hundred and two patients' left and right common carotid artery (404 carotid images) B-mode ultrasound images were retrospectively analyzed. The validation of our algorithm has done against the two manual expert tracings. The coefficient of correlation between the two manual tracings for LD was 0.98 (p < 0.0001) and 0.99 (p < 0.0001), respectively. The precision of merit between the manual expert tracings and the automated system was 97.7 and 98.7%, respectively. The experimental analysis demonstrated superior performance of the proposed method over conventional approaches. Several statistical tests demonstrated the stability and reliability of the automated system.

The ALICE-HMPID Detector Control System: Its evolution towards an expert and adaptive system

NASA Astrophysics Data System (ADS)

De Cataldo, G.; Franco, A.; Pastore, C.; Sgura, I.; Volpe, G.

2011-05-01

The High Momentum Particle IDentification (HMPID) detector is a proximity focusing Ring Imaging Cherenkov (RICH) for charged hadron identification. The HMPID is based on liquid C 6F 14 as the radiator medium and on a 10 m 2 CsI coated, pad segmented photocathode of MWPCs for UV Cherenkov photon detection. To ensure full remote control, the HMPID is equipped with a detector control system (DCS) responding to industrial standards for robustness and reliability. It has been implemented using PVSS as Slow Control And Data Acquisition (SCADA) environment, Programmable Logic Controller as control devices and Finite State Machines for modular and automatic command execution. In the perspective of reducing human presence at the experiment site, this paper focuses on DCS evolution towards an expert and adaptive control system, providing, respectively, automatic error recovery and stable detector performance. HAL9000, the first prototype of the HMPID expert system, is then presented. Finally an analysis of the possible application of the adaptive features is provided.

Autonomous Diagnostic Imaging Performed by Untrained Operators using Augmented Reality as a Form of "Just-in-Time" Training

NASA Technical Reports Server (NTRS)

Martin, D. S.; Wang, L.; Laurie, S. S.; Lee, S. M. C.; Fleischer, A. C.; Gibson, C. R.; Stenger, M. B.

2017-01-01

We will address the Human Factors and Performance Team, "Risk of performance errors due to training deficiencies" by improving the JIT training materials for ultrasound and OCT imaging by providing advanced guidance in a detailed, timely, and user-friendly manner. Specifically, we will (1) develop an audio-visual tutorial using AR that guides non-experts through an abdominal trauma ultrasound protocol; (2) develop an audio-visual tutorial using AR to guide an untrained operator through the acquisition of OCT images; (3) evaluate the quality of abdominal ultrasound and OCT images acquired by untrained operators using AR guidance compared to images acquired using traditional JIT techniques (laptop-based training conducted before image acquisition); and (4) compare the time required to complete imaging studies using AR tutorials with images acquired using current JIT practices to identify areas for time efficiency improvements. Two groups of subjects will be recruited to participate in this study. Operator-subjects, without previous experience in ultrasound or OCT, will be asked to perform both procedures using either the JIT training with AR technology or the traditional JIT training via laptop. Images acquired by inexperienced operator-subjects will be scored by experts in that imaging modality for diagnostic and research quality; experts will be blinded to the form of JIT used to acquire the images. Operator-subjects also will be asked to submit feedback to improve the training modules used during the scans to improve future training modules. Scanned-subjects will be a small group individuals from whom all images will be acquired.

Autonomous Diagnostic Imaging Performed by Untrained Operator Using Augmented Reality as a Form of "Just-in-Time" Training

NASA Technical Reports Server (NTRS)

Martin, David S.; Wang, Lui; Laurie, Steven S.; Lee, Stuart M. C.; Stenger, Michael B.

2017-01-01

We will address the Human Factors and Performance Team, "Risk of performance errors due to training deficiencies" by improving the JIT training materials for ultrasound and OCT imaging by providing advanced guidance in a detailed, timely, and user-friendly manner. Specifically, we will (1) develop an audio-visual tutorial using AR that guides non-experts through an abdominal trauma ultrasound protocol; (2) develop an audio-visual tutorial using AR to guide an untrained operator through the acquisition of OCT images; (3) evaluate the quality of abdominal ultrasound and OCT images acquired by untrained operators using AR guidance compared to images acquired using traditional JIT techniques (laptop-based training conducted before image acquisition); and (4) compare the time required to complete imaging studies using AR tutorials with images acquired using current JIT practices to identify areas for time efficiency improvements. Two groups of subjects will be recruited to participate in this study. Operator-subjects, without previous experience in ultrasound or OCT, will be asked to perform both procedures using either the JIT training with AR technology or the traditional JIT training via laptop. Images acquired by inexperienced operator-subjects will be scored by experts in that imaging modality for diagnostic and research quality; experts will be blinded to the form of JIT used to acquire the images. Operator-subjects also will be asked to submit feedback to improve the training modules used during the scans to improve future training modules. Scanned-subjects will be a small group individuals from whom all images will be acquired.

An innovative and shared methodology for event reconstruction using images in forensic science.

PubMed

Milliet, Quentin; Jendly, Manon; Delémont, Olivier

2015-09-01

This study presents an innovative methodology for forensic science image analysis for event reconstruction. The methodology is based on experiences from real cases. It provides real added value to technical guidelines such as standard operating procedures (SOPs) and enriches the community of practices at stake in this field. This bottom-up solution outlines the many facets of analysis and the complexity of the decision-making process. Additionally, the methodology provides a backbone for articulating more detailed and technical procedures and SOPs. It emerged from a grounded theory approach; data from individual and collective interviews with eight Swiss and nine European forensic image analysis experts were collected and interpreted in a continuous, circular and reflexive manner. Throughout the process of conducting interviews and panel discussions, similarities and discrepancies were discussed in detail to provide a comprehensive picture of practices and points of view and to ultimately formalise shared know-how. Our contribution sheds light on the complexity of the choices, actions and interactions along the path of data collection and analysis, enhancing both the researchers' and participants' reflexivity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

[Reliability of retinal imaging screening in retinopathy of prematurity].

PubMed

Navarro-Blanco, C; Peralta-Calvo, J; Pastora-Salvador, N; Alvarez-Rementería, L; Chamorro, E; Sánchez-Ramos, C

2014-09-01

The retinopathy of prematurity (ROP) is a potentially avoidable cause of blindness in children. The advances in neonatal care make the survival of extremely premature infants, who show a greater incidence of the disease, possible. The aim of the study is to evaluate the reliability of ROP screening using retinography imaging with the RetCam 3 wide-angle camera and also study the variability of ROP diagnosis depending on the evaluator. The indirect ophthalmoscopy exam was performed by a Pediatric ROP-Expert Ophthalmologist. The same ophthalmologist and a technician specialized in digital image capture took retinal images using the RetCam 3 wide-angle camera. A total of 30 image sets were analyzed by 3 masked groups: group A (8 ophthalmologists), group B (5 experts in vision), and group C (2 ROP-expert ophthalmologists). According to the diagnosis using indirect ophthalmoscopy, the sensitivity (26-93), Kappa (0.24-0.80), and the percent agreement were statistically significant in group C for the diagnosis of ROP Type 1. In the diagnosis of ROP Type 1+Type 2, Kappa (0.17-0.33) and the percent agreement (58-90) were statistically significant, with higher values in group C. The diagnosis, carried out by ROP-expert ophthalmologists, using the wide-angle camera RetCam 3 has proved to be a reliable method. Copyright © 2013 Asociación Española de Pediatría. Published by Elsevier Espana. All rights reserved.

A three-dimensional visualization preoperative treatment planning system for microwave ablation in liver cancer: a simulated experimental study.

PubMed

Liu, Fangyi; Cheng, Zhigang; Han, Zhiyu; Yu, Xiaoling; Yu, Mingan; Liang, Ping

2017-06-01

To evaluate the application value of three-dimensional (3D) visualization preoperative treatment planning system (VPTPS) for microwave ablation (MWA) in liver cancer. The study was a simulated experimental study using the CT imaging data of patients in DICOM format in a model. Three students (who learn to interventional ultrasound for less than 1 year) and three experts (who have more than 5 years of experience in ablation techniques) in MWA performed the preoperative planning for 39 lesions (mean diameter 3.75 ± 1.73 cm) of 32 patients using two-dimensional (2D) image planning method and 3D VPTPS, respectively. The number of planning insertions, planning ablation rate, and damage rate to surrounding structures were compared between2D image planning group and 3D VPTPS group. There were fewer planning insertions, lower ablation rate and higher damage rate to surrounding structures in 2D image planning group than 3D VPTPS group for both students and experts. When using the 2D ultrasound planning method, students could carry out fewer planning insertions and had a lower ablation rate than the experts (p < 0.001). However, there was no significant difference in planning insertions, the ablation rate, and the incidence of damage to the surrounding structures between students and experts using 3D VPTPS. 3DVPTPS enables inexperienced physicians to have similar preoperative planning results to experts, and enhances students' preoperative planning capacity, which may improve the therapeutic efficacy and reduce the complication of MWA.

Automated fluorescent miscroscopic image analysis of PTBP1 expression in glioma

PubMed Central

Becker, Aline; Elder, Brad; Puduvalli, Vinay; Winter, Jessica; Gurcan, Metin

2017-01-01

Multiplexed immunofluorescent testing has not entered into diagnostic neuropathology due to the presence of several technical barriers, amongst which includes autofluorescence. This study presents the implementation of a methodology capable of overcoming the visual challenges of fluorescent microscopy for diagnostic neuropathology by using automated digital image analysis, with long term goal of providing unbiased quantitative analyses of multiplexed biomarkers for solid tissue neuropathology. In this study, we validated PTBP1, a putative biomarker for glioma, and tested the extent to which immunofluorescent microscopy combined with automated and unbiased image analysis would permit the utility of PTBP1 as a biomarker to distinguish diagnostically challenging surgical biopsies. As a paradigm, we utilized second resections from patients diagnosed either with reactive brain changes (pseudoprogression) and recurrent glioblastoma (true progression). Our image analysis workflow was capable of removing background autofluorescence and permitted quantification of DAPI-PTBP1 positive cells. PTBP1-positive nuclei, and the mean intensity value of PTBP1 signal in cells. Traditional pathological interpretation was unable to distinguish between groups due to unacceptably high discordance rates amongst expert neuropathologists. Our data demonstrated that recurrent glioblastoma showed more DAPI-PTBP1 positive cells and a higher mean intensity value of PTBP1 signal compared to resections from second surgeries that showed only reactive gliosis. Our work demonstrates the potential of utilizing automated image analysis to overcome the challenges of implementing fluorescent microscopy in diagnostic neuropathology. PMID:28282372

Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy

NASA Astrophysics Data System (ADS)

Boschetto, Davide; Di Claudio, Gianluca; Mirzaei, Hadis; Leong, Rupert; Grisan, Enrico

2016-03-01

Celiac disease (CD) is an immune-mediated enteropathy triggered by exposure to gluten and similar proteins, affecting genetically susceptible persons, increasing their risk of different complications. Small bowels mucosa damage due to CD involves various degrees of endoscopically relevant lesions, which are not easily recognized: their overall sensitivity and positive predictive values are poor even when zoom-endoscopy is used. Confocal Laser Endomicroscopy (CLE) allows skilled and trained experts to qualitative evaluate mucosa alteration such as a decrease in goblet cells density, presence of villous atrophy or crypt hypertrophy. We present a method for automatically classifying CLE images into three different classes: normal regions, villous atrophy and crypt hypertrophy. This classification is performed after a features selection process, in which four features are extracted from each image, through the application of homomorphic filtering and border identification through Canny and Sobel operators. Three different classifiers have been tested on a dataset of 67 different images labeled by experts in three classes (normal, VA and CH): linear approach, Naive-Bayes quadratic approach and a standard quadratic analysis, all validated with a ten-fold cross validation. Linear classification achieves 82.09% accuracy (class accuracies: 90.32% for normal villi, 82.35% for VA and 68.42% for CH, sensitivity: 0.68, specificity 1.00), Naive Bayes analysis returns 83.58% accuracy (90.32% for normal villi, 70.59% for VA and 84.21% for CH, sensitivity: 0.84 specificity: 0.92), while the quadratic analysis achieves a final accuracy of 94.03% (96.77% accuracy for normal villi, 94.12% for VA and 89.47% for CH, sensitivity: 0.89, specificity: 0.98).

Analysis of a mammography teaching program based on an affordance design model.

PubMed

Luo, Ping; Eikman, Edward A; Kealy, William; Qian, Wei

2006-12-01

The wide use of computer technology in education, particularly in mammogram reading, asks for e-learning evaluation. The existing media comparative studies, learner attitude evaluations, and performance tests are problematic. Based on an affordance design model, this study examined an existing e-learning program on mammogram reading. The selection criteria include content relatedness, representativeness, e-learning orientation, image quality, program completeness, and accessibility. A case study was conducted to examine the affordance features, functions, and presentations of the selected software. Data collection and analysis methods include interviews, protocol-based document analysis, and usability tests and inspection. Also some statistics were calculated. The examination of PBE identified that this educational software designed and programmed some tools. The learner can use these tools in the process of optimizing displays, scanning images, comparing different projections, marking the region of interests, constructing a descriptive report, assessing one's learning outcomes, and comparing one's decisions with the experts' decisions. Further, PBE provides some resources for the learner to construct one's knowledge and skills, including a categorized image library, a term-searching function, and some teaching links. Besides, users found it easy to navigate and carry out tasks. The users also reacted positively toward PBE's navigation system, instructional aids, layout, pace and flow of information, graphics, and other presentation design. The software provides learners with some cognitive tools, supporting their perceptual problem-solving processes and extending their capabilities. Learners can internalize the mental models in mammogram reading through multiple perceptual triangulations, sensitization of related features, semantic description of mammogram findings, and expert-guided semantic report construction. The design of these cognitive tools and the software interface matches the findings and principles in human learning and instructional design. Working with PBE's case-based simulations and categorized gallery, learners can enrich and transfer their experience to their jobs.

6 CFR 37.31 - Source document retention.

Code of Federal Regulations, 2014 CFR

2014-01-01

... keep digital images of source documents must retain the images for a minimum of ten years. (4) States... using digital imaging to retain source documents must store the images as follows: (1) Photo images must be stored in the Joint Photographic Experts Group (JPEG) 2000 standard for image compression, or a...

6 CFR 37.31 - Source document retention.

Code of Federal Regulations, 2012 CFR

2012-01-01

... keep digital images of source documents must retain the images for a minimum of ten years. (4) States... using digital imaging to retain source documents must store the images as follows: (1) Photo images must be stored in the Joint Photographic Experts Group (JPEG) 2000 standard for image compression, or a...

6 CFR 37.31 - Source document retention.

Code of Federal Regulations, 2010 CFR

2010-01-01

... keep digital images of source documents must retain the images for a minimum of ten years. (4) States... using digital imaging to retain source documents must store the images as follows: (1) Photo images must be stored in the Joint Photographic Experts Group (JPEG) 2000 standard for image compression, or a...

6 CFR 37.31 - Source document retention.

Code of Federal Regulations, 2011 CFR

2011-01-01

... keep digital images of source documents must retain the images for a minimum of ten years. (4) States... using digital imaging to retain source documents must store the images as follows: (1) Photo images must be stored in the Joint Photographic Experts Group (JPEG) 2000 standard for image compression, or a...

6 CFR 37.31 - Source document retention.

Code of Federal Regulations, 2013 CFR

2013-01-01

... keep digital images of source documents must retain the images for a minimum of ten years. (4) States... using digital imaging to retain source documents must store the images as follows: (1) Photo images must be stored in the Joint Photographic Experts Group (JPEG) 2000 standard for image compression, or a...

An Active Vision Approach to Understanding and Improving Visual Training in the Geosciences

NASA Astrophysics Data System (ADS)

Voronov, J.; Tarduno, J. A.; Jacobs, R. A.; Pelz, J. B.; Rosen, M. R.

2009-12-01

Experience in the field is a fundamental aspect of geologic training, and its effectiveness is largely unchallenged because of anecdotal evidence of its success among expert geologists. However, there have been only a few quantitative studies based on large data collection efforts to investigate how Earth Scientists learn in the field. In a recent collaboration between Earth scientists, Cognitive scientists and experts in Imaging science at the University of Rochester and Rochester Institute of Technology, we are investigating such a study. Within Cognitive Science, one school of thought, referred to as the Active Vision approach, emphasizes that visual perception is an active process requiring us to move our eyes to acquire new information about our environment. The Active Vision approach indicates the perceptual skills which experts possess and which novices will need to acquire to achieve expert performance. We describe data collection efforts using portable eye-trackers to assess how novice and expert geologists acquire visual knowledge in the field. We also discuss our efforts to collect images for use in a semi-immersive classroom environment, useful for further testing of novices and experts using eye-tracking technologies.

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling.

PubMed

Shahedi, Maysam; Halicek, Martin; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

2018-06-01

Prostate segmentation in computed tomography (CT) images is useful for treatment planning and procedure guidance such as external beam radiotherapy and brachytherapy. However, because of the low, soft tissue contrast of CT images, manual segmentation of the prostate is a time-consuming task with high interobserver variation. In this study, we proposed a semiautomated, three-dimensional (3D) segmentation for prostate CT images using shape and texture analysis and we evaluated the method against manual reference segmentations. The prostate gland usually has a globular shape with a smoothly curved surface, and its shape could be accurately modeled or reconstructed having a limited number of well-distributed surface points. In a training dataset, using the prostate gland centroid point as the origin of a coordination system, we defined an intersubject correspondence between the prostate surface points based on the spherical coordinates. We applied this correspondence to generate a point distribution model for prostate shape using principal component analysis and to study the local texture difference between prostate and nonprostate tissue close to the different prostate surface subregions. We used the learned shape and texture characteristics of the prostate in CT images and then combined them with user inputs to segment a new image. We trained our segmentation algorithm using 23 CT images and tested the algorithm on two sets of 10 nonbrachytherapy and 37 postlow dose rate brachytherapy CT images. We used a set of error metrics to evaluate the segmentation results using two experts' manual reference segmentations. For both nonbrachytherapy and post-brachytherapy image sets, the average measured Dice similarity coefficient (DSC) was 88% and the average mean absolute distance (MAD) was 1.9 mm. The average measured differences between the two experts on both datasets were 92% (DSC) and 1.1 mm (MAD). The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of CT images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation. © 2018 American Association of Physicists in Medicine.

Ultrasound tissue analysis and characterization

NASA Astrophysics Data System (ADS)

Kaufhold, John; Chan, Ray C.; Karl, William C.; Castanon, David A.

1999-07-01

On the battlefield of the future, it may become feasible for medics to perform, via application of new biomedical technologies, more sophisticated diagnoses and surgery than is currently practiced. Emerging biomedical technology may enable the medic to perform laparoscopic surgical procedures to remove, for example, shrapnel from injured soldiers. Battlefield conditions constrain the types of medical image acquisition and interpretation which can be performed. Ultrasound is the only viable biomedical imaging modality appropriate for deployment on the battlefield -- which leads to image interpretation issues because of the poor quality of ultrasound imagery. To help overcome these issues, we develop and implement a method of image enhancement which could aid non-experts in the rapid interpretation and use of ultrasound imagery. We describe an energy minimization approach to finding boundaries in medical images and show how prior information on edge orientation can be incorporated into this framework to detect tissue boundaries oriented at a known angle.

Why the long face? The importance of vertical image structure for biological "barcodes" underlying face recognition.

PubMed

Spence, Morgan L; Storrs, Katherine R; Arnold, Derek H

2014-07-29

Humans are experts at face recognition. The mechanisms underlying this complex capacity are not fully understood. Recently, it has been proposed that face recognition is supported by a coarse-scale analysis of visual information contained in horizontal bands of contrast distributed along the vertical image axis-a biological facial "barcode" (Dakin & Watt, 2009). A critical prediction of the facial barcode hypothesis is that the distribution of image contrast along the vertical axis will be more important for face recognition than image distributions along the horizontal axis. Using a novel paradigm involving dynamic image distortions, a series of experiments are presented examining famous face recognition impairments from selectively disrupting image distributions along the vertical or horizontal image axes. Results show that disrupting the image distribution along the vertical image axis is more disruptive for recognition than matched distortions along the horizontal axis. Consistent with the facial barcode hypothesis, these results suggest that human face recognition relies disproportionately on appropriately scaled distributions of image contrast along the vertical image axis. © 2014 ARVO.

Measuring the apparent diffusion coefficient in primary rectal tumors: is there a benefit in performing histogram analyses?

PubMed

van Heeswijk, Miriam M; Lambregts, Doenja M J; Maas, Monique; Lahaye, Max J; Ayas, Z; Slenter, Jos M G M; Beets, Geerard L; Bakers, Frans C H; Beets-Tan, Regina G H

2017-06-01

The apparent diffusion coefficient (ADC) is a potential prognostic imaging marker in rectal cancer. Typically, mean ADC values are used, derived from precise manual whole-volume tumor delineations by experts. The aim was first to explore whether non-precise circular delineation combined with histogram analysis can be a less cumbersome alternative to acquire similar ADC measurements and second to explore whether histogram analyses provide additional prognostic information. Thirty-seven patients who underwent a primary staging MRI including diffusion-weighted imaging (DWI; b0, 25, 50, 100, 500, 1000; 1.5 T) were included. Volumes-of-interest (VOIs) were drawn on b1000-DWI: (a) precise delineation, manually tracing tumor boundaries (2 expert readers), and (b) non-precise delineation, drawing circular VOIs with a wide margin around the tumor (2 non-experts). Mean ADC and histogram metrics (mean, min, max, median, SD, skewness, kurtosis, 5th-95th percentiles) were derived from the VOIs and delineation time was recorded. Measurements were compared between the two methods and correlated with prognostic outcome parameters. Median delineation time reduced from 47-165 s (precise) to 21-43 s (non-precise). The 45th percentile of the non-precise delineation showed the best correlation with the mean ADC from the precise delineation as the reference standard (ICC 0.71-0.75). None of the mean ADC or histogram parameters showed significant prognostic value; only the total tumor volume (VOI) was significantly larger in patients with positive clinical N stage and mesorectal fascia involvement. When performing non-precise tumor delineation, histogram analysis (in specific 45th ADC percentile) may be used as an alternative to obtain similar ADC values as with precise whole tumor delineation. Histogram analyses are not beneficial to obtain additional prognostic information.

Diagnostic accuracy of semi-automatic quantitative metrics as an alternative to expert reading of CT myocardial perfusion in the CORE320 study.

PubMed

Ostovaneh, Mohammad R; Vavere, Andrea L; Mehra, Vishal C; Kofoed, Klaus F; Matheson, Matthew B; Arbab-Zadeh, Armin; Fujisawa, Yasuko; Schuijf, Joanne D; Rochitte, Carlos E; Scholte, Arthur J; Kitagawa, Kakuya; Dewey, Marc; Cox, Christopher; DiCarli, Marcelo F; George, Richard T; Lima, Joao A C

To determine the diagnostic accuracy of semi-automatic quantitative metrics compared to expert reading for interpretation of computed tomography perfusion (CTP) imaging. The CORE320 multicenter diagnostic accuracy clinical study enrolled patients between 45 and 85 years of age who were clinically referred for invasive coronary angiography (ICA). Computed tomography angiography (CTA), CTP, single photon emission computed tomography (SPECT), and ICA images were interpreted manually in blinded core laboratories by two experienced readers. Additionally, eight quantitative CTP metrics as continuous values were computed semi-automatically from myocardial and blood attenuation and were combined using logistic regression to derive a final quantitative CTP metric score. For the reference standard, hemodynamically significant coronary artery disease (CAD) was defined as a quantitative ICA stenosis of 50% or greater and a corresponding perfusion defect by SPECT. Diagnostic accuracy was determined by area under the receiver operating characteristic curve (AUC). Of the total 377 included patients, 66% were male, median age was 62 (IQR: 56, 68) years, and 27% had prior myocardial infarction. In patient based analysis, the AUC (95% CI) for combined CTA-CTP expert reading and combined CTA-CTP semi-automatic quantitative metrics was 0.87(0.84-0.91) and 0.86 (0.83-0.9), respectively. In vessel based analyses the AUC's were 0.85 (0.82-0.88) and 0.84 (0.81-0.87), respectively. No significant difference in AUC was found between combined CTA-CTP expert reading and CTA-CTP semi-automatic quantitative metrics in patient based or vessel based analyses(p > 0.05 for all). Combined CTA-CTP semi-automatic quantitative metrics is as accurate as CTA-CTP expert reading to detect hemodynamically significant CAD. Copyright © 2018 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Computer assisted diagnosis in renal nuclear medicine: rationale, methodology and interpretative criteria for diuretic renography

PubMed Central

Taylor, Andrew T; Garcia, Ernest V

2014-01-01

The goal of artificial intelligence, expert systems, decision support systems and computer assisted diagnosis (CAD) in imaging is the development and implementation of software to assist in the detection and evaluation of abnormalities, to alert physicians to cognitive biases, to reduce intra and inter-observer variability and to facilitate the interpretation of studies at a faster rate and with a higher level of accuracy. These developments are needed to meet the challenges resulting from a rapid increase in the volume of diagnostic imaging studies coupled with a concurrent increase in the number and complexity of images in each patient data. The convergence of an expanding knowledge base and escalating time constraints increases the likelihood of physician errors. Errors are even more likely when physicians interpret low volume studies such as 99mTc-MAG3 diuretic scans where imagers may have had limited training or experience. Decision support systems include neural networks, case-based reasoning, expert systems and statistical systems. iRENEX (renal expert) is an expert system for diuretic renography that uses a set of rules obtained from human experts to analyze a knowledge base of both clinical parameters and quantitative parameters derived from the renogram. Initial studies have shown that the interpretations provided by iRENEX are comparable to the interpretations of a panel of experts. iRENEX provides immediate patient specific feedback at the time of scan interpretation, can be queried to provide the reasons for its conclusions and can be used as an educational tool to teach trainees to better interpret renal scans. iRENEX also has the capacity to populate a structured reporting module and generate a clear and concise impression based on the elements contained in the report; adherence to the procedural and data entry components of the structured reporting module assures and documents procedural competency. Finally, although the focus is CAD applied to diuretic renography, this review offers a window into the rationale, methodology and broader applications of computer assisted diagnosis in medical imaging. PMID:24484751

Cardiac imaging: working towards fully-automated machine analysis & interpretation

PubMed Central

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-01-01

Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation. PMID:28277804

GESA--a two-dimensional processing system using knowledge base techniques.

PubMed

Rowlands, D G; Flook, A; Payne, P I; van Hoff, A; Niblett, T; McKee, S

1988-12-01

The successful analysis of two-dimensional (2-D) polyacrylamide electrophoresis gels demands considerable experience and understanding of the protein system under investigation as well as knowledge of the separation technique itself. The present work concerns the development of a computer system for analysing 2-D electrophoretic separations which incorporates concepts derived from artificial intelligence research such that non-experts can use the technique as a diagnostic or identification tool. Automatic analysis of 2-D gel separations has proved to be extremely difficult using statistical methods. Non-reproducibility of gel separations is also difficult to overcome using automatic systems. However, the human eye is extremely good at recognising patterns in images, and human intervention in semi-automatic computer systems can reduce the computational complexities of fully automatic systems. Moreover, the expertise and understanding of an "expert" is invaluable in reducing system complexity if it can be encapsulated satisfactorily in an expert system. The combination of user-intervention in the computer system together with the encapsulation of expert knowledge characterises the present system. The domain within which the system has been developed is that of wheat grain storage proteins (gliadins) which exhibit polymorphism to such an extent that cultivars can be uniquely identified by their gliadin patterns. The system can be adapted to other domains where a range of polymorpic protein sub-units exist. In its generalised form, the system can also be used for comparing more complex 2-D gel electrophoretic separations.

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier.

PubMed

Nanthagopal, A Padma; Rajamony, R Sukanesh

2012-07-01

The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.

ART-Ada design project, phase 2

NASA Technical Reports Server (NTRS)

Lee, S. Daniel; Allen, Bradley P.

1990-01-01

Interest in deploying expert systems in Ada has increased. An Ada based expert system tool is described called ART-Ada, which was built to support research into the language and methodological issues of expert systems in Ada. ART-Ada allows applications of an existing expert system tool called ART-IM (Automated Reasoning Tool for Information Management) to be deployed in various Ada environments. ART-IM, a C-based expert system tool, is used to generate Ada source code which is compiled and linked with an Ada based inference engine to produce an Ada executable image. ART-Ada is being used to implement several expert systems for NASA's Space Station Freedom Program and the U.S. Air Force.

ACR appropriateness Criteria® second and third trimester bleeding.

PubMed

Podrasky, Ann E; Javitt, Marcia C; Glanc, Phyllis; Dubinsky, Theodore; Harisinghani, Mukesh G; Harris, Robert D; Khati, Nadia J; Mitchell, Donald G; Pandharipande, Pari V; Pannu, Harpreet K; Shipp, Thomas D; Siegel, Cary Lynn; Simpson, Lynn; Wall, Darci J; Wong-You-Cheong, Jade J; Zelop, Carolyn M

2013-12-01

Vaginal bleeding occurring in the second or third trimesters of pregnancy can variably affect perinatal outcome, depending on whether it is minor (i.e. a single, mild episode) or major (heavy bleeding or multiple episodes.) Ultrasound is used to evaluate these patients. Sonographic findings may range from marginal subchorionic hematoma to placental abruption. Abnormal placentations such as placenta previa, placenta accreta and vasa previa require accurate diagnosis for clinical management. In cases of placenta accreta, magnetic resonance imaging is useful as an adjunct to ultrasound and is often appropriate for evaluation of the extent of placental invasiveness and potential involvement of adjacent structures. MRI is useful for preplanning for cases of complex delivery, which may necessitate a multi-disciplinary approach for optimal care.The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every two years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances where evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.

ACR Appropriateness Criteria® Colorectal Cancer Screening.

PubMed

Moreno, Courtney; Kim, David H; Bartel, Twyla B; Cash, Brooks D; Chang, Kevin J; Feig, Barry W; Fowler, Kathryn J; Garcia, Evelyn M; Kambadakone, Avinash R; Lambert, Drew L; Levy, Angela D; Marin, Daniele; Peterson, Christine M; Scheirey, Christopher D; Smith, Martin P; Weinstein, Stefanie; Carucci, Laura R

2018-05-01

This review summarizes the relevant literature regarding colorectal screening with imaging. For individuals at average or moderate risk for colorectal cancer, CT colonography is usually appropriate for colorectal cancer screening. After positive results on a fecal occult blood test or immunohistochemical test, CT colonography is usually appropriate for colorectal cancer detection. For individuals at high risk for colorectal cancer (eg, hereditary nonpolyposis colorectal cancer, ulcerative colitis, or Crohn colitis), optical colonoscopy is preferred because of its ability to obtain biopsies to detect dysplasia. After incomplete colonoscopy, CT colonography is usually appropriate for colorectal cancer screening for individuals at average, moderate, or high risk. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Diagnostic index: an open-source tool to classify TMJ OA condyles

NASA Astrophysics Data System (ADS)

Paniagua, Beatriz; Pascal, Laura; Prieto, Juan; Vimort, Jean Baptiste; Gomes, Liliane; Yatabe, Marilia; Ruellas, Antonio Carlos; Budin, Francois; Pieper, Steve; Styner, Martin; Benavides, Erika; Cevidanes, Lucia

2017-03-01

Osteoarthritis (OA) of temporomandibular joints (TMJ) occurs in about 40% of the patients who present TMJ disorders. Despite its prevalence, OA diagnosis and treatment remain controversial since there are no clear symptoms of the disease, especially in early stages. Quantitative tools based on 3D imaging of the TMJ condyle have the potential to help characterize TMJ OA changes. The goals of the tools proposed in this study are to ultimately develop robust imaging markers for diagnosis and assessment of treatment efficacy. This work proposes to identify differences among asymptomatic controls and different clinical phenotypes of TMJ OA by means of Statistical Shape Modeling (SSM), obtained via clinical expert consensus. From three different grouping schemes (with 3, 5 and 7 groups), our best results reveal that that the majority (74.5%) of the classifications occur in agreement with the groups assigned by consensus between our clinical experts. Our findings suggest the existence of different disease-based phenotypic morphologies in TMJ OA. Our preliminary findings with statistical shape modeling based biomarkers may provide a quantitative staging of the disease. The methodology used in this study is included in an open source image analysis toolbox, to ensure reproducibility and appropriate distribution and dissemination of the solution proposed.

[Can the degree of renal artery stenosis be automatically quantified?].

PubMed

Cherrak, I; Jaulent, M C; Azizi, M; Plouin, P F; Degoulet, P; Chatellier, G

2000-08-01

The objective of the reported study is to validate a computer system, QUASAR, dedicated to the quantification of renal artery stenoses. This system estimates automatically the reference diameter and calculates the minimum diameter to compute a degree of stenosis. A hundred and eighty images of atheromatous stenoses between 10% and 80% were collected from two French independent protocols. For the 49 images of the EMMA protocol, the results from QUASAR were compared with the visual estimation of an initial investigator and with the results from a reference method based on a panel of fixe experienced experts. For the 131 images of the ASTARTE protocol, the results from QUASAR were compared with those from a semi-automatic quantification system and with those from a system based on densitometric analysis. The present work validates QUASAR in a population of narrow atheromatous stenoses (> 50%). In the context of the EMMA protocol, QUASAR is not significantly different from the mean of the fixe experts. It is unbiased and more precise than the estimation of a single investigator. In the context of the ASTARTE protocol, there is no significant difference between the three methods for the stenoses higher than 50%, however, globally, QUASAR surestimates significantly (up to 10%) the degree of stenosis.

NESSUS/EXPERT - An expert system for probabilistic structural analysis methods

NASA Technical Reports Server (NTRS)

Millwater, H.; Palmer, K.; Fink, P.

1988-01-01

An expert system (NESSUS/EXPERT) is presented which provides assistance in using probabilistic structural analysis methods. NESSUS/EXPERT is an interactive menu-driven expert system that provides information to assist in the use of the probabilistic finite element code NESSUS/FEM and the fast probability integrator. NESSUS/EXPERT was developed with a combination of FORTRAN and CLIPS, a C language expert system tool, to exploit the strengths of each language.

Development of an Expert System as a Diagnostic Support of Cervical Cancer in Atypical Glandular Cells, Based on Fuzzy Logics and Image Interpretation

PubMed Central

Domínguez Hernández, Karem R.; Aguilar Lasserre, Alberto A.; Posada Gómez, Rubén; Palet Guzmán, José A.; González Sánchez, Blanca E.

2013-01-01

Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient's clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN. PMID:23690881

Interpretation of remotely downloaded pocket-size cardiac ultrasound images on a web-enabled smartphone: validation against workstation evaluation.

PubMed

Choi, Brian G; Mukherjee, Monica; Dala, Praveen; Young, Heather A; Tracy, Cynthia M; Katz, Richard J; Lewis, Jannet F

2011-12-01

Pocket-size ultrasound has increased echocardiographic portability, but expert point-of-care interpretation may not be readily available. The aim of this study was to test the hypothesis that remote interpretation on a smartphone with dedicated medical imaging software can be as accurate as on a workstation. Eighty-nine patients in a remote Honduran village underwent echocardiography by a nonexpert using a pocket-size ultrasound device. Images were sent for verification of point-of-care diagnosis to two expert echocardiographers in the United States reading on a workstation. Studies were then anonymized, randomly ordered, and reinterpreted on a smartphone with a dedicated, Health Insurance Portability and Accountability Act-compliant application. Point-of-care diagnosis was considered accurate if any abnormal finding was matched and categorized at the same level of severity (mild, moderate, or severe) by either expert interpretation. The mean age was 54 ± 23 years, and 57% of patients were women. The most common indications for echocardiography were arrhythmia (33%), cardiomyopathy (28%), and syncope (15%). Using the workstation, point-of-care diagnoses were changed in 38% of cases by expert overread (41% left ventricular function correction, 38% valvulopathy correction, 18% poor image quality). Expert interobserver agreement was excellent at 82%, with a Cohen's κ value of 0.82 (95% confidence interval, 0.70-0.94). Intraobserver agreement comparing interpretations on workstations and smartphones was 90%, with a Cohen's κ value of 0.86 (95% confidence interval, 0.76-0.97), signifying excellent intertechnology agreement. Remote expert echocardiographic interpretation can provide backup support to point-of-care diagnosis by nonexperts when read on a dedicated smartphone-based application. Mobile-to-mobile consultation may improve access in previously inaccessible locations to accurate echocardiographic interpretation by experienced cardiologists. Copyright © 2011 American Society of Echocardiography. Published by Mosby, Inc. All rights reserved.

Atlas-based automatic measurements of the morphology of the tibiofemoral joint

NASA Astrophysics Data System (ADS)

Brehler, M.; Thawait, G.; Shyr, W.; Ramsay, J.; Siewerdsen, J. H.; Zbijewski, W.

2017-03-01

Purpose: Anatomical metrics of the tibiofemoral joint support assessment of joint stability and surgical planning. We propose an automated, atlas-based algorithm to streamline the measurements in 3D images of the joint and reduce userdependence of the metrics arising from manual identification of the anatomical landmarks. Methods: The method is initialized with coarse registrations of a set of atlas images to the fixed input image. The initial registrations are then refined separately for the tibia and femur and the best matching atlas is selected. Finally, the anatomical landmarks of the best matching atlas are transformed onto the input image by deforming a surface model of the atlas to fit the shape of the tibial plateau in the input image (a mesh-to-volume registration). We apply the method to weight-bearing volumetric images of the knee obtained from 23 subjects using an extremity cone-beam CT system. Results of the automated algorithm were compared to an expert radiologist for measurements of Static Alignment (SA), Medial Tibial Slope (MTS) and Lateral Tibial Slope (LTS). Results: Intra-reader variability as high as 10% for LTS and 7% for MTS (ratio of standard deviation to the mean in repeated measurements) was found for expert radiologist, illustrating the potential benefits of an automated approach in improving the precision of the metrics. The proposed method achieved excellent registration of the atlas mesh to the input volumes. The resulting automated measurements yielded high correlations with expert radiologist, as indicated by correlation coefficients of 0.72 for MTS, 0.8 for LTS, and 0.89 for SA. Conclusions: The automated method for measurement of anatomical metrics of the tibiofemoral joint achieves high correlation with expert radiologist without the need for time consuming and error prone manual selection of landmarks.

SUVI Thematic Maps: A new tool for space weather forecasting

NASA Astrophysics Data System (ADS)

Hughes, J. M.; Seaton, D. B.; Darnel, J.

2017-12-01

The new Solar Ultraviolet Imager (SUVI) instruments aboard NOAA's GOES-R series satellites collect continuous, high-quality imagery of the Sun in six wavelengths. SUVI imagers produce at least one image every 10 seconds, or 8,640 images per day, considerably more data than observers can digest in real time. Over the projected 20-year lifetime of the four GOES-R series spacecraft, SUVI will provide critical imagery for space weather forecasters and produce an extensive but unwieldy archive. In order to condense the database into a dynamic and searchable form we have developed solar thematic maps, maps of the Sun with key features, such as coronal holes, flares, bright regions, quiet corona, and filaments, identified. Thematic maps will be used in NOAA's Space Weather Prediction Center to improve forecaster response time to solar events and generate several derivative products. Likewise, scientists use thematic maps to find observations of interest more easily. Using an expert-trained, naive Bayesian classifier to label each pixel, we create thematic maps in real-time. We created software to collect expert classifications of solar features based on SUVI images. Using this software, we compiled a database of expert classifications, from which we could characterize the distribution of pixels associated with each theme. Given new images, the classifier assigns each pixel the most appropriate label according to the trained distribution. Here we describe the software to collect expert training and the successes and limitations of the classifier. The algorithm excellently identifies coronal holes but fails to consistently detect filaments and prominences. We compare the Bayesian classifier to an artificial neural network, one of our attempts to overcome the aforementioned limitations. These results are very promising and encourage future research into an ensemble classification approach.

Atlas-based automatic measurements of the morphology of the tibiofemoral joint.

PubMed

Brehler, M; Thawait, G; Shyr, W; Ramsay, J; Siewerdsen, J H; Zbijewski, W

2017-02-11

Anatomical metrics of the tibiofemoral joint support assessment of joint stability and surgical planning. We propose an automated, atlas-based algorithm to streamline the measurements in 3D images of the joint and reduce user-dependence of the metrics arising from manual identification of the anatomical landmarks. The method is initialized with coarse registrations of a set of atlas images to the fixed input image. The initial registrations are then refined separately for the tibia and femur and the best matching atlas is selected. Finally, the anatomical landmarks of the best matching atlas are transformed onto the input image by deforming a surface model of the atlas to fit the shape of the tibial plateau in the input image (a mesh-to-volume registration). We apply the method to weight-bearing volumetric images of the knee obtained from 23 subjects using an extremity cone-beam CT system. Results of the automated algorithm were compared to an expert radiologist for measurements of Static Alignment (SA), Medial Tibial Slope (MTS) and Lateral Tibial Slope (LTS). Intra-reader variability as high as ~10% for LTS and 7% for MTS (ratio of standard deviation to the mean in repeated measurements) was found for expert radiologist, illustrating the potential benefits of an automated approach in improving the precision of the metrics. The proposed method achieved excellent registration of the atlas mesh to the input volumes. The resulting automated measurements yielded high correlations with expert radiologist, as indicated by correlation coefficients of 0.72 for MTS, 0.8 for LTS, and 0.89 for SA. The automated method for measurement of anatomical metrics of the tibiofemoral joint achieves high correlation with expert radiologist without the need for time consuming and error prone manual selection of landmarks.

Search and retrieval of medical images for improved diagnosis of neurodegenerative diseases

NASA Astrophysics Data System (ADS)

Ekin, Ahmet; Jasinschi, Radu; Turan, Erman; Engbers, Rene; van der Grond, Jeroen; van Buchem, Mark A.

2007-01-01

In the medical world, the accuracy of diagnosis is mainly affected by either lack of sufficient understanding of some diseases or the inter-, and/or intra-observer variability of the diagnoses. The former requires understanding the progress of diseases at much earlier stages, extraction of important information from ever growing amounts of data, and finally finding correlations with certain features and complications that will illuminate the disease progression. The latter (inter-, and intra- observer variability) is caused by the differences in the experience levels of different medical experts (inter-observer variability) or by mental and physical tiredness of one expert (intra-observer variability). We believe that the use of large databases can help improve the current status of disease understanding and decision making. By comparing large number of patients, some of the otherwise hidden relations can be revealed that results in better understanding, patients with similar complications can be found, the diagnosis and treatment can be compared so that the medical expert can make a better diagnosis. To this effect, this paper introduces a search and retrieval system for brain MR databases and shows that brain iron accumulation shape provides additional information to the shape-insensitive features, such as the total brain iron load, that are commonly used in the clinics. We propose to use Kendall's correlation value to automatically compare various returns to a query. We also describe a fully automated and fast brain MR image analysis system to detect degenerative iron accumulation in brain, as it is the case in Alzheimer's and Parkinson's. The system is composed of several novel image processing algorithms and has been extensively tested in Leiden University Medical Center over so far more than 600 patients.

Battling fire and ice: remote guidance ultrasound to diagnose injury on the International Space Station and the ice rink.

PubMed

Kwon, David; Bouffard, J Antonio; van Holsbeeck, Marnix; Sargsyan, Asot E; Hamilton, Douglas R; Melton, Shannon L; Dulchavsky, Scott A

2007-03-01

National Aeronautical and Space and Administration (NASA) researchers have optimized training methods that allow minimally trained, non-physician operators to obtain diagnostic ultrasound (US) images for medical diagnosis including musculoskeletal injury. We hypothesize that these techniques could be expanded to non-expert operators including National Hockey League (NHL) and Olympic athletic trainers to diagnose musculoskeletal injuries in athletes. NHL and Olympic athletic trainers received a brief course on musculoskeletal US. Remote guidance musculoskeletal examinations were conducted by athletic trainers, consisting of hockey groin hernia, knee, ankle, elbow, or shoulder evaluations. US images were transmitted to remote experts for interpretation. Groin, knee, ankle, elbow, or shoulder images were obtained on 32 athletes; all real-time US video stream and still capture images were considered adequate for diagnostic interpretation. This experience suggests that US can be expanded for use in locations without a high level of on-site expertise. A non-physician with minimal training can perform complex, diagnostic-quality examinations when directed by a remote-based expert.

Mobile tele-echography: user interface design.

PubMed

Cañero, Cristina; Thomos, Nikolaos; Triantafyllidis, George A; Litos, George C; Strintzis, Michael Gerassimos

2005-03-01

Ultrasound imaging allows the evaluation of the degree of emergency of a patient. However, in some instances, a well-trained sonographer is unavailable to perform such echography. To cope with this issue, the Mobile Tele-Echography Using an Ultralight Robot (OTELO) project aims to develop a fully integrated end-to-end mobile tele-echography system using an ultralight remote-controlled robot for population groups that are not served locally by medical experts. This paper focuses on the user interface of the OTELO system, consisting of the following parts: an ultrasound video transmission system providing real-time images of the scanned area, an audio/video conference to communicate with the paramedical assistant and with the patient, and a virtual-reality environment, providing visual and haptic feedback to the expert, while capturing the expert's hand movements. These movements are reproduced by the robot at the patient site while holding the ultrasound probe against the patient skin. In addition, the user interface includes an image processing facility for enhancing the received images and the possibility to include them into a database.

Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

PubMed

Kohli, Marc D; Summers, Ronald M; Geis, J Raymond

2017-08-01

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.

Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

PubMed

Romeo, Valeria; Maurea, Simone; Cuocolo, Renato; Petretta, Mario; Mainenti, Pier Paolo; Verde, Francesco; Coppola, Milena; Dell'Aversana, Serena; Brunetti, Arturo

2018-01-17

Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest. To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach. Retrospective, observational study. Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL. Unenhanced T 1 -weighted in-phase (IP) and out-of-phase (OP) as well as T 2 -weighted (T 2 -w) MR images acquired at 3T. Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T 2 -w images. Different selection methods were trained and tested using the J48 machine-learning classifiers. The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test. A total of 138 TA-derived features were extracted; among these, four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP (Mean_Intensity and Maximum_3D_Diameter), and T 2 -w (Standard_Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%. The expert radiologist obtained a diagnostic accuracy of 73%. McNemar's test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist. Machine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions. 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Extreme Tele-Echocardiography: Methodology for Remote Guidance of In-flight Echocardiography Aboard the International Space Station

NASA Technical Reports Server (NTRS)

Martin, David; Borowski, Allan; Bungo, Michael W.; Dulchavsky, Scott; Gladding, Patrick; Greenberg, Neil; Hamilton, Doug; Levine, Benjamin D.; Norwoord, Kelly; Platts, Steven H.;

A Neuroimaging Web Services Interface as a Cyber Physical System for Medical Imaging and Data Management in Brain Research: Design Study.

PubMed

Lizarraga, Gabriel; Li, Chunfei; Cabrerizo, Mercedes; Barker, Warren; Loewenstein, David A; Duara, Ranjan; Adjouadi, Malek

2018-04-26

Structural and functional brain images are essential imaging modalities for medical experts to study brain anatomy. These images are typically visually inspected by experts. To analyze images without any bias, they must be first converted to numeric values. Many software packages are available to process the images, but they are complex and difficult to use. The software packages are also hardware intensive. The results obtained after processing vary depending on the native operating system used and its associated software libraries; data processed in one system cannot typically be combined with data on another system. The aim of this study was to fulfill the neuroimaging community’s need for a common platform to store, process, explore, and visualize their neuroimaging data and results using Neuroimaging Web Services Interface: a series of processing pipelines designed as a cyber physical system for neuroimaging and clinical data in brain research. Neuroimaging Web Services Interface accepts magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and functional magnetic resonance imaging. These images are processed using existing and custom software packages. The output is then stored as image files, tabulated files, and MySQL tables. The system, made up of a series of interconnected servers, is password-protected and is securely accessible through a Web interface and allows (1) visualization of results and (2) downloading of tabulated data. All results were obtained using our processing servers in order to maintain data validity and consistency. The design is responsive and scalable. The processing pipeline started from a FreeSurfer reconstruction of Structural magnetic resonance imaging images. The FreeSurfer and regional standardized uptake value ratio calculations were validated using Alzheimer’s Disease Neuroimaging Initiative input images, and the results were posted at the Laboratory of Neuro Imaging data archive. Notable leading researchers in the field of Alzheimer’s Disease and epilepsy have used the interface to access and process the data and visualize the results. Tabulated results with unique visualization mechanisms help guide more informed diagnosis and expert rating, providing a truly unique multimodal imaging platform that combines magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and resting state functional magnetic resonance imaging. A quality control component was reinforced through expert visual rating involving at least 2 experts. To our knowledge, there is no validated Web-based system offering all the services that Neuroimaging Web Services Interface offers. The intent of Neuroimaging Web Services Interface is to create a tool for clinicians and researchers with keen interest on multimodal neuroimaging. More importantly, Neuroimaging Web Services Interface significantly augments the Alzheimer’s Disease Neuroimaging Initiative data, especially since our data contain a large cohort of Hispanic normal controls and Alzheimer’s Disease patients. The obtained results could be scrutinized visually or through the tabulated forms, informing researchers on subtle changes that characterize the different stages of the disease. ©Gabriel Lizarraga, Chunfei Li, Mercedes Cabrerizo, Warren Barker, David A Loewenstein, Ranjan Duara, Malek Adjouadi. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.04.2018.

BREAST: a novel method to improve the diagnostic efficacy of mammography

NASA Astrophysics Data System (ADS)

Brennan, P. C.; Tapia, K.; Ryan, J.; Lee, W.

2013-03-01

High quality breast imaging and accurate image assessment are critical to the early diagnoses, treatment and management of women with breast cancer. Breast Screen Reader Assessment Strategy (BREAST) provides a platform, accessible by researchers and clinicians world-wide, which will contain image data bases, algorithms to assess reader performance and on-line systems for image evaluation. The platform will contribute to the diagnostic efficacy of breast imaging in Australia and beyond on two fronts: reducing errors in mammography, and transforming our assessment of novel technologies and techniques. Mammography is the primary diagnostic tool for detecting breast cancer with over 800,000 women X-rayed each year in Australia, however, it fails to detect 30% of breast cancers with a number of missed cancers being visible on the image [1-6]. BREAST will monitor the mistakes, identify reasons for mammographic errors, and facilitate innovative solutions to reduce error rates. The BREAST platform has the potential to enable expert assessment of breast imaging innovations, anywhere in the world where experts or innovations are located. Currently, innovations are often being assessed by limited numbers of individuals who happen to be geographically located close to the innovation, resulting in equivocal studies with low statistical power. BREAST will transform this current paradigm by enabling large numbers of experts to assess any new method or technology using our embedded evaluation methods. We are confident that this world-first system will play an important part in the future efficacy of breast imaging.

HoloMonitor M4: holographic imaging cytometer for real-time kinetic label-free live-cell analysis of adherent cells

NASA Astrophysics Data System (ADS)

Sebesta, Mikael; Egelberg, Peter J.; Langberg, Anders; Lindskov, Jens-Henrik; Alm, Kersti; Janicke, Birgit

2016-03-01

Live-cell imaging enables studying dynamic cellular processes that cannot be visualized in fixed-cell assays. An increasing number of scientists in academia and the pharmaceutical industry are choosing live-cell analysis over or in addition to traditional fixed-cell assays. We have developed a time-lapse label-free imaging cytometer HoloMonitorM4. HoloMonitor M4 assists researchers to overcome inherent disadvantages of fluorescent analysis, specifically effects of chemical labels or genetic modifications which can alter cellular behavior. Additionally, label-free analysis is simple and eliminates the costs associated with staining procedures. The underlying technology principle is based on digital off-axis holography. While multiple alternatives exist for this type of analysis, we prioritized our developments to achieve the following: a) All-inclusive system - hardware and sophisticated cytometric analysis software; b) Ease of use enabling utilization of instrumentation by expert- and entrylevel researchers alike; c) Validated quantitative assay end-points tracked over time such as optical path length shift, optical volume and multiple derived imaging parameters; d) Reliable digital autofocus; e) Robust long-term operation in the incubator environment; f) High throughput and walk-away capability; and finally g) Data management suitable for single- and multi-user networks. We provide examples of HoloMonitor applications of label-free cell viability measurements and monitoring of cell cycle phase distribution.

ACR Appropriateness Criteria® Urinary Tract Infection-Child.

PubMed

Karmazyn, Boaz K; Alazraki, Adina L; Anupindi, Sudha A; Dempsey, Molly E; Dillman, Jonathan R; Dorfman, Scott R; Garber, Matthew D; Moore, Sheila G; Peters, Craig A; Rice, Henry E; Rigsby, Cynthia K; Safdar, Nabile M; Simoneaux, Stephen F; Trout, Andrew T; Westra, Sjirk J; Wootton-Gorges, Sandra L; Coley, Brian D

2017-05-01

Urinary tract infection (UTI) is common in young children and may cause pyelonephritis and renal scarring. Long-term complications from renal scarring are low. The role of imaging is to evaluate for underlying urologic abnormalities and guide treatment. In neonates there is increased risk for underlying urologic abnormalities. Evaluation for vesicoureteral reflux (VUR) may be appropriate especially in boys because of higher prevalence of VUR and to exclude posterior urethral valve. In children older than 2 months with first episode of uncomplicated UTI, there is no clear benefit of prophylactic antibiotic. Ultrasound is the only study that is usually appropriate. After the age of 6 years, UTIs are infrequent. There is no need for routine imaging as VUR is less common. In children with recurrent or complicated UTI, in addition to ultrasound, imaging of VUR is usually appropriate. Renal cortical scintigraphy may be appropriate in children with VUR, as renal scarring may support surgical intervention. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

ACR Appropriateness Criteria Low Back Pain.

PubMed

Patel, Nandini D; Broderick, Daniel F; Burns, Judah; Deshmukh, Tejaswini K; Fries, Ian Blair; Harvey, H Benjamin; Holly, Langston; Hunt, Christopher H; Jagadeesan, Bharathi D; Kennedy, Tabassum A; O'Toole, John E; Perlmutter, Joel S; Policeni, Bruno; Rosenow, Joshua M; Schroeder, Jason W; Whitehead, Matthew T; Cornelius, Rebecca S; Corey, Amanda S

2016-09-01

Most patients presenting with uncomplicated acute low back pain (LBP) and/or radiculopathy do not require imaging. Imaging is considered in those patients who have had up to 6 weeks of medical management and physical therapy that resulted in little or no improvement in their back pain. It is also considered for those patients presenting with red flags raising suspicion for serious underlying conditions, such as cauda equina syndrome, malignancy, fracture, and infection. Many imaging modalities are available to clinicians and radiologists for evaluating LBP. Application of these modalities depends largely on the working diagnosis, the urgency of the clinical problem, and comorbidities of the patient. When there is concern for fracture of the lumbar spine, multidetector CT is recommended. Those deemed to be interventional candidates, with LBP lasting for > 6 weeks having completed conservative management with persistent radiculopathic symptoms, may seek MRI. Patients with severe or progressive neurologic deficit on presentation and red flags should be evaluated with MRI. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer-reviewed journals and the application of well-established methodologies (the RAND/UCLA Appropriateness Method and the Grading of Recommendations Assessment, Development, and Evaluation) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances in which evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Expert Consensus Contouring Guidelines for Intensity Modulated Radiation Therapy in Esophageal and Gastroesophageal Junction Cancer.

PubMed

Wu, Abraham J; Bosch, Walter R; Chang, Daniel T; Hong, Theodore S; Jabbour, Salma K; Kleinberg, Lawrence R; Mamon, Harvey J; Thomas, Charles R; Goodman, Karyn A

2015-07-15

Current guidelines for esophageal cancer contouring are derived from traditional 2-dimensional fields based on bony landmarks, and they do not provide sufficient anatomic detail to ensure consistent contouring for more conformal radiation therapy techniques such as intensity modulated radiation therapy (IMRT). Therefore, we convened an expert panel with the specific aim to derive contouring guidelines and generate an atlas for the clinical target volume (CTV) in esophageal or gastroesophageal junction (GEJ) cancer. Eight expert academically based gastrointestinal radiation oncologists participated. Three sample cases were chosen: a GEJ cancer, a distal esophageal cancer, and a mid-upper esophageal cancer. Uniform computed tomographic (CT) simulation datasets and accompanying diagnostic positron emission tomographic/CT images were distributed to each expert, and the expert was instructed to generate gross tumor volume (GTV) and CTV contours for each case. All contours were aggregated and subjected to quantitative analysis to assess the degree of concordance between experts and to generate draft consensus contours. The panel then refined these contours to generate the contouring atlas. The κ statistics indicated substantial agreement between panelists for each of the 3 test cases. A consensus CTV atlas was generated for the 3 test cases, each representing common anatomic presentations of esophageal cancer. The panel agreed on guidelines and principles to facilitate the generalizability of the atlas to individual cases. This expert panel successfully reached agreement on contouring guidelines for esophageal and GEJ IMRT and generated a reference CTV atlas. This atlas will serve as a reference for IMRT contours for clinical practice and prospective trial design. Subsequent patterns of failure analyses of clinical datasets using these guidelines may require modification in the future. Copyright © 2015 Elsevier Inc. All rights reserved.

Chronic obstructive lung disease "expert system": validation of a predictive tool for assisting diagnosis.

PubMed

Braido, Fulvio; Santus, Pierachille; Corsico, Angelo Guido; Di Marco, Fabiano; Melioli, Giovanni; Scichilone, Nicola; Solidoro, Paolo

2018-01-01

The purposes of this study were development and validation of an expert system (ES) aimed at supporting the diagnosis of chronic obstructive lung disease (COLD). A questionnaire and a WebFlex code were developed and validated in silico. An expert panel pilot validation on 60 cases and a clinical validation on 241 cases were performed. The developed questionnaire and code validated in silico resulted in a suitable tool to support the medical diagnosis. The clinical validation of the ES was performed in an academic setting that included six different reference centers for respiratory diseases. The results of the ES expressed as a score associated with the risk of suffering from COLD were matched and compared with the final clinical diagnoses. A set of 60 patients were evaluated by a pilot expert panel validation with the aim of calculating the sample size for the clinical validation study. The concordance analysis between these preliminary ES scores and diagnoses performed by the experts indicated that the accuracy was 94.7% when both experts and the system confirmed the COLD diagnosis and 86.3% when COLD was excluded. Based on these results, the sample size of the validation set was established in 240 patients. The clinical validation, performed on 241 patients, resulted in ES accuracy of 97.5%, with confirmed COLD diagnosis in 53.6% of the cases and excluded COLD diagnosis in 32% of the cases. In 11.2% of cases, a diagnosis of COLD was made by the experts, although the imaging results showed a potential concomitant disorder. The ES presented here (COLD ES ) is a safe and robust supporting tool for COLD diagnosis in primary care settings.

Expert Consensus Contouring Guidelines for Intensity Modulated Radiation Therapy in Esophageal and Gastroesophageal Junction Cancer

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

Wu, Abraham J., E-mail: wua@mskcc.org; Bosch, Walter R.; Chang, Daniel T.

Purpose/Objective(s): Current guidelines for esophageal cancer contouring are derived from traditional 2-dimensional fields based on bony landmarks, and they do not provide sufficient anatomic detail to ensure consistent contouring for more conformal radiation therapy techniques such as intensity modulated radiation therapy (IMRT). Therefore, we convened an expert panel with the specific aim to derive contouring guidelines and generate an atlas for the clinical target volume (CTV) in esophageal or gastroesophageal junction (GEJ) cancer. Methods and Materials: Eight expert academically based gastrointestinal radiation oncologists participated. Three sample cases were chosen: a GEJ cancer, a distal esophageal cancer, and a mid-upper esophagealmore » cancer. Uniform computed tomographic (CT) simulation datasets and accompanying diagnostic positron emission tomographic/CT images were distributed to each expert, and the expert was instructed to generate gross tumor volume (GTV) and CTV contours for each case. All contours were aggregated and subjected to quantitative analysis to assess the degree of concordance between experts and to generate draft consensus contours. The panel then refined these contours to generate the contouring atlas. Results: The κ statistics indicated substantial agreement between panelists for each of the 3 test cases. A consensus CTV atlas was generated for the 3 test cases, each representing common anatomic presentations of esophageal cancer. The panel agreed on guidelines and principles to facilitate the generalizability of the atlas to individual cases. Conclusions: This expert panel successfully reached agreement on contouring guidelines for esophageal and GEJ IMRT and generated a reference CTV atlas. This atlas will serve as a reference for IMRT contours for clinical practice and prospective trial design. Subsequent patterns of failure analyses of clinical datasets using these guidelines may require modification in the future.« less

How automated image analysis techniques help scientists in species identification and classification?

PubMed

Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

2017-09-04

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

A midas plugin to enable construction of reproducible web-based image processing pipelines

PubMed Central

Grauer, Michael; Reynolds, Patrick; Hoogstoel, Marion; Budin, Francois; Styner, Martin A.; Oguz, Ipek

2013-01-01

Image processing is an important quantitative technique for neuroscience researchers, but difficult for those who lack experience in the field. In this paper we present a web-based platform that allows an expert to create a brain image processing pipeline, enabling execution of that pipeline even by those biomedical researchers with limited image processing knowledge. These tools are implemented as a plugin for Midas, an open-source toolkit for creating web based scientific data storage and processing platforms. Using this plugin, an image processing expert can construct a pipeline, create a web-based User Interface, manage jobs, and visualize intermediate results. Pipelines are executed on a grid computing platform using BatchMake and HTCondor. This represents a new capability for biomedical researchers and offers an innovative platform for scientific collaboration. Current tools work well, but can be inaccessible for those lacking image processing expertise. Using this plugin, researchers in collaboration with image processing experts can create workflows with reasonable default settings and streamlined user interfaces, and data can be processed easily from a lab environment without the need for a powerful desktop computer. This platform allows simplified troubleshooting, centralized maintenance, and easy data sharing with collaborators. These capabilities enable reproducible science by sharing datasets and processing pipelines between collaborators. In this paper, we present a description of this innovative Midas plugin, along with results obtained from building and executing several ITK based image processing workflows for diffusion weighted MRI (DW MRI) of rodent brain images, as well as recommendations for building automated image processing pipelines. Although the particular image processing pipelines developed were focused on rodent brain MRI, the presented plugin can be used to support any executable or script-based pipeline. PMID:24416016

A midas plugin to enable construction of reproducible web-based image processing pipelines.

PubMed

Grauer, Michael; Reynolds, Patrick; Hoogstoel, Marion; Budin, Francois; Styner, Martin A; Oguz, Ipek

2013-01-01

Image processing is an important quantitative technique for neuroscience researchers, but difficult for those who lack experience in the field. In this paper we present a web-based platform that allows an expert to create a brain image processing pipeline, enabling execution of that pipeline even by those biomedical researchers with limited image processing knowledge. These tools are implemented as a plugin for Midas, an open-source toolkit for creating web based scientific data storage and processing platforms. Using this plugin, an image processing expert can construct a pipeline, create a web-based User Interface, manage jobs, and visualize intermediate results. Pipelines are executed on a grid computing platform using BatchMake and HTCondor. This represents a new capability for biomedical researchers and offers an innovative platform for scientific collaboration. Current tools work well, but can be inaccessible for those lacking image processing expertise. Using this plugin, researchers in collaboration with image processing experts can create workflows with reasonable default settings and streamlined user interfaces, and data can be processed easily from a lab environment without the need for a powerful desktop computer. This platform allows simplified troubleshooting, centralized maintenance, and easy data sharing with collaborators. These capabilities enable reproducible science by sharing datasets and processing pipelines between collaborators. In this paper, we present a description of this innovative Midas plugin, along with results obtained from building and executing several ITK based image processing workflows for diffusion weighted MRI (DW MRI) of rodent brain images, as well as recommendations for building automated image processing pipelines. Although the particular image processing pipelines developed were focused on rodent brain MRI, the presented plugin can be used to support any executable or script-based pipeline.

Automatic detection of surface changes on Mars - a status report

NASA Astrophysics Data System (ADS)

Sidiropoulos, Panagiotis; Muller, Jan-Peter

2016-10-01

Orbiter missions have acquired approximately 500,000 high-resolution visible images of the Martian surface, covering an area approximately 6 times larger than the overall area of Mars. This data abundance allows the scientific community to examine the Martian surface thoroughly and potentially make exciting new discoveries. However, the increased data volume, as well as its complexity, generate problems at the data processing stages, which are mainly related to a number of unresolved issues that batch-mode planetary data processing presents. As a matter of fact, the scientific community is currently struggling to scale the common ("one-at-a-time" processing of incoming products by expert scientists) paradigm to tackle the large volumes of input data. Moreover, expert scientists are more or less forced to use complex software in order to extract input information for their research from raw data, even though they are not data scientists themselves.Our work within the STFC and EU FP7 i-Mars projects aims at developing automated software that will process all of the acquired data, leaving domain expert planetary scientists to focus on their final analysis and interpretation. Moreover, after completing the development of a fully automated pipeline that processes automatically the co-registration of high-resolution NASA images to ESA/DLR HRSC baseline, our main goal has shifted to the automated detection of surface changes on Mars. In particular, we are developing a pipeline that uses as an input multi-instrument image pairs, which are processed by an automated pipeline, in order to identify changes that are correlated with Mars surface dynamic phenomena. The pipeline has currently been tested in anger on 8,000 co-registered images and by the time of DPS/EPSC we expect to have processed many tens of thousands of image pairs, producing a set of change detection results, a subset of which will be shown in the presentation.The research leading to these results has received funding from the STFC "MSSL Consolidated Grant under "Planetary Surface Data Mining" ST/K000977/1 and partial support from the European Union's Seventh Framework Programme (FP7/2007-2013) under iMars grant agreement number 607379

A fast and efficient segmentation scheme for cell microscopic image.

PubMed

Lebrun, G; Charrier, C; Lezoray, O; Meurie, C; Cardot, H

2007-04-27

Microscopic cellular image segmentation schemes must be efficient for reliable analysis and fast to process huge quantity of images. Recent studies have focused on improving segmentation quality. Several segmentation schemes have good quality but processing time is too expensive to deal with a great number of images per day. For segmentation schemes based on pixel classification, the classifier design is crucial since it is the one which requires most of the processing time necessary to segment an image. The main contribution of this work is focused on how to reduce the complexity of decision functions produced by support vector machines (SVM) while preserving recognition rate. Vector quantization is used in order to reduce the inherent redundancy present in huge pixel databases (i.e. images with expert pixel segmentation). Hybrid color space design is also used in order to improve data set size reduction rate and recognition rate. A new decision function quality criterion is defined to select good trade-off between recognition rate and processing time of pixel decision function. The first results of this study show that fast and efficient pixel classification with SVM is possible. Moreover posterior class pixel probability estimation is easy to compute with Platt method. Then a new segmentation scheme using probabilistic pixel classification has been developed. This one has several free parameters and an automatic selection must dealt with, but criteria for evaluate segmentation quality are not well adapted for cell segmentation, especially when comparison with expert pixel segmentation must be achieved. Another important contribution in this paper is the definition of a new quality criterion for evaluation of cell segmentation. The results presented here show that the selection of free parameters of the segmentation scheme by optimisation of the new quality cell segmentation criterion produces efficient cell segmentation.

ACR Appropriateness Criteria® Stage I Breast Cancer: Initial Workup and Surveillance for Local Recurrence and Distant Metastases in Asymptomatic Women.

PubMed

Moy, Linda; Bailey, Lisa; D'Orsi, Carl; Green, Edward D; Holbrook, Anna I; Lee, Su-Ju; Lourenco, Ana P; Mainiero, Martha B; Sepulveda, Karla A; Slanetz, Priscilla J; Trikha, Sunita; Yepes, Monica M; Newell, Mary S

2017-05-01

Women and health care professionals generally prefer intensive follow-up after a diagnosis of breast cancer. However, there are no survival differences between women who obtain intensive surveillance with imaging and laboratory studies compared with women who only undergo testing because of the development of symptoms or findings on clinical examinations. American Society of Clinical Oncology and National Comprehensive Cancer Network guidelines state that annual mammography is the only imaging examination that should be performed to detect a localized breast recurrence in asymptomatic patients; more imaging may be needed if the patient has locoregional symptoms (eg, palpable abnormality). Women with other risk factors that increase their lifetime risk for breast cancer may warrant evaluation with breast MRI. Furthermore, the quality of life is similar for women who undergo intensive surveillance compared with those who do not. There is little justification for imaging to detect or rule out metastasis in asymptomatic women with newly diagnosed stage I breast cancer. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

ACR appropriateness criteria blunt chest trauma.

PubMed

Chung, Jonathan H; Cox, Christian W; Mohammed, Tan-Lucien H; Kirsch, Jacobo; Brown, Kathleen; Dyer, Debra Sue; Ginsburg, Mark E; Heitkamp, Darel E; Kanne, Jeffrey P; Kazerooni, Ella A; Ketai, Loren H; Ravenel, James G; Saleh, Anthony G; Shah, Rakesh D; Steiner, Robert M; Suh, Robert D

2014-04-01

Imaging is paramount in the setting of blunt trauma and is now the standard of care at any trauma center. Although anteroposterior radiography has inherent limitations, the ability to acquire a radiograph in the trauma bay with little interruption in clinical survey, monitoring, and treatment, as well as radiography's accepted role in screening for traumatic aortic injury, supports the routine use of chest radiography. Chest CT or CT angiography is the gold-standard routine imaging modality for detecting thoracic injuries caused by blunt trauma. There is disagreement on whether routine chest CT is necessary in all patients with histories of blunt trauma. Ultimately, the frequency and timing of CT chest imaging should be site specific and should depend on the local resources of the trauma center as well as patient status. Ultrasound may be beneficial in the detection of pneumothorax, hemothorax, and pericardial hemorrhage; transesophageal echocardiography is a first-line imaging tool in the setting of suspected cardiac injury. In the blunt trauma setting, MRI and nuclear medicine likely play no role in the acute setting, although these modalities may be helpful as problem-solving tools after initial assessment. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every 2 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment. Copyright © 2014 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment.

PubMed

Raunig, David L; McShane, Lisa M; Pennello, Gene; Gatsonis, Constantine; Carson, Paul L; Voyvodic, James T; Wahl, Richard L; Kurland, Brenda F; Schwarz, Adam J; Gönen, Mithat; Zahlmann, Gudrun; Kondratovich, Marina V; O'Donnell, Kevin; Petrick, Nicholas; Cole, Patricia E; Garra, Brian; Sullivan, Daniel C

2015-02-01

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures

PubMed Central

Eltzner, Benjamin; Wollnik, Carina; Gottschlich, Carsten; Huckemann, Stephan; Rehfeldt, Florian

2015-01-01

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length, and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source. PMID:25996921

ACR Appropriateness Criteria® Ovarian Cancer Screening.

PubMed

Pandharipande, Pari V; Lowry, Kathryn P; Reinhold, Caroline; Atri, Mostafa; Benson, Carol B; Bhosale, Priyadarshani R; Green, Edward D; Kang, Stella K; Lakhman, Yulia; Maturen, Katherine E; Nicola, Refky; Salazar, Gloria M; Shipp, Thomas D; Simpson, Lynn; Sussman, Betsy L; Uyeda, Jennifer; Wall, Darci J; Whitcomb, Bradford; Zelop, Carolyn M; Glanc, Phyllis

2017-11-01

There has been much interest in the identification of a successful ovarian cancer screening test, in particular, one that can detect ovarian cancer at an early stage and improve survival. We reviewed the currently available data from randomized and observational trials that examine the role of imaging for ovarian cancer screening in average-risk and high-risk women. We found insufficient evidence to recommend ovarian cancer screening, when considering the imaging modality (pelvic ultrasound) and population (average-risk postmenopausal women) for which there is the greatest available published evidence; randomized controlled trials have not demonstrated a mortality benefit in this setting. Screening high-risk women using pelvic ultrasound may be appropriate in some clinical situations; however, related data are limited because large, randomized trials have not been performed in this setting. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

ACR Appropriateness Criteria® rib fractures.

PubMed

Henry, Travis S; Kirsch, Jacobo; Kanne, Jeffrey P; Chung, Jonathan H; Donnelly, Edwin F; Ginsburg, Mark E; Heitkamp, Darel E; Kazerooni, Ella A; Ketai, Loren H; McComb, Barbara L; Parker, J Anthony; Ravenel, James G; Restrepo, Carlos Santiago; Saleh, Anthony G; Shah, Rakesh D; Steiner, Robert M; Suh, Robert D; Mohammed, Tan-Lucien H

2014-11-01

Rib fracture is the most common thoracic injury, present in 10% of all traumatic injuries and almost 40% of patients who sustain severe nonpenetrating trauma. Although rib fractures can produce significant morbidity, the diagnosis of associated complications (such as pneumothorax, hemothorax, pulmonary contusion, atelectasis, flail chest, cardiovascular injury, and injuries to solid and hollow abdominal organs) may have a more significant clinical impact. When isolated, rib fractures have a relatively low morbidity and mortality, and failure to detect isolated rib fractures does not necessarily alter patient management or outcome in uncomplicated cases. A standard posteroanterior chest radiograph should be the initial, and often the only, imaging test required in patients with suspected rib fracture after minor trauma. Detailed radiographs of the ribs rarely add additional information that would change treatment, and, although other imaging tests (eg, computed tomography, bone scan) have increased sensitivity for detection of rib fractures, there are little data to support their use. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every 3 years by a multidisciplinary expert panel. The guideline development and review process include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.

ACR Appropriateness Criteria Evaluation of the Symptomatic Male Breast.

PubMed

Mainiero, Martha B; Lourenco, Ana P; Barke, Lora D; Argus, Amy D; Bailey, Lisa; Carkaci, Selin; D'Orsi, Carl; Green, Edward D; Holley, Susan O; Jokich, Peter M; Lee, Su-Ju; Mahoney, Mary C; Moy, Linda; Slanetz, Priscilla J; Trikha, Sunita; Yepes, Monica M; Newell, Mary S

2015-07-01

Most male breast problems are benign, and men with typical symptoms of gynecomastia or pseudogynecomastia do not usually need imaging. When a differentiation between benign disease and breast cancer cannot be made on the basis of clinical findings or when the clinical findings are suspicious for breast cancer, imaging is indicated. Mammography is useful in both identifying cancer and obviating the need for biopsy in patients for whom a benign mammographic impression confirms the clinical impression. However, because of the relationship of breast cancer to increasing age, age-based protocols that do not include mammography have been developed. For men with an indeterminate palpable mass, begin with ultrasound if the patient is <25 years of age, because breast cancer is highly unlikely. Mammography should be performed if ultrasound is suspicious. For men ≥25 years of age or having a highly concerning physical examination, usually begin with mammography; ultrasound is useful if mammography is inconclusive or suspicious. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every 3 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals, and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

How visual search relates to visual diagnostic performance: a narrative systematic review of eye-tracking research in radiology.

PubMed

van der Gijp, A; Ravesloot, C J; Jarodzka, H; van der Schaaf, M F; van der Schaaf, I C; van Schaik, J P J; Ten Cate, Th J

2017-08-01

Eye tracking research has been conducted for decades to gain understanding of visual diagnosis such as in radiology. For educational purposes, it is important to identify visual search patterns that are related to high perceptual performance and to identify effective teaching strategies. This review of eye-tracking literature in the radiology domain aims to identify visual search patterns associated with high perceptual performance. Databases PubMed, EMBASE, ERIC, PsycINFO, Scopus and Web of Science were searched using 'visual perception' OR 'eye tracking' AND 'radiology' and synonyms. Two authors independently screened search results and included eye tracking studies concerning visual skills in radiology published between January 1, 1994 and July 31, 2015. Two authors independently assessed study quality with the Medical Education Research Study Quality Instrument, and extracted study data with respect to design, participant and task characteristics, and variables. A thematic analysis was conducted to extract and arrange study results, and a textual narrative synthesis was applied for data integration and interpretation. The search resulted in 22 relevant full-text articles. Thematic analysis resulted in six themes that informed the relation between visual search and level of expertise: (1) time on task, (2) eye movement characteristics of experts, (3) differences in visual attention, (4) visual search patterns, (5) search patterns in cross sectional stack imaging, and (6) teaching visual search strategies. Expert search was found to be characterized by a global-focal search pattern, which represents an initial global impression, followed by a detailed, focal search-to-find mode. Specific task-related search patterns, like drilling through CT scans and systematic search in chest X-rays, were found to be related to high expert levels. One study investigated teaching of visual search strategies, and did not find a significant effect on perceptual performance. Eye tracking literature in radiology indicates several search patterns are related to high levels of expertise, but teaching novices to search as an expert may not be effective. Experimental research is needed to find out which search strategies can improve image perception in learners.

Evaluating performance of a user-trained MR lung tumor autocontouring algorithm in the context of intra- and interobserver variations.

PubMed

Yip, Eugene; Yun, Jihyun; Gabos, Zsolt; Baker, Sarah; Yee, Don; Wachowicz, Keith; Rathee, Satyapal; Fallone, B Gino

2018-01-01

Real-time tracking of lung tumors using magnetic resonance imaging (MRI) has been proposed as a potential strategy to mitigate the ill-effects of breathing motion in radiation therapy. Several autocontouring methods have been evaluated against a "gold standard" of a single human expert user. However, contours drawn by experts have inherent intra- and interobserver variations. In this study, we aim to evaluate our user-trained autocontouring algorithm with manually drawn contours from multiple expert users, and to contextualize the accuracy of these autocontours within intra- and interobserver variations. Six nonsmall cell lung cancer patients were recruited, with institutional ethics approval. Patients were imaged with a clinical 3 T Philips MR scanner using a dynamic 2D balanced SSFP sequence under free breathing. Three radiation oncology experts, each in two separate sessions, contoured 130 dynamic images for each patient. For autocontouring, the first 30 images were used for algorithm training, and the remaining 100 images were autocontoured and evaluated. Autocontours were compared against manual contours in terms of Dice's coefficient (DC) and Hausdorff distances (d H ). Intra- and interobserver variations of the manual contours were also evaluated. When compared with the manual contours of the expert user who trained it, the algorithm generates autocontours whose evaluation metrics (same session: DC = 0.90(0.03), d H  = 3.8(1.6) mm; different session DC = 0.88(0.04), d H  = 4.3(1.5) mm) are similar to or better than intraobserver variations (DC = 0.88(0.04), and d H  = 4.3(1.7) mm) between two sessions. The algorithm's autocontours are also compared to the manual contours from different expert users with evaluation metrics (DC = 0.87(0.04), d H  = 4.8(1.7) mm) similar to interobserver variations (DC = 0.87(0.04), d H  = 4.7(1.6) mm). Our autocontouring algorithm delineates tumor contours (<20 ms per contour), in dynamic MRI of lung, that are comparable to multiple human experts (several seconds per contour), but at a much faster speed. At the same time, the agreement between autocontours and manual contours is comparable to the intra- and interobserver variations. This algorithm may be a key component of the real time tumor tracking workflow for our hybrid Linac-MR device in the future. © 2017 American Association of Physicists in Medicine.

Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.

PubMed

Lao, Zhiqiang; Shen, Dinggang; Liu, Dengfeng; Jawad, Abbas F; Melhem, Elias R; Launer, Lenore J; Bryan, R Nick; Davatzikos, Christos

2008-03-01

Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this article, we present a computer-assisted WML segmentation method, based on local features extracted from multiparametric magnetic resonance imaging (MRI) sequences (ie, T1-weighted, T2-weighted, proton density-weighted, and fluid attenuation inversion recovery MRI scans). A support vector machine classifier is first trained on expert-defined WMLs, and is then used to classify new scans. Postprocessing analysis further reduces false positives by using anatomic knowledge and measures of distance from the training set. Cross-validation on a population of 35 patients from three different imaging sites with WMLs of varying sizes, shapes, and locations tests the robustness and accuracy of the proposed segmentation method, compared with the manual segmentation results from two experienced neuroradiologists.

Retinopathy of Prematurity-assist: Novel Software for Detecting Plus Disease

PubMed Central

Pour, Elias Khalili; Pourreza, Hamidreza; Zamani, Kambiz Ameli; Mahmoudi, Alireza; Sadeghi, Arash Mir Mohammad; Shadravan, Mahla; Karkhaneh, Reza; Pour, Ramak Rouhi

2017-01-01

Purpose To design software with a novel algorithm, which analyzes the tortuosity and vascular dilatation in fundal images of retinopathy of prematurity (ROP) patients with an acceptable accuracy for detecting plus disease. Methods Eighty-seven well-focused fundal images taken with RetCam were classified to three groups of plus, non-plus, and pre-plus by agreement between three ROP experts. Automated algorithms in this study were designed based on two methods: the curvature measure and distance transform for assessment of tortuosity and vascular dilatation, respectively as two major parameters of plus disease detection. Results Thirty-eight plus, 12 pre-plus, and 37 non-plus images, which were classified by three experts, were tested by an automated algorithm and software evaluated the correct grouping of images in comparison to expert voting with three different classifiers, k-nearest neighbor, support vector machine and multilayer perceptron network. The plus, pre-plus, and non-plus images were analyzed with 72.3%, 83.7%, and 84.4% accuracy, respectively. Conclusions The new automated algorithm used in this pilot scheme for diagnosis and screening of patients with plus ROP has acceptable accuracy. With more improvements, it may become particularly useful, especially in centers without a skilled person in the ROP field. PMID:29022295

Improved cardiovascular diagnostic accuracy by pocket size imaging device in non-cardiologic outpatients: the NaUSiCa (Naples Ultrasound Stethoscope in Cardiology) study

PubMed Central

2010-01-01

Miniaturization has evolved in the creation of a pocket-size imaging device which can be utilized as an ultrasound stethoscope. This study assessed the additional diagnostic power of pocket size device by both experts operators and trainees in comparison with physical examination and its appropriateness of use in comparison with standard echo machine in a non-cardiologic population. Three hundred four consecutive non cardiologic outpatients underwent a sequential assessment including physical examination, pocket size imaging device and standard Doppler-echo exam. Pocket size device was used by both expert operators and trainees (who received specific training before the beginning of the study). All the operators were requested to give only visual, qualitative insights on specific issues. All standard Doppler-echo exams were performed by expert operators. One hundred two pocket size device exams were performed by experts and two hundred two by trainees. The time duration of the pocket size device exam was 304 ± 117 sec. Diagnosis of cardiac abnormalities was made in 38.2% of cases by physical examination and in 69.7% of cases by physical examination + pocket size device (additional diagnostic power = 31.5%, p < 0.0001). The overall K between pocket size device and standard Doppler-echo was 0.67 in the pooled population (0.84 by experts and 0.58 by trainees). K was suboptimal for trainees in the eyeball evaluation of ejection fraction, left atrial dilation and right ventricular dilation. Overall sensitivity was 91% and specificity 76%. Sensitivity and specificity were lower in trainees than in experts. In conclusion, pocket size device showed a relevant additional diagnostic value in comparison with physical examination. Sensitivity and specificity were good in experts and suboptimal in trainees. Specificity was particularly influenced by the level of experience. Training programs are needed for pocket size device users. PMID:21110840

Expert-novice differences in brain function of field hockey players.

PubMed

Wimshurst, Z L; Sowden, P T; Wright, M

2016-02-19

The aims of this study were to use functional magnetic resonance imaging to examine the neural bases for perceptual-cognitive superiority in a hockey anticipation task. Thirty participants (15 hockey players, 15 non-hockey players) lay in an MRI scanner while performing a video-based task in which they predicted the direction of an oncoming shot in either a hockey or a badminton scenario. Video clips were temporally occluded either 160 ms before the shot was made or 60 ms after the ball/shuttle left the stick/racquet. Behavioral data showed a significant hockey expertise×video-type interaction in which hockey experts were superior to novices with hockey clips but there were no significant differences with badminton clips. The imaging data on the other hand showed a significant main effect of hockey expertise and of video type (hockey vs. badminton), but the expertise×video-type interaction did not survive either a whole-brain or a small-volume correction for multiple comparisons. Further analysis of the expertise main effect revealed that when watching hockey clips, experts showed greater activation in the rostral inferior parietal lobule, which has been associated with an action observation network, and greater activation than novices in Brodmann areas 17 and 18 and middle frontal gyrus when watching badminton videos. The results provide partial support both for domain-specific and domain-general expertise effects in an action anticipation task. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Design and validation of realistic breast models for use in multiple alternative forced choice virtual clinical trials

NASA Astrophysics Data System (ADS)

Elangovan, Premkumar; Mackenzie, Alistair; Dance, David R.; Young, Kenneth C.; Cooke, Victoria; Wilkinson, Louise; Given-Wilson, Rosalind M.; Wallis, Matthew G.; Wells, Kevin

2017-04-01

A novel method has been developed for generating quasi-realistic voxel phantoms which simulate the compressed breast in mammography and digital breast tomosynthesis (DBT). The models are suitable for use in virtual clinical trials requiring realistic anatomy which use the multiple alternative forced choice (AFC) paradigm and patches from the complete breast image. The breast models are produced by extracting features of breast tissue components from DBT clinical images including skin, adipose and fibro-glandular tissue, blood vessels and Cooper’s ligaments. A range of different breast models can then be generated by combining these components. Visual realism was validated using a receiver operating characteristic (ROC) study of patches from simulated images calculated using the breast models and from real patient images. Quantitative analysis was undertaken using fractal dimension and power spectrum analysis. The average areas under the ROC curves for 2D and DBT images were 0.51  ±  0.06 and 0.54  ±  0.09 demonstrating that simulated and real images were statistically indistinguishable by expert breast readers (7 observers); errors represented as one standard error of the mean. The average fractal dimensions (2D, DBT) for real and simulated images were (2.72  ±  0.01, 2.75  ±  0.01) and (2.77  ±  0.03, 2.82  ±  0.04) respectively; errors represented as one standard error of the mean. Excellent agreement was found between power spectrum curves of real and simulated images, with average β values (2D, DBT) of (3.10  ±  0.17, 3.21  ±  0.11) and (3.01  ±  0.32, 3.19  ±  0.07) respectively; errors represented as one standard error of the mean. These results demonstrate that radiological images of these breast models realistically represent the complexity of real breast structures and can be used to simulate patches from mammograms and DBT images that are indistinguishable from patches from the corresponding real breast images. The method can generate about 500 radiological patches (~30 mm  ×  30 mm) per day for AFC experiments on a single workstation. This is the first study to quantitatively validate the realism of simulated radiological breast images using direct blinded comparison with real data via the ROC paradigm with expert breast readers.

iElectrodes: A Comprehensive Open-Source Toolbox for Depth and Subdural Grid Electrode Localization.

PubMed

Blenkmann, Alejandro O; Phillips, Holly N; Princich, Juan P; Rowe, James B; Bekinschtein, Tristan A; Muravchik, Carlos H; Kochen, Silvia

2017-01-01

The localization of intracranial electrodes is a fundamental step in the analysis of invasive electroencephalography (EEG) recordings in research and clinical practice. The conclusions reached from the analysis of these recordings rely on the accuracy of electrode localization in relationship to brain anatomy. However, currently available techniques for localizing electrodes from magnetic resonance (MR) and/or computerized tomography (CT) images are time consuming and/or limited to particular electrode types or shapes. Here we present iElectrodes, an open-source toolbox that provides robust and accurate semi-automatic localization of both subdural grids and depth electrodes. Using pre- and post-implantation images, the method takes 2-3 min to localize the coordinates in each electrode array and automatically number the electrodes. The proposed pre-processing pipeline allows one to work in a normalized space and to automatically obtain anatomical labels of the localized electrodes without neuroimaging experts. We validated the method with data from 22 patients implanted with a total of 1,242 electrodes. We show that localization distances were within 0.56 mm of those achieved by experienced manual evaluators. iElectrodes provided additional advantages in terms of robustness (even with severe perioperative cerebral distortions), speed (less than half the operator time compared to expert manual localization), simplicity, utility across multiple electrode types (surface and depth electrodes) and all brain regions.

EEG artifact elimination by extraction of ICA-component features using image processing algorithms.

PubMed

Radüntz, T; Scouten, J; Hochmuth, O; Meffert, B

2015-03-30

Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

iElectrodes: A Comprehensive Open-Source Toolbox for Depth and Subdural Grid Electrode Localization

PubMed Central

Blenkmann, Alejandro O.; Phillips, Holly N.; Princich, Juan P.; Rowe, James B.; Bekinschtein, Tristan A.; Muravchik, Carlos H.; Kochen, Silvia

2017-01-01

The localization of intracranial electrodes is a fundamental step in the analysis of invasive electroencephalography (EEG) recordings in research and clinical practice. The conclusions reached from the analysis of these recordings rely on the accuracy of electrode localization in relationship to brain anatomy. However, currently available techniques for localizing electrodes from magnetic resonance (MR) and/or computerized tomography (CT) images are time consuming and/or limited to particular electrode types or shapes. Here we present iElectrodes, an open-source toolbox that provides robust and accurate semi-automatic localization of both subdural grids and depth electrodes. Using pre- and post-implantation images, the method takes 2–3 min to localize the coordinates in each electrode array and automatically number the electrodes. The proposed pre-processing pipeline allows one to work in a normalized space and to automatically obtain anatomical labels of the localized electrodes without neuroimaging experts. We validated the method with data from 22 patients implanted with a total of 1,242 electrodes. We show that localization distances were within 0.56 mm of those achieved by experienced manual evaluators. iElectrodes provided additional advantages in terms of robustness (even with severe perioperative cerebral distortions), speed (less than half the operator time compared to expert manual localization), simplicity, utility across multiple electrode types (surface and depth electrodes) and all brain regions. PMID:28303098

Computational assessment of mammography accreditation phantom images and correlation with human observer analysis

NASA Astrophysics Data System (ADS)

Barufaldi, Bruno; Lau, Kristen C.; Schiabel, Homero; Maidment, D. A.

2015-03-01

Routine performance of basic test procedures and dose measurements are essential for assuring high quality of mammograms. International guidelines recommend that breast care providers ascertain that mammography systems produce a constant high quality image, using as low a radiation dose as is reasonably achievable. The main purpose of this research is to develop a framework to monitor radiation dose and image quality in a mixed breast screening and diagnostic imaging environment using an automated tracking system. This study presents a module of this framework, consisting of a computerized system to measure the image quality of the American College of Radiology mammography accreditation phantom. The methods developed combine correlation approaches, matched filters, and data mining techniques. These methods have been used to analyze radiological images of the accreditation phantom. The classification of structures of interest is based upon reports produced by four trained readers. As previously reported, human observers demonstrate great variation in their analysis due to the subjectivity of human visual inspection. The software tool was trained with three sets of 60 phantom images in order to generate decision trees using the software WEKA (Waikato Environment for Knowledge Analysis). When tested with 240 images during the classification step, the tool correctly classified 88%, 99%, and 98%, of fibers, speck groups and masses, respectively. The variation between the computer classification and human reading was comparable to the variation between human readers. This computerized system not only automates the quality control procedure in mammography, but also decreases the subjectivity in the expert evaluation of the phantom images.

Visual Recognition Software for Binary Classification and its Application to Pollen Identification

NASA Astrophysics Data System (ADS)

Punyasena, S. W.; Tcheng, D. K.; Nayak, A.

2014-12-01

An underappreciated source of uncertainty in paleoecology is the uncertainty of palynological identifications. The confidence of any given identification is not regularly reported in published results, so cannot be incorporated into subsequent meta-analyses. Automated identifications systems potentially provide a means of objectively measuring the confidence of a given count or single identification, as well as a mechanism for increasing sample sizes and throughput. We developed the software ARLO (Automated Recognition with Layered Optimization) to tackle difficult visual classification problems such as pollen identification. ARLO applies pattern recognition and machine learning to the analysis of pollen images. The features that the system discovers are not the traditional features of pollen morphology. Instead, general purpose image features, such as pixel lines and grids of different dimensions, size, spacing, and resolution, are used. ARLO adapts to a given problem by searching for the most effective combination of feature representation and learning strategy. We present a two phase approach which uses our machine learning process to first segment pollen grains from the background and then classify pollen pixels and report species ratios. We conducted two separate experiments that utilized two distinct sets of algorithms and optimization procedures. The first analysis focused on reconstructing black and white spruce pollen ratios, training and testing our classification model at the slide level. This allowed us to directly compare our automated counts and expert counts to slides of known spruce ratios. Our second analysis focused on maximizing classification accuracy at the individual pollen grain level. Instead of predicting ratios of given slides, we predicted the species represented in a given image window. The resulting analysis was more scalable, as we were able to adapt the most efficient parts of the methodology from our first analysis. ARLO was able to distinguish between the pollen of black and white spruce with an accuracy of ~83.61%. This compared favorably to human expert performance. At the writing of this abstract, we are also experimenting with experimenting with the analysis of higher diversity samples, including modern tropical pollen material collected from ground pollen traps.

Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection.

PubMed

Swiderska-Chadaj, Zaneta; Markiewicz, Tomasz; Grala, Bartlomiej; Lorent, Malgorzata

2016-10-07

Hot-spot based examination of immunohistochemically stained histological specimens is one of the most important procedures in pathomorphological practice. The development of image acquisition equipment and computational units allows for the automation of this process. Moreover, a lot of possible technical problems occur in everyday histological material, which increases the complexity of the problem. Thus, a full context-based analysis of histological specimens is also needed in the quantification of immunohistochemically stained specimens. One of the most important reactions is the Ki-67 proliferation marker in meningiomas, the most frequent intracranial tumour. The aim of our study is to propose a context-based analysis of Ki-67 stained specimens of meningiomas for automatic selection of hot-spots. The proposed solution is based on textural analysis, mathematical morphology, feature ranking and classification, as well as on the proposed hot-spot gradual extinction algorithm to allow for the proper detection of a set of hot-spot fields. The designed whole slide image processing scheme eliminates such artifacts as hemorrhages, folds or stained vessels from the region of interest. To validate automatic results, a set of 104 meningioma specimens were selected and twenty hot-spots inside them were identified independently by two experts. The Spearman rho correlation coefficient was used to compare the results which were also analyzed with the help of a Bland-Altman plot. The results show that most of the cases (84) were automatically examined properly with two fields of view with a technical problem at the very most. Next, 13 had three such fields, and only seven specimens did not meet the requirement for the automatic examination. Generally, the Automatic System identifies hot-spot areas, especially their maximum points, better. Analysis of the results confirms the very high concordance between an automatic Ki-67 examination and the expert's results, with a Spearman rho higher than 0.95. The proposed hot-spot selection algorithm with an extended context-based analysis of whole slide images and hot-spot gradual extinction algorithm provides an efficient tool for simulation of a manual examination. The presented results have confirmed that the automatic examination of Ki-67 in meningiomas could be introduced in the near future.

Bayesian Networks for enterprise risk assessment

NASA Astrophysics Data System (ADS)

Bonafede, C. E.; Giudici, P.

2007-08-01

According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. Risk, in general, is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover, qualitative data must be converted in numerical values or bounds to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Networks (BNs) are a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a BN in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.

Sensitivity of endoscopic ultrasound, multidetector computed tomography, and magnetic resonance cholangiopancreatography in the diagnosis of pancreas divisum: a tertiary center experience.

PubMed

Kushnir, Vladimir M; Wani, Sachin B; Fowler, Kathryn; Menias, Christine; Varma, Rakesh; Narra, Vamsi; Hovis, Christine; Murad, Faris M; Mullady, Daniel K; Jonnalagadda, Sreenivasa S; Early, Dayna S; Edmundowicz, Steven A; Azar, Riad R

2013-04-01

There are limited data comparing imaging modalities in the diagnosis of pancreas divisum. We aimed to: (1) evaluate the sensitivity of endoscopic ultrasound (EUS), magnetic resonance cholangiopancreatography (MRCP), and multidetector computed tomography (MDCT) for pancreas divisum; and (2) assess interobserver agreement (IOA) among expert radiologists for detecting pancreas divisum on MDCT and MRCP. For this retrospective cohort study, we identified 45 consecutive patients with pancreaticobiliary symptoms and pancreas divisum established by endoscopic retrograde pancreatography who underwent EUS and cross-sectional imaging. The control group was composed of patients without pancreas divisum who underwent endoscopic retrograde pancreatography and cross-sectional imaging. The sensitivity of EUS for pancreas divisum was 86.7%, significantly higher than the sensitivity reported in the medical records for MDCT (15.5%) or MRCP (60%) (P < 0.001 for each). On review by expert radiologists, the sensitivity of MDCT increased to 83.3% in cases where the pancreatic duct was visualized, with fair IOA (κ = 0.34). Expert review of MRCPs did not identify any additional cases of pancreas divisum; IOA was moderate (κ = 0.43). Endoscopic ultrasound is a sensitive test for diagnosing pancreas divisum and is superior to MDCT and MRCP. Review of MDCT studies by expert radiologists substantially raises its sensitivity for pancreas divisum.

A photon recycling approach to the denoising of ultra-low dose X-ray sequences.

PubMed

Hariharan, Sai Gokul; Strobel, Norbert; Kaethner, Christian; Kowarschik, Markus; Demirci, Stefanie; Albarqouni, Shadi; Fahrig, Rebecca; Navab, Nassir

2018-06-01

Clinical procedures that make use of fluoroscopy may expose patients as well as the clinical staff (throughout their career) to non-negligible doses of radiation. The potential consequences of such exposures fall under two categories, namely stochastic (mostly cancer) and deterministic risks (skin injury). According to the "as low as reasonably achievable" principle, the radiation dose can be lowered only if the necessary image quality can be maintained. Our work improves upon the existing patch-based denoising algorithms by utilizing a more sophisticated noise model to exploit non-local self-similarity better and this in turn improves the performance of low-rank approximation. The novelty of the proposed approach lies in its properly designed and parameterized noise model and the elimination of initial estimates. This reduces the computational cost significantly. The algorithm has been evaluated on 500 clinical images (7 patients, 20 sequences, 3 clinical sites), taken at ultra-low dose levels, i.e. 50% of the standard low dose level, during electrophysiology procedures. An average improvement in the contrast-to-noise ratio (CNR) by a factor of around 3.5 has been found. This is associated with an image quality achieved at around 12 (square of 3.5) times the ultra-low dose level. Qualitative evaluation by X-ray image quality experts suggests that the method produces denoised images that comply with the required image quality criteria. The results are consistent with the number of patches used, and they demonstrate that it is possible to use motion estimation techniques and "recycle" photons from previous frames to improve the image quality of the current frame. Our results are comparable in terms of CNR to Video Block Matching 3D-a state-of-the-art denoising method. But qualitative analysis by experts confirms that the denoised ultra-low dose X-ray images obtained using our method are more realistic with respect to appearance.

Fully automated, deep learning segmentation of oxygen-induced retinopathy images

PubMed Central

Xiao, Sa; Bucher, Felicitas; Wu, Yue; Rokem, Ariel; Lee, Cecilia S.; Marra, Kyle V.; Fallon, Regis; Diaz-Aguilar, Sophia; Aguilar, Edith; Friedlander, Martin; Lee, Aaron Y.

2017-01-01

Oxygen-induced retinopathy (OIR) is a widely used model to study ischemia-driven neovascularization (NV) in the retina and to serve in proof-of-concept studies in evaluating antiangiogenic drugs for ocular, as well as nonocular, diseases. The primary parameters that are analyzed in this mouse model include the percentage of retina with vaso-obliteration (VO) and NV areas. However, quantification of these two key variables comes with a great challenge due to the requirement of human experts to read the images. Human readers are costly, time-consuming, and subject to bias. Using recent advances in machine learning and computer vision, we trained deep learning neural networks using over a thousand segmentations to fully automate segmentation in OIR images. While determining the percentage area of VO, our algorithm achieved a similar range of correlation coefficients to that of expert inter-human correlation coefficients. In addition, our algorithm achieved a higher range of correlation coefficients compared with inter-expert correlation coefficients for quantification of the percentage area of neovascular tufts. In summary, we have created an open-source, fully automated pipeline for the quantification of key values of OIR images using deep learning neural networks. PMID:29263301

Forensic comparison and matching of fingerprints: using quantitative image measures for estimating error rates through understanding and predicting difficulty.

PubMed

Kellman, Philip J; Mnookin, Jennifer L; Erlikhman, Gennady; Garrigan, Patrick; Ghose, Tandra; Mettler, Everett; Charlton, David; Dror, Itiel E

2014-01-01

Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert performance and subjective assessment of difficulty in fingerprint comparisons.

An efficient method for automatic morphological abnormality detection from human sperm images.

PubMed

Ghasemian, Fatemeh; Mirroshandel, Seyed Abolghasem; Monji-Azad, Sara; Azarnia, Mahnaz; Zahiri, Ziba

2015-12-01

Sperm morphology analysis (SMA) is an important factor in the diagnosis of human male infertility. This study presents an automatic algorithm for sperm morphology analysis (to detect malformation) using images of human sperm cells. The SMA method was used to detect and analyze different parts of the human sperm. First of all, SMA removes the image noises and enhances the contrast of the image to a great extent. Then it recognizes the different parts of sperm (e.g., head, tail) and analyzes the size and shape of each part. Finally, the algorithm classifies each sperm as normal or abnormal. Malformations in the head, midpiece, and tail of a sperm, can be detected by the SMA method. In contrast to other similar methods, the SMA method can work with low resolution and non-stained images. Furthermore, an image collection created for the SMA, has also been described in this study. This benchmark consists of 1457 sperm images from 235 patients, and is known as human sperm morphology analysis dataset (HSMA-DS). The proposed algorithm was tested on HSMA-DS. The experimental results show the high ability of SMA to detect morphological deformities from sperm images. In this study, the SMA algorithm produced above 90% accuracy in sperm abnormality detection task. Another advantage of the proposed method is its low computation time (that is, less than 9s), as such, the expert can quickly decide to choose the analyzed sperm or select another one. Automatic and fast analysis of human sperm morphology can be useful during intracytoplasmic sperm injection for helping embryologists to select the best sperm in real time. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Teledermatologist expert skin advice: A unique model of care for managing skin disorders and adverse drug reactions in hepatitis C patients.

PubMed

Charlston, Samuel; Siller, Gregory

2018-03-23

To conduct an audit of teledermatologist expert skin advice, a store and forward tele-dermatological service, to determine its effectiveness and user satisfaction in managing cutaneous adverse drug reactions in patients with hepatitis C, and to demonstrate a unique collaborative model of care for patients receiving specialised drug therapy. A retrospective analysis of data on teledermatologist expert skin advice referrals from January 2014 to December 2015 was performed. The primary outcomes assessed included number of referrals, referral locations, diagnoses, response times, quality of clinical information provided and user satisfaction ratings. Altogether 43 consultations from 29 referring sites were received from Australian metropolitan and rural settings. Of the patients, 43 were diagnosed with an adverse drug reaction related to the use of either telaprevir or simeprevir. The average time taken for the dermatologist to reply electronically with a final diagnosis and management plan was 1 h 57 min. As many as 26% of referrals required additional photos to establish a diagnosis due to poor-quality images or insufficient detail. Altogether 18 clinicians completed the customer satisfaction survey, all of whom rated teledermatologist expert skin advice nine or above on a scale of one to 10. Teledermatologist expert skin advice was regarded by clinicians as a valuable patient care service. The platform is a novel modality that supports patients undergoing specialised treatments at risk of cutaneous adverse drug reaction. © 2018 The Australasian College of Dermatologists.

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

NASA Astrophysics Data System (ADS)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.

Point-of-care and point-of-procedure optical imaging technologies for primary care and global health

PubMed Central

Boppart, Stephen A.; Richards-Kortum, Rebecca

2015-01-01

Leveraging advances in consumer electronics and wireless telecommunications, low-cost, portable optical imaging devices have the potential to improve screening and detection of disease at the point of care in primary health care settings in both low- and high-resource countries. Similarly, real-time optical imaging technologies can improve diagnosis and treatment at the point of procedure by circumventing the need for biopsy and analysis by expert pathologists, who are scarce in developing countries. Although many optical imaging technologies have been translated from bench to bedside, industry support is needed to commercialize and broadly disseminate these from the patient level to the population level to transform the standard of care. This review provides an overview of promising optical imaging technologies, the infrastructure needed to integrate them into widespread clinical use, and the challenges that must be addressed to harness the potential of these technologies to improve health care systems around the world. PMID:25210062

Point-of-care and point-of-procedure optical imaging technologies for primary care and global health.

PubMed

Boppart, Stephen A; Richards-Kortum, Rebecca

2014-09-10

Leveraging advances in consumer electronics and wireless telecommunications, low-cost, portable optical imaging devices have the potential to improve screening and detection of disease at the point of care in primary health care settings in both low- and high-resource countries. Similarly, real-time optical imaging technologies can improve diagnosis and treatment at the point of procedure by circumventing the need for biopsy and analysis by expert pathologists, who are scarce in developing countries. Although many optical imaging technologies have been translated from bench to bedside, industry support is needed to commercialize and broadly disseminate these from the patient level to the population level to transform the standard of care. This review provides an overview of promising optical imaging technologies, the infrastructure needed to integrate them into widespread clinical use, and the challenges that must be addressed to harness the potential of these technologies to improve health care systems around the world. Copyright © 2014, American Association for the Advancement of Science.

An Automatic Image Processing System for Glaucoma Screening

PubMed Central

Alodhayb, Sami; Lakshminarayanan, Vasudevan

2017-01-01

Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma's population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while thresholding using Type-II fuzzy approach was developed for segmenting the cup. The results from the algorithms were verified using the manual markings of images from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by six ophthalmologists. The algorithm's accuracy for HCDR and VCDR combined was 74.2%. Only the accuracy of manual markings by one ophthalmologist was higher than the algorithm's accuracy. The algorithm's best agreement was with markings by ophthalmologist number 1 in 230 images (41.8%) of the total tested images. PMID:28947898

ACR Appropriateness Criteria Myelopathy.

PubMed

Roth, Christopher J; Angevine, Peter D; Aulino, Joseph M; Berger, Kevin L; Choudhri, Asim F; Fries, Ian Blair; Holly, Langston T; Kendi, Ayse Tuba Karaqulle; Kessler, Marcus M; Kirsch, Claudia F; Luttrull, Michael D; Mechtler, Laszlo L; O'Toole, John E; Sharma, Aseem; Shetty, Vilaas S; West, O Clark; Cornelius, Rebecca S; Bykowski, Julie

2016-01-01

Patients presenting with myelopathic symptoms may have a number of causative intradural and extradural etiologies, including disc degenerative diseases, spinal masses, infectious or inflammatory processes, vascular compromise, and vertebral fracture. Patients may present acutely or insidiously and may progress toward long-term paralysis if not treated promptly and effectively. Noncontrast CT is the most appropriate first examination in acute trauma cases to diagnose vertebral fracture as the cause of acute myelopathy. In most nontraumatic cases, MRI is the modality of choice to evaluate the location, severity, and causative etiology of spinal cord myelopathy, and predicts which patients may benefit from surgery. Myelopathy from spinal stenosis and spinal osteoarthritis is best confirmed without MRI intravenous contrast. Many other myelopathic conditions are more easily visualized after contrast administration. Imaging performed should be limited to the appropriate spinal levels, based on history, physical examination, and clinical judgment. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every three years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals, and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Automatic classification of DMSA scans using an artificial neural network

NASA Astrophysics Data System (ADS)

Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

2014-04-01

DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

Towards the VWO Annotation Service: a Success Story of the IMAGE RPI Expert Rating System

NASA Astrophysics Data System (ADS)

Reinisch, B. W.; Galkin, I. A.; Fung, S. F.; Benson, R. F.; Kozlov, A. V.; Khmyrov, G. M.; Garcia, L. N.

2010-12-01

Interpretation of Heliophysics wave data requires specialized knowledge of wave phenomena. Users of the virtual wave observatory (VWO) will greatly benefit from a data annotation service that will allow querying of data by phenomenon type, thus helping accomplish the VWO goal to make Heliophysics wave data searchable, understandable, and usable by the scientific community. Individual annotations can be sorted by phenomenon type and reduced into event lists (catalogs). However, in contrast to the event lists, annotation records allow a greater flexibility of collaborative management by more easily admitting operations of addition, revision, or deletion. They can therefore become the building blocks for an interactive Annotation Service with a suitable graphic user interface to the VWO middleware. The VWO Annotation Service vision is an interactive, collaborative sharing of domain expert knowledge with fellow scientists and students alike. An effective prototype of the VWO Annotation Service has been in operation at the University of Massachusetts Lowell since 2001. An expert rating system (ERS) was developed for annotating the IMAGE radio plasma imager (RPI) active sounding data containing 1.2 million plasmagrams. The RPI data analysts can use ERS to submit expert ratings of plasmagram features, such as presence of echo traces resulted from reflected RPI signals from distant plasma structures. Since its inception in 2001, the RPI ERS has accumulated 7351 expert plasmagram ratings in 16 phenomenon categories, together with free-text descriptions and other metadata. In addition to human expert ratings, the system holds 225,125 ratings submitted by the CORPRAL data prospecting software that employs a model of the human pre-attentive vision to select images potentially containing interesting features. The annotation records proved to be instrumental in a number of investigations where manual data exploration would have been prohibitively tedious and expensive. Especially useful are queries of the annotation database for successive plasmagrams containing echo traces. Several success stories of the RPI ERS using this capability will be discussed, particularly in terms of how they may be extended to develop the VWO Annotation Service.

The Desired Image of a Science Writer.

ERIC Educational Resources Information Center

Yore, Larry D.; Hand, Brian M.; Prain, Vaughan

This study attempted to establish a desired image of an expert science writer based on a synthesis of writing theory, models, and research literature on academic writing in science and other disciplines and to contrast this desired image with an actual prototypical image of scientists as writers of science. The synthesis was used to develop a…

ATM experiment S-056 image processing requirements definition

NASA Technical Reports Server (NTRS)

1972-01-01

A plan is presented for satisfying the image data processing needs of the S-056 Apollo Telescope Mount experiment. The report is based on information gathered from related technical publications, consultation with numerous image processing experts, and on the experience that was in working on related image processing tasks over a two-year period.

Deep image mining for diabetic retinopathy screening.

PubMed

Quellec, Gwenolé; Charrière, Katia; Boudi, Yassine; Cochener, Béatrice; Lamard, Mathieu

2017-07-01

Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predictions. In other words, a ConvNet trained for image-level classification can be used to detect lesions as well. A generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps. The proposed solution is applied to diabetic retinopathy (DR) screening in a dataset of almost 90,000 fundus photographs from the 2015 Kaggle Diabetic Retinopathy competition and a private dataset of almost 110,000 photographs (e-ophtha). For the task of detecting referable DR, very good detection performance was achieved: A z =0.954 in Kaggle's dataset and A z =0.949 in e-ophtha. Performance was also evaluated at the image level and at the lesion level in the DiaretDB1 dataset, where four types of lesions are manually segmented: microaneurysms, hemorrhages, exudates and cotton-wool spots. For the task of detecting images containing these four lesion types, the proposed detector, which was trained to detect referable DR, outperforms recent algorithms trained to detect those lesions specifically, with pixel-level supervision. At the lesion level, the proposed detector outperforms heatmap generation algorithms for ConvNets. This detector is part of the Messidor® system for mobile eye pathology screening. Because it does not rely on expert knowledge or manual segmentation for detecting relevant patterns, the proposed solution is a promising image mining tool, which has the potential to discover new biomarkers in images. Copyright © 2017 Elsevier B.V. All rights reserved.

Semi-automatic segmentation of nonviable cardiac tissue using cine and delayed enhancement magnetic resonance images

NASA Astrophysics Data System (ADS)

O'Donnell, Thomas P.; Xu, Ning; Setser, Randolph M.; White, Richard D.

2003-05-01

Post myocardial infarction, the identification and assessment of non-viable (necrotic) tissues is necessary for effective development of intervention strategies and treatment plans. Delayed Enhancement Magnetic Resonance (DEMR) imaging is a technique whereby non-viable cardiac tissue appears with increased signal intensity. Radiologists typically acquire these images in conjunction with other functional modalities (e.g., MR Cine), and use domain knowledge and experience to isolate the non-viable tissues. In this paper, we present a technique for automatically segmenting these tissues given the delineation of myocardial borders in the DEMR and in the End-systolic and End-diastolic MR Cine images. Briefly, we obtain a set of segmentations furnished by an expert and employ an artificial intelligence technique, Support Vector Machines (SVMs), to "learn" the segmentations based on features culled from the images. Using those features we then allow the SVM to predict the segmentations the expert would provide on previously unseen images.

Design of a Web-tool for diagnostic clinical trials handling medical imaging research.

PubMed

Baltasar Sánchez, Alicia; González-Sistal, Angel

2011-04-01

New clinical studies in medicine are based on patients and controls using different imaging diagnostic modalities. Medical information systems are not designed for clinical trials employing clinical imaging. Although commercial software and communication systems focus on storage of image data, they are not suitable for storage and mining of new types of quantitative data. We sought to design a Web-tool to support diagnostic clinical trials involving different experts and hospitals or research centres. The image analysis of this project is based on skeletal X-ray imaging. It involves a computerised image method using quantitative analysis of regions of interest in healthy bone and skeletal metastases. The database is implemented with ASP.NET 3.5 and C# technologies for our Web-based application. For data storage, we chose MySQL v.5.0, one of the most popular open source databases. User logins were necessary, and access to patient data was logged for auditing. For security, all data transmissions were carried over encrypted connections. This Web-tool is available to users scattered at different locations; it allows an efficient organisation and storage of data (case report form) and images and allows each user to know precisely what his task is. The advantages of our Web-tool are as follows: (1) sustainability is guaranteed; (2) network locations for collection of data are secured; (3) all clinical information is stored together with the original images and the results derived from processed images and statistical analysis that enable us to perform retrospective studies; (4) changes are easily incorporated because of the modular architecture; and (5) assessment of trial data collected at different sites is centralised to reduce statistical variance.

Quantification of localized vertebral deformities using a sparse wavelet-based shape model.

PubMed

Zewail, R; Elsafi, A; Durdle, N

2008-01-01

Medical experts often examine hundreds of spine x-ray images to determine existence of various pathologies. Common pathologies of interest are anterior osteophites, disc space narrowing, and wedging. By careful inspection of the outline shapes of the vertebral bodies, experts are able to identify and assess vertebral abnormalities with respect to the pathology under investigation. In this paper, we present a novel method for quantification of vertebral deformation using a sparse shape model. Using wavelets and Independent component analysis (ICA), we construct a sparse shape model that benefits from the approximation power of wavelets and the capability of ICA to capture higher order statistics in wavelet space. The new model is able to capture localized pathology-related shape deformations, hence it allows for quantification of vertebral shape variations. We investigate the capability of the model to predict localized pathology related deformations. Next, using support-vector machines, we demonstrate the diagnostic capabilities of the method through the discrimination of anterior osteophites in lumbar vertebrae. Experiments were conducted using a set of 150 contours from digital x-ray images of lumbar spine. Each vertebra is labeled as normal or abnormal. Results reported in this work focus on anterior osteophites as the pathology of interest.

Analysis of high-speed digital phonoscopy pediatric images

NASA Astrophysics Data System (ADS)

Unnikrishnan, Harikrishnan; Donohue, Kevin D.; Patel, Rita R.

2012-02-01

The quantitative characterization of vocal fold (VF) motion can greatly enhance the diagnosis and treatment of speech pathologies. The recent availability of high-speed systems has created new opportunities to understand VF dynamics. This paper presents quantitative methods for analyzing VF dynamics with high-speed digital phonoscopy, with a focus on expected VF changes during childhood. A robust method for automatic VF edge tracking during phonation is introduced and evaluated against 4 expert human observers. Results from 100 test frames show a subpixel difference between the VF edges selected by algorithm and expert observers. Waveforms created from the VF edge displacement are used to created motion features with limited sensitivity to variations of camera resolution on the imaging plane. New features are introduced based on acceleration ratios of critical points over each phonation cycle, which have the potential for studying issues related to impact stress. A novel denoising and hybrid interpolation/extrapolation scheme is also introduced to reduce the impact of quantization errors and large sampling intervals relative to the phonation cycle. Features extracted from groups of 4 adults and 5 children show large differences for features related to asymmetry between the right and left fold and consistent differences for impact acceleration ratio.

Automated segmentation and reconstruction of patient-specific cardiac anatomy and pathology from in vivo MRI*

NASA Astrophysics Data System (ADS)

Ringenberg, Jordan; Deo, Makarand; Devabhaktuni, Vijay; Filgueiras-Rama, David; Pizarro, Gonzalo; Ibañez, Borja; Berenfeld, Omer; Boyers, Pamela; Gold, Jeffrey

2012-12-01

This paper presents an automated method to segment left ventricle (LV) tissues from functional and delayed-enhancement (DE) cardiac magnetic resonance imaging (MRI) scans using a sequential multi-step approach. First, a region of interest (ROI) is computed to create a subvolume around the LV using morphological operations and image arithmetic. From the subvolume, the myocardial contours are automatically delineated using difference of Gaussians (DoG) filters and GSV snakes. These contours are used as a mask to identify pathological tissues, such as fibrosis or scar, within the DE-MRI. The presented automated technique is able to accurately delineate the myocardium and identify the pathological tissue in patient sets. The results were validated by two expert cardiologists, and in one set the automated results are quantitatively and qualitatively compared with expert manual delineation. Furthermore, the method is patient-specific, performed on an entire patient MRI series. Thus, in addition to providing a quick analysis of individual MRI scans, the fully automated segmentation method is used for effectively tagging regions in order to reconstruct computerized patient-specific 3D cardiac models. These models can then be used in electrophysiological studies and surgical strategy planning.

To image analysis in computed tomography

NASA Astrophysics Data System (ADS)

Chukalina, Marina; Nikolaev, Dmitry; Ingacheva, Anastasia; Buzmakov, Alexey; Yakimchuk, Ivan; Asadchikov, Victor

2017-03-01

The presence of errors in tomographic image may lead to misdiagnosis when computed tomography (CT) is used in medicine, or the wrong decision about parameters of technological processes when CT is used in the industrial applications. Two main reasons produce these errors. First, the errors occur on the step corresponding to the measurement, e.g. incorrect calibration and estimation of geometric parameters of the set-up. The second reason is the nature of the tomography reconstruction step. At the stage a mathematical model to calculate the projection data is created. Applied optimization and regularization methods along with their numerical implementations of the method chosen have their own specific errors. Nowadays, a lot of research teams try to analyze these errors and construct the relations between error sources. In this paper, we do not analyze the nature of the final error, but present a new approach for the calculation of its distribution in the reconstructed volume. We hope that the visualization of the error distribution will allow experts to clarify the medical report impression or expert summary given by them after analyzing of CT results. To illustrate the efficiency of the proposed approach we present both the simulation and real data processing results.

On the analysis of local and global features for hyperemia grading

NASA Astrophysics Data System (ADS)

Sánchez, L.; Barreira, N.; Sánchez, N.; Mosquera, A.; Pena-Verdeal, H.; Yebra-Pimentel, E.

2017-03-01

In optometry, hyperemia is the accumulation of blood flow in the conjunctival tissue. Dry eye syndrome or allergic conjunctivitis are two of its main causes. Its main symptom is the presence of a red hue in the eye that optometrists evaluate according to a scale in a subjective manner. In this paper, we propose an automatic approach to the problem of hyperemia grading in the bulbar conjunctiva. We compute several image features on images of the patients' eyes, analyse the relations among them by using feature selection techniques and transform the feature vector of each image to the value in the adequate range by means of machine learning techniques. We analyse different areas of the conjunctiva to evaluate their importance for the diagnosis. Our results show that it is possible to mimic the experts' behaviour through the proposed approach.

Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays

PubMed Central

Lawson, Jonathan; Robinson-Vyas, Rupesh J; McQuillan, Janette P; Paterson, Andy; Christie, Sarah; Kidza-Griffiths, Matthew; McDuffus, Leigh-Anne; Moutasim, Karwan A; Shaw, Emily C; Kiltie, Anne E; Howat, William J; Hanby, Andrew M; Thomas, Gareth J; Smittenaar, Peter

2017-01-01

Background: Academic pathology suffers from an acute and growing lack of workforce resource. This especially impacts on translational elements of clinical trials, which can require detailed analysis of thousands of tissue samples. We tested whether crowdsourcing – enlisting help from the public – is a sufficiently accurate method to score such samples. Methods: We developed a novel online interface to train and test lay participants on cancer detection and immunohistochemistry scoring in tissue microarrays. Lay participants initially performed cancer detection on lung cancer images stained for CD8, and we measured how extending a basic tutorial by annotated example images and feedback-based training affected cancer detection accuracy. We then applied this tutorial to additional cancer types and immunohistochemistry markers – bladder/ki67, lung/EGFR, and oesophageal/CD8 – to establish accuracy compared with experts. Using this optimised tutorial, we then tested lay participants' accuracy on immunohistochemistry scoring of lung/EGFR and bladder/p53 samples. Results: We observed that for cancer detection, annotated example images and feedback-based training both improved accuracy compared with a basic tutorial only. Using this optimised tutorial, we demonstrate highly accurate (>0.90 area under curve) detection of cancer in samples stained with nuclear, cytoplasmic and membrane cell markers. We also observed high Spearman correlations between lay participants and experts for immunohistochemistry scoring (0.91 (0.78, 0.96) and 0.97 (0.91, 0.99) for lung/EGFR and bladder/p53 samples, respectively). Conclusions: These results establish crowdsourcing as a promising method to screen large data sets for biomarkers in cancer pathology research across a range of cancers and immunohistochemical stains. PMID:27959886

Evaluation of image quality of digital photo documentation of female genital injuries following sexual assault.

PubMed

Ernst, E J; Speck, Patricia M; Fitzpatrick, Joyce J

2011-12-01

With the patient's consent, physical injuries sustained in a sexual assault are evaluated and treated by the sexual assault nurse examiner (SANE) and documented on preprinted traumagrams and with photographs. Digital imaging is now available to the SANE for documentation of sexual assault injuries, but studies of the image quality of forensic digital imaging of female genital injuries after sexual assault were not found in the literature. The Photo Documentation Image Quality Scoring System (PDIQSS) was developed to rate the image quality of digital photo documentation of female genital injuries after sexual assault. Three expert observers performed evaluations on 30 separate images at two points in time. An image quality score, the sum of eight integral technical and anatomical attributes on the PDIQSS, was obtained for each image. Individual image quality ratings, defined by rating image quality for each of the data, were also determined. The results demonstrated a high level of image quality and agreement when measured in all dimensions. For the SANE in clinical practice, the results of this study indicate that a high degree of agreement exists between expert observers when using the PDIQSS to rate image quality of individual digital photographs of female genital injuries after sexual assault. © 2011 International Association of Forensic Nurses.

Quantitative assessment of multiple sclerosis lesion load using CAD and expert input

NASA Astrophysics Data System (ADS)

Gertych, Arkadiusz; Wong, Alexis; Sangnil, Alan; Liu, Brent J.

2008-03-01

Multiple sclerosis (MS) is a frequently encountered neurological disease with a progressive but variable course affecting the central nervous system. Outline-based lesion quantification in the assessment of lesion load (LL) performed on magnetic resonance (MR) images is clinically useful and provides information about the development and change reflecting overall disease burden. Methods of LL assessment that rely on human input are tedious, have higher intra- and inter-observer variability and are more time-consuming than computerized automatic (CAD) techniques. At present it seems that methods based on human lesion identification preceded by non-interactive outlining by CAD are the best LL quantification strategies. We have developed a CAD that automatically quantifies MS lesions, displays 3-D lesion map and appends radiological findings to original images according to current DICOM standard. CAD is also capable to display and track changes and make comparison between patient's separate MRI studies to determine disease progression. The findings are exported to a separate imaging tool for review and final approval by expert. Capturing and standardized archiving of manual contours is also implemented. Similarity coefficients calculated from quantities of LL in collected exams show a good correlation of CAD-derived results vs. those incorporated as expert's reading. Combining the CAD approach with an expert interaction may impact to the diagnostic work-up of MS patients because of improved reproducibility in LL assessment and reduced time for single MR or comparative exams reading. Inclusion of CAD-generated outlines as DICOM-compliant overlays into the image data can serve as a better reference in MS progression tracking.

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).

A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging.

PubMed

Bagci, Ulas; Foster, Brent; Miller-Jaster, Kirsten; Luna, Brian; Dey, Bappaditya; Bishai, William R; Jonsson, Colleen B; Jain, Sanjay; Mollura, Daniel J

2013-07-23

Infectious diseases are the second leading cause of death worldwide. In order to better understand and treat them, an accurate evaluation using multi-modal imaging techniques for anatomical and functional characterizations is needed. For non-invasive imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), there have been many engineering improvements that have significantly enhanced the resolution and contrast of the images, but there are still insufficient computational algorithms available for researchers to use when accurately quantifying imaging data from anatomical structures and functional biological processes. Since the development of such tools may potentially translate basic research into the clinic, this study focuses on the development of a quantitative and qualitative image analysis platform that provides a computational radiology perspective for pulmonary infections in small animal models. Specifically, we designed (a) a fast and robust automated and semi-automated image analysis platform and a quantification tool that can facilitate accurate diagnostic measurements of pulmonary lesions as well as volumetric measurements of anatomical structures, and incorporated (b) an image registration pipeline to our proposed framework for volumetric comparison of serial scans. This is an important investigational tool for small animal infectious disease models that can help advance researchers' understanding of infectious diseases. We tested the utility of our proposed methodology by using sequentially acquired CT and PET images of rabbit, ferret, and mouse models with respiratory infections of Mycobacterium tuberculosis (TB), H1N1 flu virus, and an aerosolized respiratory pathogen (necrotic TB) for a total of 92, 44, and 24 scans for the respective studies with half of the scans from CT and the other half from PET. Institutional Administrative Panel on Laboratory Animal Care approvals were obtained prior to conducting this research. First, the proposed computational framework registered PET and CT images to provide spatial correspondences between images. Second, the lungs from the CT scans were segmented using an interactive region growing (IRG) segmentation algorithm with mathematical morphology operations to avoid false positive (FP) uptake in PET images. Finally, we segmented significant radiotracer uptake from the PET images in lung regions determined from CT and computed metabolic volumes of the significant uptake. All segmentation processes were compared with expert radiologists' delineations (ground truths). Metabolic and gross volume of lesions were automatically computed with the segmentation processes using PET and CT images, and percentage changes in those volumes over time were calculated. (Continued on next page)(Continued from previous page) Standardized uptake value (SUV) analysis from PET images was conducted as a complementary quantitative metric for disease severity assessment. Thus, severity and extent of pulmonary lesions were examined through both PET and CT images using the aforementioned quantification metrics outputted from the proposed framework. Each animal study was evaluated within the same subject class, and all steps of the proposed methodology were evaluated separately. We quantified the accuracy of the proposed algorithm with respect to the state-of-the-art segmentation algorithms. For evaluation of the segmentation results, dice similarity coefficient (DSC) as an overlap measure and Haussdorf distance as a shape dissimilarity measure were used. Significant correlations regarding the estimated lesion volumes were obtained both in CT and PET images with respect to the ground truths (R2=0.8922,p<0.01 and R2=0.8664,p<0.01, respectively). The segmentation accuracy (DSC (%)) was 93.4±4.5% for normal lung CT scans and 86.0±7.1% for pathological lung CT scans. Experiments showed excellent agreements (all above 85%) with expert evaluations for both structural and functional imaging modalities. Apart from quantitative analysis of each animal, we also qualitatively showed how metabolic volumes were changing over time by examining serial PET/CT scans. Evaluation of the registration processes was based on precisely defined anatomical landmark points by expert clinicians. An average of 2.66, 3.93, and 2.52 mm errors was found in rabbit, ferret, and mouse data (all within the resolution limits), respectively. Quantitative results obtained from the proposed methodology were visually related to the progress and severity of the pulmonary infections as verified by the participating radiologists. Moreover, we demonstrated that lesions due to the infections were metabolically active and appeared multi-focal in nature, and we observed similar patterns in the CT images as well. Consolidation and ground glass opacity were the main abnormal imaging patterns and consistently appeared in all CT images. We also found that the gross and metabolic lesion volume percentage follow the same trend as the SUV-based evaluation in the longitudinal analysis. We explored the feasibility of using PET and CT imaging modalities in three distinct small animal models for two diverse pulmonary infections. We concluded from the clinical findings, derived from the proposed computational pipeline, that PET-CT imaging is an invaluable hybrid modality for tracking pulmonary infections longitudinally in small animals and has great potential to become routinely used in clinics. Our proposed methodology showed that automated computed-aided lesion detection and quantification of pulmonary infections in small animal models are efficient and accurate as compared to the clinical standard of manual and semi-automated approaches. Automated analysis of images in pre-clinical applications can increase the efficiency and quality of pre-clinical findings that ultimately inform downstream experimental design in human clinical studies; this innovation will allow researchers and clinicians to more effectively allocate study resources with respect to research demands without compromising accuracy.

Telehealth solutions to enable global collaboration in rheumatic heart disease screening.

PubMed

Lopes, Eduardo Lv; Beaton, Andrea Z; Nascimento, Bruno R; Tompsett, Alison; Dos Santos, Julia Pa; Perlman, Lindsay; Diamantino, Adriana C; Oliveira, Kaciane Kb; Oliveira, Cassio M; Nunes, Maria do Carmo P; Bonisson, Leonardo; Ribeiro, Antônio Lp; Sable, Craig

2018-02-01

Background The global burden of rheumatic heart disease is nearly 33 million people. Telemedicine, using cloud-server technology, provides an ideal solution for sharing images performed by non-physicians with cardiologists who are experts in rheumatic heart disease. Objective We describe our experience in using telemedicine to support a large rheumatic heart disease outreach screening programme in the Brazilian state of Minas Gerais. Methods The Programa de Rastreamento da Valvopatia Reumática (PROVAR) is a prospective cross-sectional study aimed at gathering epidemiological data on the burden of rheumatic heart disease in Minas Gerais and testing of a non-expert, telemedicine-supported model of outreach rheumatic heart disease screening. The primary goal is to enable expert support of remote rheumatic heart disease outreach through cloud-based sharing of echocardiographic images between Minas Gerais and Washington. Secondary goals include (a) developing and sharing online training modules for non-physicians in echocardiography performance and interpretation and (b) utilising a secure web-based system to share clinical and research data. Results PROVAR included 4615 studies that were performed by non-experts at 21 schools and shared via cloud-telemedicine technology. Latent rheumatic heart disease was found in 251 subjects (4.2% of subjects: 3.7% borderline and 0.5% definite disease). Of the studies, 50% were preformed on full functional echocardiography machines and transmitted via Digital Imaging and Communications in Medicine (DICOM) and 50% were performed on handheld echocardiography machines and transferred via a secure Dropbox connection. The average time between study performance date and interpretation was 10 days. There was 100% success in initial image transfer. Less than 1% of studies performed by non-experts could not be interpreted. Discussion A sustainable, low-cost telehealth model, using task-shifting with non-medical personal in low and middle income countries can improve access to echocardiography for rheumatic heart disease.

How Expert Pilots Think Cognitive Processes in Expert Decision Making

DTIC Science & Technology

1993-02-01

Management (CRM) This document is available to the public Advanced Qualification Program (AQP) through the National Technical Information Cognitive Task Analysis (CTA...8217 Selecting realistic EDM scenarios with critical events and performing a cognitive task analysis of novice vs. expert decision making for these events...scenarios with critical events and performing a cognitive task analysis of novice vs. expert decision making for these events is a basic requirement for

Automatic SAR/optical cross-matching for GCP monograph generation

NASA Astrophysics Data System (ADS)

Nutricato, Raffaele; Morea, Alberto; Nitti, Davide Oscar; La Mantia, Claudio; Agrimano, Luigi; Samarelli, Sergio; Chiaradia, Maria Teresa

2016-10-01

Ground Control Points (GCP), automatically extracted from Synthetic Aperture Radar (SAR) images through 3D stereo analysis, can be effectively exploited for an automatic orthorectification of optical imagery if they can be robustly located in the basic optical images. The present study outlines a SAR/Optical cross-matching procedure that allows a robust alignment of radar and optical images, and consequently to derive automatically the corresponding sub-pixel position of the GCPs in the optical image in input, expressed as fractional pixel/line image coordinates. The cross-matching in performed in two subsequent steps, in order to gradually gather a better precision. The first step is based on the Mutual Information (MI) maximization between optical and SAR chips while the last one uses the Normalized Cross-Correlation as similarity metric. This work outlines the designed algorithmic solution and discusses the results derived over the urban area of Pisa (Italy), where more than ten COSMO-SkyMed Enhanced Spotlight stereo images with different beams and passes are available. The experimental analysis involves different satellite images, in order to evaluate the performances of the algorithm w.r.t. the optical spatial resolution. An assessment of the performances of the algorithm has been carried out, and errors are computed by measuring the distance between the GCP pixel/line position in the optical image, automatically estimated by the tool, and the "true" position of the GCP, visually identified by an expert user in the optical images.

Segmentation and analysis of mouse pituitary cells with graphic user interface (GUI)

NASA Astrophysics Data System (ADS)

González, Erika; Medina, Lucía.; Hautefeuille, Mathieu; Fiordelisio, Tatiana

2018-02-01

In this work we present a method to perform pituitary cell segmentation in image stacks acquired by fluorescence microscopy from pituitary slice preparations. Although there exist many procedures developed to achieve cell segmentation tasks, they are generally based on the edge detection and require high resolution images. However in the biological preparations that we worked on, the cells are not well defined as experts identify their intracellular calcium activity due to fluorescence intensity changes in different regions over time. This intensity changes were associated with time series over regions, and because they present a particular behavior they were used into a classification procedure in order to perform cell segmentation. Two logistic regression classifiers were implemented for the time series classification task using as features the area under the curve and skewness in the first classifier and skewness and kurtosis in the second classifier. Once we have found both decision boundaries in two different feature spaces by training using 120 time series, the decision boundaries were tested over 12 image stacks through a python graphical user interface (GUI), generating binary images where white pixels correspond to cells and the black ones to background. Results show that area-skewness classifier reduces the time an expert dedicates in locating cells by up to 75% in some stacks versus a 92% for the kurtosis-skewness classifier, this evaluated on the number of regions the method found. Due to the promising results, we expect that this method will be improved adding more relevant features to the classifier.

Enhancing reproducibility of ultrasonic measurements by new users

NASA Astrophysics Data System (ADS)

Pramanik, Manojit; Gupta, Madhumita; Krishnan, Kajoli Banerjee

2013-03-01

Perception of operator influences ultrasound image acquisition and processing. Lower costs are attracting new users to medical ultrasound. Anticipating an increase in this trend, we conducted a study to quantify the variability in ultrasonic measurements made by novice users and identify methods to reduce it. We designed a protocol with four presets and trained four new users to scan and manually measure the head circumference of a fetal phantom with an ultrasound scanner. In the first phase, the users followed this protocol in seven distinct sessions. They then received feedback on the quality of the scans from an expert. In the second phase, two of the users repeated the entire protocol aided by visual cues provided to them during scanning. We performed off-line measurements on all the images using a fully automated algorithm capable of measuring the head circumference from fetal phantom images. The ground truth (198.1±1.6 mm) was based on sixteen scans and measurements made by an expert. Our analysis shows that: (1) the inter-observer variability of manual measurements was 5.5 mm, whereas the inter-observer variability of automated measurements was only 0.6 mm in the first phase (2) consistency of image appearance improved and mean manual measurements was 4-5 mm closer to the ground truth in the second phase (3) automated measurements were more precise, accurate and less sensitive to different presets compared to manual measurements in both phases. Our results show that visual aids and automation can bring more reproducibility to ultrasonic measurements made by new users.

Automated liver elasticity calculation for 3D MRE

NASA Astrophysics Data System (ADS)

Dzyubak, Bogdan; Glaser, Kevin J.; Manduca, Armando; Ehman, Richard L.

2017-03-01

Magnetic Resonance Elastography (MRE) is a phase-contrast MRI technique which calculates quantitative stiffness images, called elastograms, by imaging the propagation of acoustic waves in tissues. It is used clinically to diagnose liver fibrosis. Automated analysis of MRE is difficult as the corresponding MRI magnitude images (which contain anatomical information) are affected by intensity inhomogeneity, motion artifact, and poor tissue- and edge-contrast. Additionally, areas with low wave amplitude must be excluded. An automated algorithm has already been successfully developed and validated for clinical 2D MRE. 3D MRE acquires substantially more data and, due to accelerated acquisition, has exacerbated image artifacts. Also, the current 3D MRE processing does not yield a confidence map to indicate MRE wave quality and guide ROI selection, as is the case in 2D. In this study, extension of the 2D automated method, with a simple wave-amplitude metric, was developed and validated against an expert reader in a set of 57 patient exams with both 2D and 3D MRE. The stiffness discrepancy with the expert for 3D MRE was -0.8% +/- 9.45% and was better than discrepancy with the same reader for 2D MRE (-3.2% +/- 10.43%), and better than the inter-reader discrepancy observed in previous studies. There were no automated processing failures in this dataset. Thus, the automated liver elasticity calculation (ALEC) algorithm is able to calculate stiffness from 3D MRE data with minimal bias and good precision, while enabling stiffness measurements to be fully reproducible and to be easily performed on the large 3D MRE datasets.

Applications of artificial intelligence to space station: General purpose intelligent sensor interface

NASA Technical Reports Server (NTRS)

Mckee, James W.

1988-01-01

This final report describes the accomplishments of the General Purpose Intelligent Sensor Interface task of the Applications of Artificial Intelligence to Space Station grant for the period from October 1, 1987 through September 30, 1988. Portions of the First Biannual Report not revised will not be included but only referenced. The goal is to develop an intelligent sensor system that will simplify the design and development of expert systems using sensors of the physical phenomena as a source of data. This research will concentrate on the integration of image processing sensors and voice processing sensors with a computer designed for expert system development. The result of this research will be the design and documentation of a system in which the user will not need to be an expert in such areas as image processing algorithms, local area networks, image processor hardware selection or interfacing, television camera selection, voice recognition hardware selection, or analog signal processing. The user will be able to access data from video or voice sensors through standard LISP statements without any need to know about the sensor hardware or software.

Consensus Recommendations of the Multiple Sclerosis Study Group and Portuguese Neuroradiological Society for the Use of the Magnetic Resonance Imaging in Multiple Sclerosis in Clinical Practice: Part 1.

PubMed

Abreu, Pedro; Pedrosa, Rui; Sá, Maria José; Cerqueira, João; Sousa, Lívia; Da Silva, Ana Martins; Pinheiro, Joaquim; De Sá, João; Batista, Sónia; Simões, Rita Moiron; Pereira, Daniela Jardim; Vilela, Pedro; Vale, José

2018-05-30

Magnetic resonance imaging is established as a recognizable tool in the diagnosis and monitoring of multiple sclerosis patients. In the present, among multiple sclerosis centers, there are different magnetic resonance imaging sequences and protocols used to study multiple sclerosis that may hamper the optimal use of magnetic resonance imaging in multiple sclerosis. In this context, the Group of Studies of Multiple Sclerosis and the Portuguese Society of Neuroradiology, after a joint discussion, appointed a committee of experts to create recommendations adapted to the national reality on the use of magnetic resonance imaging in multiple sclerosis. The purpose of this document is to publish the first Portuguese consensus recommendations on the use of magnetic resonance imaging in multiple sclerosis in clinical practice. The Group of Studies of Multiple Sclerosis and the Portuguese Society of Neuroradiology, after discussion of the topic in national meetings and after a working group meeting held in Figueira da Foz on May 2017, have appointed a committee of experts that have developed by consensus several standard protocols on the use of magnetic resonance imaging in the diagnosis and follow-up of multiple sclerosis. The document obtained was based on the best scientific evidence and expert opinion. Subsequently, the majority of Portuguese multiple sclerosis consultants and departments of neuroradiology scrutinized and reviewed the consensus paper; comments and suggestions were considered. Technical magnetic resonance imaging protocols regarding diagnostic, monitoring and the recommended information to be included in the magnetic resonance imaging report will be published in a separate paper. We provide some practical guidelines to promote standardized strategies to be applied in the clinical practice setting of Portuguese healthcare professionals regarding the use of magnetic resonance imaging in multiple sclerosis. We hope that these first Portuguese magnetic resonance imaging guidelines, based in the best available clinical evidence and practices, will serve to optimize multiple sclerosis management and improve multiple sclerosis patient care across Portugal.

Quantitative investigation of inappropriate regression model construction and the importance of medical statistics experts in observational medical research: a cross-sectional study.

PubMed

Nojima, Masanori; Tokunaga, Mutsumi; Nagamura, Fumitaka

2018-05-05

To investigate under what circumstances inappropriate use of 'multivariate analysis' is likely to occur and to identify the population that needs more support with medical statistics. The frequency of inappropriate regression model construction in multivariate analysis and related factors were investigated in observational medical research publications. The inappropriate algorithm of using only variables that were significant in univariate analysis was estimated to occur at 6.4% (95% CI 4.8% to 8.5%). This was observed in 1.1% of the publications with a medical statistics expert (hereinafter 'expert') as the first author, 3.5% if an expert was included as coauthor and in 12.2% if experts were not involved. In the publications where the number of cases was 50 or less and the study did not include experts, inappropriate algorithm usage was observed with a high proportion of 20.2%. The OR of the involvement of experts for this outcome was 0.28 (95% CI 0.15 to 0.53). A further, nation-level, analysis showed that the involvement of experts and the implementation of unfavourable multivariate analysis are associated at the nation-level analysis (R=-0.652). Based on the results of this study, the benefit of participation of medical statistics experts is obvious. Experts should be involved for proper confounding adjustment and interpretation of statistical models. © 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.

SENSITIVITY OF ENDOSCOPIC ULTRASOUND, MULTIDETECTOR COMPUTER TOMOGRAPHY AND MAGNETIC RESONANCE CHOLANGIOPANCREATOGRAPHY IN THE DIAGNOSIS OF PANCREAS DIVISUM: A TERTIARY CENTER EXPERIENCE

PubMed Central

Kushnir, Vladimir M.; Wani, Sachin B.; Fowler, Kathryn; Menias, Christine; Varma, Rakesh; Narra, Vamsi; Hovis, Christine; Murad, Faris; Mullady, Daniel; Jonnalagadda, Sreenivasa S.; Early, Dayna S.; Edmundowicz, Steven A.; Azar, Riad R.

2014-01-01

OBJECTIVES There are limited data comparing imaging modalities in the diagnosis of pancreas divisum. We aimed to: 1. Evaluate the sensitivity of endoscopic ultrasound (EUS), magnetic resonance cholangiopancreatography (MRCP) and multi-detector computed tomography (MDCT) for pancreas divisum. 2. Assess interobserver agreement (IOA) among expert radiologists for detecting pancreas divisum on MDCT and MRCP. METHODS For this retrospective cohort study, we identified 45 consecutive patients with pancreaticobiliary symptoms and pancreas divisum established by endoscopic retrograde pancreatography (ERP) who underwent EUS and cross-sectional imaging. The control group was composed of patients without pancreas divisum who underwent ERP and cross-sectional imaging. RESULTS The sensitivity of EUS for pancreas divisum was 86.7%, significantly higher than sensitivity reported in the medical records for MDCT (15.5%) or MRCP (60%) [p<0.001 for each]. On review by expert radiologists the sensitivity of MDCT increased to 83.3% in cases where the pancreatic duct was visualized, with fair IOA (қ=0.34). Expert review of MRCPs did not identify any additional cases of pancreas divisum; IOA was moderate (қ=0.43). CONCLUSIONS EUS is a sensitive test for diagnosing pancreas divisum and is superior to MDCT and MRCP. Review of MDCT studies by expert radiologists substantially raises its sensitivity for pancreas divisum. PMID:23211370

At the Core of the Apple Store: Images of Next Generation Learning

ERIC Educational Resources Information Center

Washor, Elliot; Mojkowski, Charles; Newsom, Loran

2009-01-01

The physical, psychological, cultural, social, and organizational elements of a learning environment are as important as the learning opportunities themselves. The Apple Store blends retail and school into a new type of learning environment that lets the customer learn anything, at any time, at any level, from experts, expert practitioners, and…

Evaluation of expert criteria for preoperative magnetic resonance imaging of newly diagnosed breast cancer.

PubMed

Behrendt, Carolyn E; Tumyan, Lusine; Gonser, Laura; Shaw, Sara L; Vora, Lalit; Paz, I Benjamin; Ellenhorn, Joshua D I; Yim, John H

2014-08-01

Despite 2 randomized trials reporting no reduction in operations or local recurrence at 1 year, preoperative magnetic resonance imaging (MRI) is increasingly used in diagnostic workup of breast cancer. We evaluated 5 utilization criteria recently proposed by experts. Of women (n = 340) newly diagnosed with unilateral breast cancer who underwent bilateral MRI, most (69.4%) met at least 1 criterion before MRI: mammographic density (44.4%), under consideration for partial breast irradiation (PBI) (19.7%), genetic-familial risk (12.9%), invasive lobular carcinoma (11.8%), and multifocal/multicentric disease (10.6%). MRI detected occult malignant lesion or extension of index lesion in 21.2% of index, 3.3% of contralateral, breasts. No expert criterion was associated with MRI-detected malignant lesion, which associated instead with pre-MRI plan of lumpectomy without PBI (48.2% of subjects): Odds Ratio 3.05, 95% CI 1.57-5.91 (p adjusted for multiple hypothesis testing = 0.007, adjusted for index-vs-contralateral breast and covariates). The expert guidelines were not confirmed by clinical evidence. Copyright © 2014 Elsevier Ltd. All rights reserved.

Picture Pile: A citizen-powered tool for rapid post-disaster damage assessments

NASA Astrophysics Data System (ADS)

Danylo, Olha; Sturn, Tobias; Giovando, Cristiano; Moorthy, Inian; Fritz, Steffen; See, Linda; Kapur, Ravi; Girardot, Blake; Ajmar, Andrea; Giulio Tonolo, Fabio; Reinicke, Tobias; Mathieu, Pierre Philippe; Duerauer, Martina

2017-04-01

According to the World Bank's global risk analysis, around 34% of the total world's population lives in areas of high mortality risk from two or more natural hazards. Therefore, timely and innovative methods to rapidly assess damage to subsequently aid relief and recovery efforts are critical. In this field of post-disaster damage assessment, several crowdsourcing-based technological tools that engage citizens in carrying out various tasks, including data collection, satellite image analysis and online interactive mapping, have recently been developed. One such tool is Picture Pile, a cross-platform application that is designed as a generic and flexible tool for ingesting satellite imagery for rapid classification. As part of the ESA's Crowd4Sat initiative led by Imperative Space, this study develops a workflow for employing Picture Pile for rapid post-disaster damage assessment. We outline how satellite image interpretation tasks within Picture Pile can be crowdsourced using the example of Hurricane Matthew, which affected large regions of Haiti in September 2016. The application provides simple microtasks, where the user is presented with satellite images and is asked a simple yes/no question. A "before" disaster satellite image is displayed next to an "after" disaster image and the user is asked to assess whether there is any visible, detectable damage. The question is formulated precisely to focus the user's attention on a particular aspect of the damage. The user-interface of Picture Pile is also built for users to rapidly classify the images by swiping to indicate their answer, thereby efficiently completing the microstask. The proposed approach will not only help to increase citizen awareness of natural disasters, but also provide them with a unique opportunity to contribute directly to relief efforts. Furthermore, to gain confidence in the crowdsourced results, quality assurance methods were integrated during the testing phase of the application using image classifications from experts. The application has a built-in real-time quality assurance system to provide volunteers with feedback when their answer does not agree with that of an expert. Picture Pile is intended to supplement existing approaches for post-disaster damage assessment and can be used by different networks of volunteers (e.g., the Humanitarian OpenStreetMap Team) to assess damage and create up-to-date maps of response to disaster events.

Breadth in Design Problem Scoping: Using Insights from Experts to Investigate Student Processes. Research Brief

ERIC Educational Resources Information Center

Morozov, Andrew; Kilgore, Deborah; Atman, Cynthia

2007-01-01

In this study, the authors used two methods for analyzing expert data: verbal protocol analysis (VPA) and narrative analysis. VPA has been effectively used to describe the design processes employed by engineering students, expert designers, and expert-novice comparative research. VPA involves asking participants to "think aloud" while…

Using collective expert judgements to evaluate quality measures of mass spectrometry images.

PubMed

Palmer, Andrew; Ovchinnikova, Ekaterina; Thuné, Mikael; Lavigne, Régis; Guével, Blandine; Dyatlov, Andrey; Vitek, Olga; Pineau, Charles; Borén, Mats; Alexandrov, Theodore

2015-06-15

Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures. We propose a novel approach for evaluating potential measures by creating a gold-standard set using collective expert judgements upon which we evaluated image-based measures. To produce a gold standard, we engaged 80 IMS experts, each to rate the relative quality between 52 pairs of ion images from MALDI-TOF IMS datasets of rat brain coronal sections. Experts' optional feedback on their expertise, the task and the survey showed that (i) they had diverse backgrounds and sufficient expertise, (ii) the task was properly understood, and (iii) the survey was comprehensible. A moderate inter-rater agreement was achieved with Krippendorff's alpha of 0.5. A gold-standard set of 634 pairs of images with accompanying ratings was constructed and showed a high agreement of 0.85. Eight families of potential measures with a range of parameters and statistical descriptors, giving 143 in total, were evaluated. Both signal-to-noise and spatial chaos-based measures performed highly with a correlation of 0.7 to 0.9 with the gold standard ratings. Moreover, we showed that a composite measure with the linear coefficients (trained on the gold standard with regularized least squares optimization and lasso) showed a strong linear correlation of 0.94 and an accuracy of 0.98 in predicting which image in a pair was of higher quality. The anonymized data collected from the survey and the Matlab source code for data processing can be found at: https://github.com/alexandrovteam/IMS_quality. © The Author 2015. Published by Oxford University Press.

Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method

PubMed Central

Veta, Mitko; van Diest, Paul J.; Jiwa, Mehdi; Al-Janabi, Shaimaa; Pluim, Josien P. W.

2016-01-01

Background Tumor proliferation speed, most commonly assessed by counting of mitotic figures in histological slide preparations, is an important biomarker for breast cancer. Although mitosis counting is routinely performed by pathologists, it is a tedious and subjective task with poor reproducibility, particularly among non-experts. Inter- and intraobserver reproducibility of mitosis counting can be improved when a strict protocol is defined and followed. Previous studies have examined only the agreement in terms of the mitotic count or the mitotic activity score. Studies of the observer agreement at the level of individual objects, which can provide more insight into the procedure, have not been performed thus far. Methods The development of automatic mitosis detection methods has received large interest in recent years. Automatic image analysis is viewed as a solution for the problem of subjectivity of mitosis counting by pathologists. In this paper we describe the results from an interobserver agreement study between three human observers and an automatic method, and make two unique contributions. For the first time, we present an analysis of the object-level interobserver agreement on mitosis counting. Furthermore, we train an automatic mitosis detection method that is robust with respect to staining appearance variability and compare it with the performance of expert observers on an “external” dataset, i.e. on histopathology images that originate from pathology labs other than the pathology lab that provided the training data for the automatic method. Results The object-level interobserver study revealed that pathologists often do not agree on individual objects, even if this is not reflected in the mitotic count. The disagreement is larger for objects from smaller size, which suggests that adding a size constraint in the mitosis counting protocol can improve reproducibility. The automatic mitosis detection method can perform mitosis counting in an unbiased way, with substantial agreement with human experts. PMID:27529701

Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method.

PubMed

Veta, Mitko; van Diest, Paul J; Jiwa, Mehdi; Al-Janabi, Shaimaa; Pluim, Josien P W

2016-01-01

Tumor proliferation speed, most commonly assessed by counting of mitotic figures in histological slide preparations, is an important biomarker for breast cancer. Although mitosis counting is routinely performed by pathologists, it is a tedious and subjective task with poor reproducibility, particularly among non-experts. Inter- and intraobserver reproducibility of mitosis counting can be improved when a strict protocol is defined and followed. Previous studies have examined only the agreement in terms of the mitotic count or the mitotic activity score. Studies of the observer agreement at the level of individual objects, which can provide more insight into the procedure, have not been performed thus far. The development of automatic mitosis detection methods has received large interest in recent years. Automatic image analysis is viewed as a solution for the problem of subjectivity of mitosis counting by pathologists. In this paper we describe the results from an interobserver agreement study between three human observers and an automatic method, and make two unique contributions. For the first time, we present an analysis of the object-level interobserver agreement on mitosis counting. Furthermore, we train an automatic mitosis detection method that is robust with respect to staining appearance variability and compare it with the performance of expert observers on an "external" dataset, i.e. on histopathology images that originate from pathology labs other than the pathology lab that provided the training data for the automatic method. The object-level interobserver study revealed that pathologists often do not agree on individual objects, even if this is not reflected in the mitotic count. The disagreement is larger for objects from smaller size, which suggests that adding a size constraint in the mitosis counting protocol can improve reproducibility. The automatic mitosis detection method can perform mitosis counting in an unbiased way, with substantial agreement with human experts.

Factors that act as facilitators and barriers to nurse leaders’ participation in health policy development

PubMed Central

2014-01-01

Background Health policies impact on nursing profession and health care. Nurses' involvement in health policy development ensures that health care is safe, of a high quality, accessible and affordable. Numerous factors influence nurse leaders' ability to be politically active in influencing health policy development. These factors can be facilitators or barriers to their participation. There is scant research evidence from Eastern African region that draws attention to this topic. This paper reports part of the larger study. The objectives reported in this paper were those aimed to: build consensus on factors that act as facilitators and barriers to nurse leaders' participation in health policy development in Kenya, Uganda and Tanzania. Methods A Delphi survey was applied which included: expert panelists, iterative rounds, statistical analysis, and consensus building. The expert panelists were purposively selected and included national nurse leaders in leadership positions in East Africa. Data collection was done, in three iterative rounds, and utilized a questionnaire with open and closed ended questions. 78 expert panelists were invited to participate in the study; the response rate was 47% of these 64.8% participated in the second round and of those 100% participated in the third round. Data analysis was done by examining the data for the most commonly occurring categories for the open ended questions and descriptive statistics for structured questions. Results The findings of the study indicate that both facilitators and barriers exist. The former include: being involved in health policy development, having knowledge and skills, enhancing the image of nursing and enabling structures and processes. The latter include: lack of involvement, negative image of nursing and structures and processes which exclude them. Conclusion There is a window of opportunity to enhance national nurse leaders' participation in health policy development. Nurse leaders have a key role in mentoring, supporting and developing future nurse policy makers. PMID:25053921

Representing and Inferring Visual Perceptual Skills in Dermatological Image Understanding

ERIC Educational Resources Information Center

Li, Rui

2013-01-01

Experts have a remarkable capability of locating, perceptually organizing, identifying, and categorizing objects in images specific to their domains of expertise. Eliciting and representing their visual strategies and some aspects of domain knowledge will benefit a wide range of studies and applications. For example, image understanding may be…

Are all types of expertise created equal? Car experts use different spatial frequency scales for subordinate categorization of cars and faces.

PubMed

Harel, Assaf; Bentin, Shlomo

2013-01-01

A much-debated question in object recognition is whether expertise for faces and expertise for non-face objects utilize common perceptual information. We investigated this issue by assessing the diagnostic information required for different types of expertise. Specifically, we asked whether face categorization and expert car categorization at the subordinate level relies on the same spatial frequency (SF) scales. Fifteen car experts and fifteen novices performed a category verification task with spatially filtered images of faces, cars, and airplanes. Images were categorized based on their basic (e.g. "car") and subordinate level (e.g. "Japanese car") identity. The effect of expertise was not evident when objects were categorized at the basic level. However, when the car experts categorized faces and cars at the subordinate level, the two types of expertise required different kinds of SF information. Subordinate categorization of faces relied on low SFs more than on high SFs, whereas subordinate expert car categorization relied on high SFs more than on low SFs. These findings suggest that expertise in the recognition of objects and faces do not utilize the same type of information. Rather, different types of expertise require different types of diagnostic visual information.

Are All Types of Expertise Created Equal? Car Experts Use Different Spatial Frequency Scales for Subordinate Categorization of Cars and Faces

PubMed Central

Harel, Assaf; Bentin, Shlomo

2013-01-01

A much-debated question in object recognition is whether expertise for faces and expertise for non-face objects utilize common perceptual information. We investigated this issue by assessing the diagnostic information required for different types of expertise. Specifically, we asked whether face categorization and expert car categorization at the subordinate level relies on the same spatial frequency (SF) scales. Fifteen car experts and fifteen novices performed a category verification task with spatially filtered images of faces, cars, and airplanes. Images were categorized based on their basic (e.g. “car”) and subordinate level (e.g. “Japanese car”) identity. The effect of expertise was not evident when objects were categorized at the basic level. However, when the car experts categorized faces and cars at the subordinate level, the two types of expertise required different kinds of SF information. Subordinate categorization of faces relied on low SFs more than on high SFs, whereas subordinate expert car categorization relied on high SFs more than on low SFs. These findings suggest that expertise in the recognition of objects and faces do not utilize the same type of information. Rather, different types of expertise require different types of diagnostic visual information. PMID:23826188

Deep Learning for Classification of Colorectal Polyps on Whole-slide Images

PubMed Central

Korbar, Bruno; Olofson, Andrea M.; Miraflor, Allen P.; Nicka, Catherine M.; Suriawinata, Matthew A.; Torresani, Lorenzo; Suriawinata, Arief A.; Hassanpour, Saeed

2017-01-01

Context: Histopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability. Aims: We built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis. Setting and Design: Our method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Subjects and Methods: Our method covers five common types of polyps (i.e., hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous) that are included in the US Multisociety Task Force guidelines for colorectal cancer risk assessment and surveillance. We developed multiple deep-learning approaches by leveraging a dataset of 2074 crop images, which were annotated by multiple domain expert pathologists as reference standards. Statistical Analysis: We evaluated our method on an independent test set of 239 whole-slide images and measured standard machine-learning evaluation metrics of accuracy, precision, recall, and F1 score and their 95% confidence intervals. Results: Our evaluation shows that our method with residual network architecture achieves the best performance for classification of colorectal polyps on whole-slide images (overall accuracy: 93.0%, 95% confidence interval: 89.0%–95.9%). Conclusions: Our method can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations. PMID:28828201

Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation

NASA Astrophysics Data System (ADS)

Irshad, Mehreen; Muhammad, Nazeer; Sharif, Muhammad; Yasmeen, Mussarat

2018-04-01

Conventionally, cardiac MR image analysis is done manually. Automatic examination for analyzing images can replace the monotonous tasks of massive amounts of data to analyze the global and regional functions of the cardiac left ventricle (LV). This task is performed using MR images to calculate the analytic cardiac parameter like end-systolic volume, end-diastolic volume, ejection fraction, and myocardial mass, respectively. These analytic parameters depend upon genuine delineation of epicardial, endocardial, papillary muscle, and trabeculations contours. In this paper, we propose an automatic segmentation method using the sum of absolute differences technique to localize the left ventricle. Blind morphological operations are proposed to segment and detect the LV contours of the epicardium and endocardium, automatically. We test the benchmark Sunny Brook dataset for evaluation of the proposed work. Contours of epicardium and endocardium are compared quantitatively to determine contour's accuracy and observe high matching values. Similarity or overlapping of an automatic examination to the given ground truth analysis by an expert are observed with high accuracy as with an index value of 91.30% . The proposed method for automatic segmentation gives better performance relative to existing techniques in terms of accuracy.

The effects of AVIRIS atmospheric calibration methodology on identification and quantitative mapping of surface mineralogy, Drum Mountains, Utah

NASA Technical Reports Server (NTRS)

Kruse, Fred A.; Dwyer, John L.

1993-01-01

The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) measures reflected light in 224 contiguous spectra bands in the 0.4 to 2.45 micron region of the electromagnetic spectrum. Numerous studies have used these data for mineralogic identification and mapping based on the presence of diagnostic spectral features. Quantitative mapping requires conversion of the AVIRIS data to physical units (usually reflectance) so that analysis results can be compared and validated with field and laboratory measurements. This study evaluated two different AVIRIS calibration techniques to ground reflectance: an empirically-based method and an atmospheric model based method to determine their effects on quantitative scientific analyses. Expert system analysis and linear spectral unmixing were applied to both calibrated data sets to determine the effect of the calibration on the mineral identification and quantitative mapping results. Comparison of the image-map results and image reflectance spectra indicate that the model-based calibrated data can be used with automated mapping techniques to produce accurate maps showing the spatial distribution and abundance of surface mineralogy. This has positive implications for future operational mapping using AVIRIS or similar imaging spectrometer data sets without requiring a priori knowledge.

Quantitative Imaging Biomarkers: A Review of Statistical Methods for Technical Performance Assessment

PubMed Central

2017-01-01

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers (QIBs) to measure changes in these features. Critical to the performance of a QIB in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method and metrics used to assess a QIB for clinical use. It is therefore, difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America (RSNA) and the Quantitative Imaging Biomarker Alliance (QIBA) with technical, radiological and statistical experts developed a set of technical performance analysis methods, metrics and study designs that provide terminology, metrics and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of QIB performance studies so that results from multiple studies can be compared, contrasted or combined. PMID:24919831

Three-dimensional segmentation of the tumor mass in computed tomographic images of neuroblastoma

NASA Astrophysics Data System (ADS)

Deglint, Hanford J.; Rangayyan, Rangaraj M.; Boag, Graham S.

2004-05-01

Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, fibrosis, and normal tissue are often intermixed. Rather than attempt to separate these tissue types into distinct regions, we propose to explore methods to delineate the normal structures expected in abdominal CT images, remove them from further consideration, and examine the remaining parts of the images for the tumor mass. We explore the use of fuzzy connectivity for this purpose. Expert knowledge provided by the radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are also incorporated in the segmentation algorithm. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to chemotherapy and in the planning of delayed surgery for resection of the tumor. The performance of the algorithm is evaluated using cases acquired from the Alberta Children's Hospital.

Expert cognitive control and individual differences associated with frontal and parietal white matter microstructure

PubMed Central

Roberts, R.E.; Anderson, E. J.; Husain, M.

2011-01-01

Although many functional imaging studies have reported frontal activity associated with ‘cognitive control’ tasks, little is understood about factors underlying individual differences in performance. Here we compared the behaviour and brain structure of healthy controls with fighter pilots, an expert group trained to make precision choices at speed in the presence of conflicting cues. Two different behavioural paradigms – Eriksen Flanker and Change of plan tasks – were used to assess the influence of distractors and the ability to update ongoing action plans. Fighter pilots demonstrated superior cognitive control as indexed by accuracy and post-conflict adaptation on the flanker task, but also showed increased sensitivity to irrelevant, distracting choices. By contrast, when pilots were examined on their ability to inhibit a current action plan in favour of an alternative response, their performance was no better than the control group. Diffusion weighted imaging revealed differences in white matter radial diffusivity between pilots and controls not only in the right dorsomedial frontal region but also in the right parietal lobe. Moreover, analysis of individual differences in reaction time costs for conflict trials on the flanker task demonstrated significant correlations with radial diffusivity at these locations, but in different directions. Post-conflict adaptation effects, however, were confined to the dorsomedial frontal locus. The findings demonstrate that in humans expert cognitive control may surprisingly be mediated by enhanced response gain to both relevant and irrelevant stimuli, and is accompanied by structural alterations in the white matter of the frontal and parietal lobe. PMID:21159976

Evaluating Spatiotemporal Image Correlation Technology as a Tool for Training Nonexpert Sonographers to Perform Examinations of the Fetal Heart.

PubMed

Avnet, Hagai; Mazaaki, Eyal; Shen, Ori; Cohen, Sarah; Yagel, Simcha

2016-01-01

We aimed to evaluate the use of spatiotemporal image correlation (STIC) as a tool for training nonexpert examiners to perform screening examinations of the fetal heart by acquiring and examining STIC volumes according to a standardized questionnaire based on the 5 transverse planes of the fetal heart. We conducted a prospective study at 2 tertiary care centers. Two sonographers without formal training in fetal echocardiography received theoretical instruction on the 5 fetal echocardiographic transverse planes, as well as STIC technology. Only women with conditions allowing 4-dimensional STIC volume acquisitions (grayscale and Doppler) were included in the study. Acquired volumes were evaluated offline according to a standardized protocol that required the trainee to mark 30 specified structures on 5 required axial planes. Volumes were then reviewed by an expert examiner for quality of acquisition and correct identification of specified structures. Ninety-six of 112 pregnant women examined entered the study. Patients had singleton pregnancies between 20 and 32 weeks' gestation. After an initial learning curve of 20 examinations, trainees succeeded in identifying 97% to 98% of structures, with a highly significant degree of agreement with the expert's analysis (P < .001). A median of 2 STIC volumes for each examination was necessary for maximal structure identification. Acquisition quality scores were high (8.6-8.7 of a maximal score of 10) and were found to correlate with identification rates (P = .017). After an initial learning curve and under expert guidance, STIC is an excellent tool for trainees to master extended screening examinations of the fetal heart.

Automated podosome identification and characterization in fluorescence microscopy images.

PubMed

Meddens, Marjolein B M; Rieger, Bernd; Figdor, Carl G; Cambi, Alessandra; van den Dries, Koen

2013-02-01

Podosomes are cellular adhesion structures involved in matrix degradation and invasion that comprise an actin core and a ring of cytoskeletal adaptor proteins. They are most often identified by staining with phalloidin, which binds F-actin and therefore visualizes the core. However, not only podosomes, but also many other cytoskeletal structures contain actin, which makes podosome segmentation by automated image processing difficult. Here, we have developed a quantitative image analysis algorithm that is optimized to identify podosome cores within a typical sample stained with phalloidin. By sequential local and global thresholding, our analysis identifies up to 76% of podosome cores excluding other F-actin-based structures. Based on the overlap in podosome identifications and quantification of podosome numbers, our algorithm performs equally well compared to three experts. Using our algorithm we show effects of actin polymerization and myosin II inhibition on the actin intensity in both podosome core and associated actin network. Furthermore, by expanding the core segmentations, we reveal a previously unappreciated differential distribution of cytoskeletal adaptor proteins within the podosome ring. These applications illustrate that our algorithm is a valuable tool for rapid and accurate large-scale analysis of podosomes to increase our understanding of these characteristic adhesion structures.

Computerized Liquid Crystal Phase Identification by Neural Networks Analysis of Polarizing Microscopy Textures

NASA Astrophysics Data System (ADS)

Karaszi, Zoltan; Konya, Andrew; Dragan, Feodor; Jakli, Antal; CPIP/LCI; CS Dept. of Kent State University Collaboration

Polarizing optical microscopy (POM) is traditionally the best-established method of studying liquid crystals, and using POM started already with Otto Lehman in 1890. An expert, who is familiar with the science of optics of anisotropic materials and typical textures of liquid crystals, can identify phases with relatively large confidence. However, for unambiguous identification usually other expensive and time-consuming experiments are needed. Replacement of the subjective and qualitative human eye-based liquid crystal texture analysis with quantitative computerized image analysis technique started only recently and were used to enhance the detection of smooth phase transitions, determine order parameter and birefringence of specific liquid crystal phases. We investigate if the computer can recognize and name the phase where the texture was taken. To judge the potential of reliable image recognition based on this procedure, we used 871 images of liquid crystal textures belonging to five main categories: Nematic, Smectic A, Smectic C, Cholesteric and Crystal, and used a Neural Network Clustering Technique included in the data mining software package in Java ``WEKA''. A neural network trained on a set of 827 LC textures classified the remaining 44 textures with 80% accuracy.

Virtual surgical planning in endoscopic skull base surgery.

PubMed

Haerle, Stephan K; Daly, Michael J; Chan, Harley H L; Vescan, Allan; Kucharczyk, Walter; Irish, Jonathan C

2013-12-01

Skull base surgery (SBS) involves operative tasks in close proximity to critical structures in a complex three-dimensional (3D) anatomy. The aim was to investigate the value of virtual planning (VP) based on preoperative magnetic resonance imaging (MRI) for surgical planning in SBS and to compare the effects of virtual planning with 3D contours between the expert and the surgeon in training. Retrospective analysis. Twelve patients with manually segmented anatomical structures based on preoperative MRI were evaluated by eight surgeons in a randomized order using a validated National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire. Multivariate analysis revealed significant reduction of workload when using VP (P<.0001) compared to standard planning. Further, it showed that the experience level of the surgeon had a significant effect on the NASA-TLX differences (P<.05). Additional subanalysis did not reveal any significant findings regarding which type of surgeon benefits the most (P>.05). Preoperative anatomical segmentation with virtual surgical planning using contours in endoscopic SBS significantly reduces the workload for the expert and the surgeon in training. Copyright © 2013 The American Laryngological, Rhinological and Otological Society, Inc.

Expectations of medical specialists about image-based teleconsultation - A qualitative study on acute burns in South Africa.

PubMed

Blom, Lisa; Laflamme, Lucie; Mölsted Alvesson, Helle

2018-01-01

Image-based teleconsultation between medical experts and healthcare staff at remote emergency centres can improve the diagnosis of conditions which are challenging to assess. One such condition is burns. Knowledge is scarce regarding how medical experts perceive the influence of such teleconsultation on their roles and relations to colleagues at point of care. In this qualitative study, semi-structured interviews were conducted with 15 medical experts to explore their expectations of a newly developed App for burns diagnostics and care prior to its implementation. Purposive sampling included male and female physicians at different stages of their career, employed at different referral hospitals and all potential future tele-experts in remote teleconsultation using the App. Positioning theory was used to analyse the data. The experts are already facing changes in their diagnostic practices due to the informal use of open access applications like WhatsApp. Additional changes are expected when the new App is launched. Four positions of medical experts were identified in situations of diagnostic advice, two related to patient flow-clinical specialist and gatekeeper-and two to point of care staff-educator and mentor. The experts move flexibly between the positions during diagnostic practices with remote colleagues. A new position in relation to previous research on medical roles-the mentor-came to light in this setting. The App is expected to have an important educational impact, streamline the diagnostic process, improve both triage and referrals and be a more secure option for remote diagnosis compared to current practices. Verbal communication is however expected to remain important for certain situations, in particular those related to the mentor position. The quality and security of referrals are expected to be improved through the App but the medical experts see less potential for conveying moral support via the App during remote consultations. Experts' reflections on remote consultations highlight the embedded social and cultural dimensions of implementing new technology.

A randomized clinical study comparing the plaque inhibition effect of a SnF2/SHMP dentifrice (blend-a-med EXPERT GUMS PROTECTION) and a chlorhexidine digluconate dentifrice (Lacalut Aktiv).

PubMed

Bellamy, P G; Khera, N; Day, T N; Mussett, A J; Barker, M L

2009-01-01

To compare the plaque inhibition benefits of a control 0.454% stannous fluoride/sodium hexametaphosphate/sodium fluoride dentifrice (SnF2/SHMP with 1450 ppm F) to a chlorhexidine digluconate (0.05%), aluminum lactate (0.8%), and aluminum fluoride (AlF3/Chx with 1400 ppm F) dentifrice. Twenty-nine subjects were randomized to a two-period, two-treatment, double-blind crossover sequence using blend-a-med EXPERT GUMS PROTECTION toothpaste (SnF2/SHMP) and Lacalut Aktiv toothpaste (AlF3/Chx). Each treatment was used along with a standard manual toothbrush (Oral-B P35 Indicator) for 17 days. Digital plaque image analysis (DPIA) was used at the end of each period for three consecutive days to evaluate plaque levels; a) overnight (A.M. pre-brush); b) following 40 seconds of brushing with the test product (A.M. post-brush); and c) mid-afternoon (P.M.). Images were analyzed using an objective computer algorithm to calculate the total area of visible plaque. A four-day washout period was instituted for the crossover phase. Twenty-seven subjects completed the study. The SnF2/SHMP dentifrice provided a statistically significant lower level of plaque area coverage compared to the AlF3/Chx dentifrice at all time points. For the SnF2/SHMP dentifrice, plaque coverage was 19.4% lower (p = 0.0043) at the A.M. pre-brush, 25.6% lower (p = 0.0014) at the A.M. post-brush, and 19.8% lower (p = 0.0057) at the P.M. measure relative to the AlF3/Chx dentifrice. The blend-a-med EXPERT GUMS PROTECTION toothpaste inhibits plaque regrowth, both overnight and during the day, to a significantly greater degree than Lacalut Aktiv. Additionally, immediately after brushing with blend-a-med EXPERT GUMS PROTECTION, subjects had significantly less plaque than after brushing with Lacalut Aktiv.

Breast histopathology image segmentation using spatio-colour-texture based graph partition method.

PubMed

Belsare, A D; Mushrif, M M; Pangarkar, M A; Meshram, N

2016-06-01

This paper proposes a novel integrated spatio-colour-texture based graph partitioning method for segmentation of nuclear arrangement in tubules with a lumen or in solid islands without a lumen from digitized Hematoxylin-Eosin stained breast histology images, in order to automate the process of histology breast image analysis to assist the pathologists. We propose a new similarity based super pixel generation method and integrate it with texton representation to form spatio-colour-texture map of Breast Histology Image. Then a new weighted distance based similarity measure is used for generation of graph and final segmentation using normalized cuts method is obtained. The extensive experiments carried shows that the proposed algorithm can segment nuclear arrangement in normal as well as malignant duct in breast histology tissue image. For evaluation of the proposed method the ground-truth image database of 100 malignant and nonmalignant breast histology images is created with the help of two expert pathologists and the quantitative evaluation of proposed breast histology image segmentation has been performed. It shows that the proposed method outperforms over other methods. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

Remote Sensing Image Analysis Without Expert Knowledge - A Web-Based Classification Tool On Top of Taverna Workflow Management System

NASA Astrophysics Data System (ADS)

Selsam, Peter; Schwartze, Christian

2016-10-01

Providing software solutions via internet has been known for quite some time and is now an increasing trend marketed as "software as a service". A lot of business units accept the new methods and streamlined IT strategies by offering web-based infrastructures for external software usage - but geospatial applications featuring very specialized services or functionalities on demand are still rare. Originally applied in desktop environments, the ILMSimage tool for remote sensing image analysis and classification was modified in its communicating structures and enabled for running on a high-power server and benefiting from Tavema software. On top, a GIS-like and web-based user interface guides the user through the different steps in ILMSimage. ILMSimage combines object oriented image segmentation with pattern recognition features. Basic image elements form a construction set to model for large image objects with diverse and complex appearance. There is no need for the user to set up detailed object definitions. Training is done by delineating one or more typical examples (templates) of the desired object using a simple vector polygon. The template can be large and does not need to be homogeneous. The template is completely independent from the segmentation. The object definition is done completely by the software.

Determination of Flaw Type and Location Using an Expert Module in Ultrasonic Nondestructive Testing for Weld Inspection

NASA Astrophysics Data System (ADS)

Shahriari, D.; Zolfaghari, A.; Masoumi, F.

2011-01-01

Nondestructive evaluation is explained as nondestructive testing, nondestructive inspection, and nondestructive examination. It is a desire to determine some characteristic of the object or to determine whether the object contains irregularities, discontinuities, or flaws. Ultrasound based inspection techniques are used extensively throughout industry for detection of flaws in engineering materials. The range and variety of imperfections encountered is large, and critical assessment of location, size, orientation and type is often difficult. In addition, increasing quality requirements of new standards and codes of practice relating to fitness for purpose are placing higher demands on operators. Applying of an expert knowledge-based analysis in ultrasonic examination is a powerful tool that can help assure safety, quality, and reliability; increase productivity; decrease liability; and save money. In this research, an expert module system is coupled with ultrasonic examination (A-Scan Procedure) to determine and evaluate type and location of flaws that embedded during welding parts. The processing module of this expert system is implemented based on EN standard to classify welding defects, acceptance condition and measuring of their location via echo static pattern and image processing. The designed module introduces new system that can automate evaluating of the results of A-scan method according to EN standard. It can simultaneously recognize the number and type of defects, and determine flaw position during each scan.

Use of artificial intelligence in analytical systems for the clinical laboratory

PubMed Central

Truchaud, Alain; Ozawa, Kyoichi; Pardue, Harry; Schnipelsky, Paul

1995-01-01

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI), both as expert systems and as neural networks. This paper considers the role of software in system operation, control and automation, and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of the paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. It is concluded that AI constitutes a collective form of intellectual propery, and that there is a need for better documentation, evaluation and regulation of the systems already being used in clinical laboratories. PMID:18924784

Teledermatological monitoring of leg ulcers in cooperation with home care nurses.

PubMed

Binder, Barbara; Hofmann-Wellenhof, Rainer; Salmhofer, Wolfgang; Okcu, Aslihan; Kerl, Helmut; Soyer, H Peter

2007-12-01

To examine the feasibility and acceptance of teledermatology for wound management for patients with leg ulcers by home care nurses and evaluate the reduction of costs and the acceptance of teledermatology by patients and home care nurses. Case series of telemonitored patients with leg ulcers including cost-effectiveness analysis. Home monitoring by home care nurses. Sixteen patients with 45 leg ulcers of different origin were included. After an initial outpatient visit when the leg ulcers were assessed and classified, teledermatological follow-up was done by home care nurses. Relevant clinical information and 1 to 4 digital images of the wound and surrounding skin were transmitted weekly via a secure Web site to an expert at the wound care center, who assessed the wound and made therapeutic recommendations. Of the 707 images transmitted for teleconsultation, in 644 (89%) the quality of the images was excellent or sufficient and the experts were confident in giving therapeutic recommendations. Of the 45 ulcers, 32 (71%) decreased in size and 14 (31%) healed completely, whereas 10 of the 45 ulcers (22%) increased slightly in size despite the teledermatological monitoring. In 3 ulcers (7%), no measurement was possible owing to the overly large size of the ulcers. The acceptance of telemedicine was very good by most patients. Of 15 home care nurses working in the district, 7 were very satisfied with teledermatological monitoring of wound care. There was a reduction of 46% in transportation costs for the insurance companies as well as for the patients owing to a significant decrease in the number of visits to general physicians or the wound care center. The acceptance of teledermatological monitoring of wound care was very high by patients, home care nurses, and wound experts. Decreased health care costs by reducing the number of visits to wound care centers or specialist physicians and improvement in quality of life for patients with leg ulcers using telemedicine seems possible. Teledermatology offers great potential for long-term wound care.

Resting-State Functional Magnetic Resonance Imaging for Language Preoperative Planning

PubMed Central

Branco, Paulo; Seixas, Daniela; Deprez, Sabine; Kovacs, Silvia; Peeters, Ronald; Castro, São L.; Sunaert, Stefan

2016-01-01

Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in resting-state protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using resting-state fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-state networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting-state language maps, particularly within the language regions of interest. Furthermore, resting-state language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that resting-state protocols may be suitable to map language networks in a quick and clinically efficient way. PMID:26869899

Neighborhood resolved fiber orientation distributions (NRFOD) in automatic labeling of white matter fiber pathways.

PubMed

Ugurlu, Devran; Firat, Zeynep; Türe, Uğur; Unal, Gozde

2018-05-01

Accurate digital representation of major white matter bundles in the brain is an important goal in neuroscience image computing since the representations can be used for surgical planning, intra-patient longitudinal analysis and inter-subject population connectivity studies. Reconstructing desired fiber bundles generally involves manual selection of regions of interest by an expert, which is subject to user bias and fatigue, hence an automation is desirable. To that end, we first present a novel anatomical representation based on Neighborhood Resolved Fiber Orientation Distributions (NRFOD) along the fibers. The resolved fiber orientations are obtained by generalized q-sampling imaging (GQI) and a subsequent diffusion decomposition method. A fiber-to-fiber distance measure between the proposed fiber representations is then used in a density-based clustering framework to select the clusters corresponding to the major pathways of interest. In addition, neuroanatomical priors are utilized to constrain the set of candidate fibers before density-based clustering. The proposed fiber clustering approach is exemplified on automation of the reconstruction of the major fiber pathways in the brainstem: corticospinal tract (CST); medial lemniscus (ML); middle cerebellar peduncle (MCP); inferior cerebellar peduncle (ICP); superior cerebellar peduncle (SCP). Experimental results on Human Connectome Project (HCP)'s publicly available "WU-Minn 500 Subjects + MEG2 dataset" and expert evaluations demonstrate the potential of the proposed fiber clustering method in brainstem white matter structure analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Informative-frame filtering in endoscopy videos

NASA Astrophysics Data System (ADS)

An, Yong Hwan; Hwang, Sae; Oh, JungHwan; Lee, JeongKyu; Tavanapong, Wallapak; de Groen, Piet C.; Wong, Johnny

2005-04-01

Advances in video technology are being incorporated into today"s healthcare practice. For example, colonoscopy is an important screening tool for colorectal cancer. Colonoscopy allows for the inspection of the entire colon and provides the ability to perform a number of therapeutic operations during a single procedure. During a colonoscopic procedure, a tiny video camera at the tip of the endoscope generates a video signal of the internal mucosa of the colon. The video data are displayed on a monitor for real-time analysis by the endoscopist. Other endoscopic procedures include upper gastrointestinal endoscopy, enteroscopy, bronchoscopy, cystoscopy, and laparoscopy. However, a significant number of out-of-focus frames are included in this type of videos since current endoscopes are equipped with a single, wide-angle lens that cannot be focused. The out-of-focus frames do not hold any useful information. To reduce the burdens of the further processes such as computer-aided image processing or human expert"s examinations, these frames need to be removed. We call an out-of-focus frame as non-informative frame and an in-focus frame as informative frame. We propose a new technique to classify the video frames into two classes, informative and non-informative frames using a combination of Discrete Fourier Transform (DFT), Texture Analysis, and K-Means Clustering. The proposed technique can evaluate the frames without any reference image, and does not need any predefined threshold value. Our experimental studies indicate that it achieves over 96% of four different performance metrics (i.e. precision, sensitivity, specificity, and accuracy).

Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Basavanhally, Ajay; González, Fabio; Gilmore, Hannah; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant

2014-03-01

This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classifier for invasive tumor classification using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classification mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.

Glaucoma progression detection: agreement, sensitivity, and specificity of expert visual field evaluation, event analysis, and trend analysis.

PubMed

Antón, Alfonso; Pazos, Marta; Martín, Belén; Navero, José Manuel; Ayala, Miriam Eleonora; Castany, Marta; Martínez, Patricia; Bardavío, Javier

2013-01-01

To assess sensitivity, specificity, and agreement among automated event analysis, automated trend analysis, and expert evaluation to detect glaucoma progression. This was a prospective study that included 37 eyes with a follow-up of 36 months. All had glaucomatous disks and fields and performed reliable visual fields every 6 months. Each series of fields was assessed with 3 different methods: subjective assessment by 2 independent teams of glaucoma experts, glaucoma/guided progression analysis (GPA) event analysis, and GPA (visual field index-based) trend analysis. Kappa agreement coefficient between methods and sensitivity and specificity for each method using expert opinion as gold standard were calculated. The incidence of glaucoma progression was 16% to 18% in 3 years but only 3 cases showed progression with all 3 methods. Kappa agreement coefficient was high (k=0.82) between subjective expert assessment and GPA event analysis, and only moderate between these two and GPA trend analysis (k=0.57). Sensitivity and specificity for GPA event and GPA trend analysis were 71% and 96%, and 57% and 93%, respectively. The 3 methods detected similar numbers of progressing cases. The GPA event analysis and expert subjective assessment showed high agreement between them and moderate agreement with GPA trend analysis. In a period of 3 years, both methods of GPA analysis offered high specificity, event analysis showed 83% sensitivity, and trend analysis had a 66% sensitivity.

A validation framework for brain tumor segmentation.

PubMed

Archip, Neculai; Jolesz, Ferenc A; Warfield, Simon K

2007-10-01

We introduce a validation framework for the segmentation of brain tumors from magnetic resonance (MR) images. A novel unsupervised semiautomatic brain tumor segmentation algorithm is also presented. The proposed framework consists of 1) T1-weighted MR images of patients with brain tumors, 2) segmentation of brain tumors performed by four independent experts, 3) segmentation of brain tumors generated by a semiautomatic algorithm, and 4) a software tool that estimates the performance of segmentation algorithms. We demonstrate the validation of the novel segmentation algorithm within the proposed framework. We show its performance and compare it with existent segmentation. The image datasets and software are available at http://www.brain-tumor-repository.org/. We present an Internet resource that provides access to MR brain tumor image data and segmentation that can be openly used by the research community. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results.

Dog experts' brains distinguish socially relevant body postures similarly in dogs and humans.

PubMed

Kujala, Miiamaaria V; Kujala, Jan; Carlson, Synnöve; Hari, Riitta

2012-01-01

We read conspecifics' social cues effortlessly, but little is known about our abilities to understand social gestures of other species. To investigate the neural underpinnings of such skills, we used functional magnetic resonance imaging to study the brain activity of experts and non-experts of dog behavior while they observed humans or dogs either interacting with, or facing away from a conspecific. The posterior superior temporal sulcus (pSTS) of both subject groups dissociated humans facing toward each other from humans facing away, and in dog experts, a distinction also occurred for dogs facing toward vs. away in a bilateral area extending from the pSTS to the inferior temporo-occipital cortex: the dissociation of dog behavior was significantly stronger in expert than control group. Furthermore, the control group had stronger pSTS responses to humans than dogs facing toward a conspecific, whereas in dog experts, the responses were of similar magnitude. These findings suggest that dog experts' brains distinguish socially relevant body postures similarly in dogs and humans.

Innovation contests to promote sexual health in China: a qualitative evaluation.

PubMed

Zhang, Wei; Schaffer, David; Tso, Lai Sze; Tang, Songyuan; Tang, Weiming; Huang, Shujie; Yang, Bin; Tucker, Joseph D

2017-01-14

Innovation contests call on non-experts to help solve problems. While these contests have been used extensively in the private sector to increase engagement between organizations and clients, there is little data on the role of innovation contests to promote health campaigns. We implemented an innovation contest in China to increase sexual health awareness among youth and evaluated community engagement in the contest. The sexual health image contest consisted of an open call for sexual health images, contest promotion activities, judging of entries, and celebrating contributions. Contest promotion activities included in-person and social media feedback, classroom didactics, and community-driven activities. We conducted 19 semi-structured interviews with a purposive sample to ensure a range of participant scores, experts and non-expert participants, submitters and non-submitters. Transcripts of each interview were coded with Atlas.ti and evaluated by three reviewers. We identified stages of community engagement in the contest which contributed to public health impact. Community engagement progressed across a continuum from passive, moderate, active, and finally strong engagement. Engagement was a dynamic process that appeared to have little relationship with formally submitting an image to the contest. Among non-expert participants, contest engagement increased knowledge, healthy attitudes, and empowered participants to share ideas about safe sex with others outside of the contest. Among experts who helped organize the contest, the process of implementing the contest fostered multi-sectoral collaboration and re-oriented public health leadership towards more patient-centered public health campaigns. The results of this study suggest that innovation contests may be a useful tool for public health promotion by enhancing community engagement and re-orienting health campaigns to make them more patient-centered.

Automated Mapping and Characterization of RSL from HiRISE data with MAARSL

NASA Astrophysics Data System (ADS)

Bue, Brian; Wagstaff, Kiri; Stillman, David

2017-10-01

Recurring slope lineae (RSL) are narrow (0.5-5m) low-albedo features on Mars that recur, fade, and incrementally lengthen on steep slopes throughout the year. Determining the processes that generate RSL requires detailed analysis of high-resolution orbital images to measure RSL surface properties and seasonal variation. However, conducting this analysis manually is labor intensive, time consuming, and infeasible given the large number of relevant sites. This abstract describes the Mapping and Automated Analysis of RSL (MAARSL) system, which we designed to aid large-scale analysis of seasonal RSL properties. MAARSL takes an ordered sequence of high spatial resolution, orthorectified, and coregistered orbital image data (e.g., MRO HiRISE images) and a corresponding Digital Terrain Model (DTM) as input and performs three primary functions: (1) detect and delineate candidate RSL in each image, (2) compute statistics of surface morphology and observed radiance for each candidate, and (3) measure temporal variation between candidates in adjacent images.The main challenge in automatic image-based RSL detection is discriminating true RSL from other low-albedo regions such as shadows or changes in surface materials is . To discriminate RSL from shadows, MAARSL constructs a linear illumination model for each image based on the DTM and position and orientation of the instrument at image acquisition time. We filter out any low-albedo regions that appear to be shadows via a least-squares fit between the modeled illumination and the observed intensity in each image. False detections occur in areas where the 1m/pixel HiRISE DTM poorly captures the variability of terrain observed in the 0.25m/pixel HiRISE images. To remove these spurious detections, we developed an interactive machine learning graphical interface that uses expert input to filter and validate the RSL candidates. This tool yielded 636 candidates from a well-studied sequence of 18 HiRISE images of Garni crater in Valles Marineris with minimal manual effort. We describe our analysis of RSL candidates at Garni crater and Coprates Montes and ongoing studies of other regions where RSL occur.

Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

NASA Astrophysics Data System (ADS)

Amrute, Junedh M.; Athanasiou, Lambros S.; Rikhtegar, Farhad; de la Torre Hernández, José M.; Camarero, Tamara García; Edelman, Elazer R.

2018-03-01

Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging-they are relatively invisible via angiography-and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images.

A medical software system for volumetric analysis of cerebral pathologies in magnetic resonance imaging (MRI) data.

PubMed

Egger, Jan; Kappus, Christoph; Freisleben, Bernd; Nimsky, Christopher

2012-08-01

In this contribution, a medical software system for volumetric analysis of different cerebral pathologies in magnetic resonance imaging (MRI) data is presented. The software system is based on a semi-automatic segmentation algorithm and helps to overcome the time-consuming process of volume determination during monitoring of a patient. After imaging, the parameter settings-including a seed point-are set up in the system and an automatic segmentation is performed by a novel graph-based approach. Manually reviewing the result leads to reseeding, adding seed points or an automatic surface mesh generation. The mesh is saved for monitoring the patient and for comparisons with follow-up scans. Based on the mesh, the system performs a voxelization and volume calculation, which leads to diagnosis and therefore further treatment decisions. The overall system has been tested with different cerebral pathologies-glioblastoma multiforme, pituitary adenomas and cerebral aneurysms- and evaluated against manual expert segmentations using the Dice Similarity Coefficient (DSC). Additionally, intra-physician segmentations have been performed to provide a quality measure for the presented system.

DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images.

PubMed

Skounakis, Emmanouil; Farmaki, Christina; Sakkalis, Vangelis; Roniotis, Alexandros; Banitsas, Konstantinos; Graf, Norbert; Marias, Konstantinos

2010-01-01

This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. the platform, a manual and tutorial videos are available at: http://biomodeling.ics.forth.gr. it is free to use under the GNU General Public License.

EULAR-PReS points to consider for the use of imaging in the diagnosis and management of juvenile idiopathic arthritis in clinical practice.

PubMed

Colebatch-Bourn, A N; Edwards, C J; Collado, P; D'Agostino, M-A; Hemke, R; Jousse-Joulin, S; Maas, M; Martini, A; Naredo, E; Østergaard, M; Rooney, M; Tzaribachev, N; van Rossum, M A; Vojinovic, J; Conaghan, P G; Malattia, C

2015-11-01

To develop evidence based points to consider the use of imaging in the diagnosis and management of juvenile idiopathic arthritis (JIA) in clinical practice. The task force comprised a group of paediatric rheumatologists, rheumatologists experienced in imaging, radiologists, methodologists and patients from nine countries. Eleven questions on imaging in JIA were generated using a process of discussion and consensus. Research evidence was searched systematically for each question using MEDLINE, EMBASE and Cochrane CENTRAL. Imaging modalities included were conventional radiography, ultrasound, MRI, CT, scintigraphy and positron emission tomography. The experts used the evidence obtained from the relevant studies to develop a set of points to consider. The level of agreement with each point to consider was assessed using a numerical rating scale. A total of 13 277 references were identified from the search process, from which 204 studies were included in the systematic review. Nine points to consider were produced, taking into account the heterogeneity of JIA, the lack of normative data and consequent difficulty identifying pathology. These encompassed the role of imaging in making a diagnosis of JIA, detecting and monitoring inflammation and damage, predicting outcome and response to treatment, use of guided therapies, progression and remission. Level of agreement for each proposition varied according to the research evidence and expert opinion. Nine points to consider and a related research agenda for the role of imaging in the management of JIA were developed using published evidence and expert opinion. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

An evaluation of consensus techniques for diagnostic interpretation

NASA Astrophysics Data System (ADS)

Sauter, Jake N.; LaBarre, Victoria M.; Furst, Jacob D.; Raicu, Daniela S.

2018-02-01

Learning diagnostic labels from image content has been the standard in computer-aided diagnosis. Most computer-aided diagnosis systems use low-level image features extracted directly from image content to train and test machine learning classifiers for diagnostic label prediction. When the ground truth for the diagnostic labels is not available, reference truth is generated from the experts diagnostic interpretations of the image/region of interest. More specifically, when the label is uncertain, e.g. when multiple experts label an image and their interpretations are different, techniques to handle the label variability are necessary. In this paper, we compare three consensus techniques that are typically used to encode the variability in the experts labeling of the medical data: mean, median and mode, and their effects on simple classifiers that can handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees). Given that the NIH/NCI Lung Image Database Consortium (LIDC) data provides interpretations for lung nodules by up to four radiologists, we leverage the LIDC data to evaluate and compare these consensus approaches when creating computer-aided diagnosis systems for lung nodules. First, low-level image features of nodules are extracted and paired with their radiologists semantic ratings (1= most likely benign, , 5 = most likely malignant); second, machine learning multi-class classifiers that handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees) are built to predict the lung nodules semantic ratings. We show that the mean-based consensus generates the most robust classi- fier overall when compared to the median- and mode-based consensus. Lastly, the results of this study show that, when building CAD systems with uncertain diagnostic interpretation, it is important to evaluate different strategies for encoding and predicting the diagnostic label.

Automated detection of diabetic retinopathy: barriers to translation into clinical practice.

PubMed

Abramoff, Michael D; Niemeijer, Meindert; Russell, Stephen R

2010-03-01

Automated identification of diabetic retinopathy (DR), the primary cause of blindness and visual loss for those aged 18-65 years, from color images of the retina has enormous potential to increase the quality, cost-effectiveness and accessibility of preventative care for people with diabetes. Through advanced image analysis techniques, retinal images are analyzed for abnormalities that define and correlate with the severity of DR. Translating automated DR detection into clinical practice will require surmounting scientific and nonscientific barriers. Scientific concerns, such as DR detection limits compared with human experts, can be studied and measured. Ethical, legal and political issues can be addressed, but are difficult or impossible to measure. The primary objective of this review is to survey the methods, potential benefits and limitations of automated detection in order to better manage translation into clinical practice, based on extensive experience with the systems we have developed.

Automatic multiresolution age-related macular degeneration detection from fundus images

NASA Astrophysics Data System (ADS)

Garnier, Mickaël.; Hurtut, Thomas; Ben Tahar, Houssem; Cheriet, Farida

2014-03-01

Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is captured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93:3%, with a specificity of 95:5% and a sensitivity of 91:3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.

A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data.

PubMed

Engblom, Henrik; Tufvesson, Jane; Jablonowski, Robert; Carlsson, Marcus; Aletras, Anthony H; Hoffmann, Pavel; Jacquier, Alexis; Kober, Frank; Metzler, Bernhard; Erlinge, David; Atar, Dan; Arheden, Håkan; Heiberg, Einar

2016-05-04

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images). Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in PSIR images (n = 49). The EWA algorithm was validated experimentally and in patient data with a low bias in both IR and PSIR LGE images. Thus, the use of EM and a weighted intensity as in the EWA algorithm, may serve as a clinical standard for the quantification of myocardial infarction in LGE CMR images. CHILL-MI: NCT01379261 . NCT01374321 .

Computer interpretation of thallium SPECT studies based on neural network analysis

NASA Astrophysics Data System (ADS)

Wang, David C.; Karvelis, K. C.

1991-06-01

A class of artificial intelligence (Al) programs known as neural networks are well suited to pattern recognition. A neural network is trained rather than programmed to recognize patterns. This differs from "expert system" Al programs in that it is not following an extensive set of rules determined by the programmer, but rather bases its decision on a gestalt interpretation of the image. The "bullseye" images from cardiac stress thallium tests performed on 50 male patients, as well as several simulated images were used to train the network. The network was able to accurately classify all patients in the training set. The network was then tested against 50 unknown patients and was able to correctly categorize 77% of the areas of ischemia and 92% of the areas of infarction. While not yet matching the ability of a trained physician, the neural network shows great promise in this area and has potential application in other areas of medical imaging.

Shape priors for segmentation of the cervix region within uterine cervix images

NASA Astrophysics Data System (ADS)

Lotenberg, Shelly; Gordon, Shiri; Greenspan, Hayit

2008-03-01

The work focuses on a unique medical repository of digital Uterine Cervix images ("Cervigrams") collected by the National Cancer Institute (NCI), National Institute of Health, in longitudinal multi-year studies. NCI together with the National Library of Medicine is developing a unique web-based database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for the automated analysis of the cervigram content to support the cancer research. In recent works, a multi-stage automated system for segmenting and labeling regions of medical and anatomical interest within the cervigrams was developed. The current paper concentrates on incorporating prior-shape information in the cervix region segmentation task. In accordance with the fact that human experts mark the cervix region as circular or elliptical, two shape models (and corresponding methods) are suggested. The shape models are embedded within an active contour framework that relies on image features. Experiments indicate that incorporation of the prior shape information augments previous results.

International Endometrial Tumor Analysis (IETA) terminology in women with postmenopausal bleeding and sonographic endometrial thickness ≥ 4.5 mm: agreement and reliability study.

PubMed

Sladkevicius, P; Installé, A; Van Den Bosch, T; Timmerman, D; Benacerraf, B; Jokubkiene, L; Di Legge, A; Votino, A; Zannoni, L; De Moor, B; De Cock, B; Van Calster, B; Valentin, L

2018-02-01

To estimate intra- and interrater agreement and reliability with regard to describing ultrasound images of the endometrium using the International Endometrial Tumor Analysis (IETA) terminology. Four expert and four non-expert raters assessed videoclips of transvaginal ultrasound examinations of the endometrium obtained from 99 women with postmenopausal bleeding and sonographic endometrial thickness ≥ 4.5 mm but without fluid in the uterine cavity. The following features were rated: endometrial echogenicity, endometrial midline, bright edge, endometrial-myometrial junction, color score, vascular pattern, irregularly branching vessels and color splashes. The color content of the endometrial scan was estimated using a visual analog scale graded from 0 to 100. To estimate intrarater agreement and reliability, the same videoclips were assessed twice with a minimum of 2 months' interval. The raters were blinded to their own results and to those of the other raters. Interrater differences in the described prevalence of most IETA variables were substantial, and some variable categories were observed rarely. Specific agreement was poor for variables with many categories. For binary variables, specific agreement was better for absence than for presence of a category. For variables with more than two outcome categories, specific agreement for expert and non-expert raters was best for not-defined endometrial midline (93% and 96%), regular endometrial-myometrial junction (72% and 70%) and three-layer endometrial pattern (67% and 56%). The grayscale ultrasound variable with the best reliability was uniform vs non-uniform echogenicity (multirater kappa (κ), 0.55 for expert and 0.52 for non-expert raters), and the variables with the lowest reliability were appearance of the endometrial-myometrial junction (κ, 0.25 and 0.16) and the nine-category endometrial echogenicity variable (κ, 0.29 and 0.28). The most reliable color Doppler variable was color score (mean weighted κ, 0.77 and 0.69). Intra- and interrater agreement and reliability were similar for experts and non-experts. Inter- and intrarater agreement and reliability when using IETA terminology were limited. This may have implications when assessing the association between a particular ultrasound feature and a specific histological diagnosis, because lack of reproducibility reduces the reliability of the association between a feature and the outcome. Future studies should investigate whether using fewer categories of variable or offering practical training could improve agreement and reliability. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

Differences between Experts' and Students' Conceptual Images of the Mathematical Structure of Taylor Series Convergence

ERIC Educational Resources Information Center

Martin, Jason

2013-01-01

Taylor series convergence is a complicated mathematical structure which incorporates multiple concepts. Therefore, it can be very difficult for students to initially comprehend. How might students make sense of this structure? How might experts make sense of this structure? To answer these questions, an exploratory study was conducted using…

The KIDROP model of combining strategies for providing retinopathy of prematurity screening in underserved areas in India using wide-field imaging, tele-medicine, non-physician graders and smart phone reporting

PubMed Central

Vinekar, Anand; Gilbert, Clare; Dogra, Mangat; Kurian, Mathew; Shainesh, Gangadharan; Shetty, Bhujang; Bauer, Noel

2014-01-01

Aim: To report the Karnataka Internet Assisted Diagnosis of Retinopathy of Prematurity (KIDROP) program for retinopathy of prematurity (ROP) screening in underserved rural areas using an indigenously developed tele-ROP model. Materials and Methods: KIDROP currently provides ROP screening and treatment services in three zones and 81 neonatal units in Karnataka, India. Technicians were trained to use a portable Retcam Shuttle (Clarity, USA) and validated against ROP experts performing indirect ophthalmoscopy. An indigenously developed 20-point score (STAT score) graded their ability (Level I to III) to image and decide follow-up based on a three-way algorithm. Images were also uploaded on a secure tele-ROP platform and accessed and reported by remote experts on their smart phones (iPhone, Apple). Results: 6339 imaging sessions of 1601 infants were analyzed. A level III technician agreed with 94.3% of all expert decisions. The sensitivity, specificity, positive predictive value and negative predictive value for treatment grade disease were 95.7, 93.2, 81.5 and 98.6 respectively. The kappa for technicians to decide discharge of babies was 0.94 (P < 0.001). Only 0.4% of infants needing treatment were missed. The kappa agreement of experts reporting on the iPhone vs Retcam for treatment requiring and mild ROP were 0.96 and 0.94 (P < 0.001) respectively. Conclusions: This is the first and largest real-world program to employ accredited non-physicians to grade and report ROP. The KIDROP tele-ROP model demonstrates that ROP services can be delivered to the outreach despite lack of specialists and may be useful in other middle-income countries with similar demographics. PMID:24492500

Collaborative real-time motion video analysis by human observer and image exploitation algorithms

NASA Astrophysics Data System (ADS)

Hild, Jutta; Krüger, Wolfgang; Brüstle, Stefan; Trantelle, Patrick; Unmüßig, Gabriel; Heinze, Norbert; Peinsipp-Byma, Elisabeth; Beyerer, Jürgen

2015-05-01

Motion video analysis is a challenging task, especially in real-time applications. In most safety and security critical applications, a human observer is an obligatory part of the overall analysis system. Over the last years, substantial progress has been made in the development of automated image exploitation algorithms. Hence, we investigate how the benefits of automated video analysis can be integrated suitably into the current video exploitation systems. In this paper, a system design is introduced which strives to combine both the qualities of the human observer's perception and the automated algorithms, thus aiming to improve the overall performance of a real-time video analysis system. The system design builds on prior work where we showed the benefits for the human observer by means of a user interface which utilizes the human visual focus of attention revealed by the eye gaze direction for interaction with the image exploitation system; eye tracker-based interaction allows much faster, more convenient, and equally precise moving target acquisition in video images than traditional computer mouse selection. The system design also builds on prior work we did on automated target detection, segmentation, and tracking algorithms. Beside the system design, a first pilot study is presented, where we investigated how the participants (all non-experts in video analysis) performed in initializing an object tracking subsystem by selecting a target for tracking. Preliminary results show that the gaze + key press technique is an effective, efficient, and easy to use interaction technique when performing selection operations on moving targets in videos in order to initialize an object tracking function.

Optical granulometric analysis of sedimentary deposits by color segmentation-based software: OPTGRAN-CS

NASA Astrophysics Data System (ADS)

Chávez, G. Moreno; Sarocchi, D.; Santana, E. Arce; Borselli, L.

2015-12-01

The study of grain size distribution is fundamental for understanding sedimentological environments. Through these analyses, clast erosion, transport and deposition processes can be interpreted and modeled. However, grain size distribution analysis can be difficult in some outcrops due to the number and complexity of the arrangement of clasts and matrix and their physical size. Despite various technological advances, it is almost impossible to get the full grain size distribution (blocks to sand grain size) with a single method or instrument of analysis. For this reason development in this area continues to be fundamental. In recent years, various methods of particle size analysis by automatic image processing have been developed, due to their potential advantages with respect to classical ones; speed and final detailed content of information (virtually for each analyzed particle). In this framework, we have developed a novel algorithm and software for grain size distribution analysis, based on color image segmentation using an entropy-controlled quadratic Markov measure field algorithm and the Rosiwal method for counting intersections between clast and linear transects in the images. We test the novel algorithm in different sedimentary deposit types from 14 varieties of sedimentological environments. The results of the new algorithm were compared with grain counts performed manually by the same Rosiwal methods applied by experts. The new algorithm has the same accuracy as a classical manual count process, but the application of this innovative methodology is much easier and dramatically less time-consuming. The final productivity of the new software for analysis of clasts deposits after recording field outcrop images can be increased significantly.

Enhancing Image Findability through a Dual-Perspective Navigation Framework

ERIC Educational Resources Information Center

Lin, Yi-Ling

2013-01-01

This dissertation focuses on investigating whether users will locate desired images more efficiently and effectively when they are provided with information descriptors from both experts and the general public. This study develops a way to support image finding through a human-computer interface by providing subject headings and social tags about…

A User's Applications of Imaging Techniques: The University of Maryland Historic Textile Database.

ERIC Educational Resources Information Center

Anderson, Clarita S.

1991-01-01

Describes the incorporation of textile images into the University of Maryland Historic Textile Database by a computer user rather than a computer expert. Selection of a database management system is discussed, and PICTUREPOWER, a system that integrates photographic quality images with text and numeric information in databases, is described. (three…

Assessment of using digital manipulation tools for diagnosing mandibular radiolucent lesions

PubMed Central

Raitz, R; Assunção Junior, JNR; Fenyo-Pereira, M; Correa, L; de Lima, LP

2012-01-01

Objective The purpose of this study was to analyse the use of digital tools for image enhancement of mandibular radiolucent lesions and the effects of this manipulation on the percentage of correct radiographic diagnoses. Methods 24 panoramic radiographs exhibiting radiolucent lesions were selected, digitized and evaluated by non-experts (undergraduate and newly graduated practitioners) and by professional experts in oral diagnosis. The percentages of correct and incorrect diagnoses, according to the use of brightness/contrast, sharpness, inversion, highlight and zoom tools, were compared. All dental professionals made their evaluations without (T1) and with (T2) a list of radiographic diagnostic parameters. Results Digital tools were used with low frequency mainly in T2. The most preferred tool was sharpness (45.2%). In the expert group, the percentage of correct diagnoses did not change when any of the digital tools were used. For the non-expert group, there was an increase in the frequency of correct diagnoses when brightness/contrast was used in T2 (p=0.008) and when brightness/contrast and sharpness were not used in T1 (p=0.027). The use or non-use of brightness/contrast, zoom and sharpness showed moderate agreement in the group of experts [kappa agreement coefficient (κ)=0.514, 0.425 and 0.335, respectively]. For the non-expert group there was slight agreement for all the tools used (κ≤0.237). Conclusions Consulting the list of radiographic parameters before image manipulation reduced the frequency of tool use in both groups of examiners. Consulting the radiographic parameters with the use of some digital tools was important for improving correct diagnosis only in the group of non-expert examiners. PMID:22116126

Assessment of using digital manipulation tools for diagnosing mandibular radiolucent lesions.

PubMed

Raitz, R; Assunção Junior, J N R; Fenyo-Pereira, M; Correa, L; de Lima, L P

2012-03-01

The purpose of this study was to analyse the use of digital tools for image enhancement of mandibular radiolucent lesions and the effects of this manipulation on the percentage of correct radiographic diagnoses. 24 panoramic radiographs exhibiting radiolucent lesions were selected, digitized and evaluated by non-experts (undergraduate and newly graduated practitioners) and by professional experts in oral diagnosis. The percentages of correct and incorrect diagnoses, according to the use of brightness/contrast, sharpness, inversion, highlight and zoom tools, were compared. All dental professionals made their evaluations without (T₁) and with (T₂) a list of radiographic diagnostic parameters. Digital tools were used with low frequency mainly in T₂. The most preferred tool was sharpness (45.2%). In the expert group, the percentage of correct diagnoses did not change when any of the digital tools were used. For the non-expert group, there was an increase in the frequency of correct diagnoses when brightness/contrast was used in T₂ (p=0.008) and when brightness/contrast and sharpness were not used in T₁ (p=0.027). The use or non-use of brightness/contrast, zoom and sharpness showed moderate agreement in the group of experts [kappa agreement coefficient (κ) = 0.514, 0.425 and 0.335, respectively]. For the non-expert group there was slight agreement for all the tools used (κ ≤ 0.237). Consulting the list of radiographic parameters before image manipulation reduced the frequency of tool use in both groups of examiners. Consulting the radiographic parameters with the use of some digital tools was important for improving correct diagnosis only in the group of non-expert examiners.

Mimicking human expert interpretation of remotely sensed raster imagery by using a novel segmentation analysis within ArcGIS

NASA Astrophysics Data System (ADS)

Le Bas, Tim; Scarth, Anthony; Bunting, Peter

2015-04-01

Traditional computer based methods for the interpretation of remotely sensed imagery use each pixel individually or the average of a small window of pixels to calculate a class or thematic value, which provides an interpretation. However when a human expert interprets imagery, the human eye is excellent at finding coherent and homogenous areas and edge features. It may therefore be advantageous for computer analysis to mimic human interpretation. A new toolbox for ArcGIS 10.x will be presented that segments the data layers into a set of polygons. Each polygon is defined by a K-means clustering and region growing algorithm, thus finding areas, their edges and any lineations in the imagery. Attached to each polygon are the characteristics of the imagery such as mean and standard deviation of the pixel values, within the polygon. The segmentation of imagery into a jigsaw of polygons also has the advantage that the human interpreter does not need to spend hours digitising the boundaries. The segmentation process has been taken from the RSGIS library of analysis and classification routines (Bunting et al., 2014). These routines are freeware and have been modified to be available in the ArcToolbox under the Windows (v7) operating system. Input to the segmentation process is a multi-layered raster image, for example; a Landsat image, or a set of raster datasets made up from derivatives of topography. The size and number of polygons are set by the user and are dependent on the imagery used. Examples will be presented of data from the marine environment utilising bathymetric depth, slope, rugosity and backscatter from a multibeam system. Meaningful classification of the polygons using their numerical characteristics is the next goal. Object based image analysis (OBIA) should help this workflow. Fully calibrated imagery systems will allow numerical classification to be translated into more readily understandable terms. Peter Bunting, Daniel Clewley, Richard M. Lucas and Sam Gillingham. 2014. The Remote Sensing and GIS Software Library (RSGISLib), Computers & Geosciences. Volume 62, Pages 216-226 http://dx.doi.org/10.1016/j.cageo.2013.08.007.

Forensic Comparison and Matching of Fingerprints: Using Quantitative Image Measures for Estimating Error Rates through Understanding and Predicting Difficulty

PubMed Central

Kellman, Philip J.; Mnookin, Jennifer L.; Erlikhman, Gennady; Garrigan, Patrick; Ghose, Tandra; Mettler, Everett; Charlton, David; Dror, Itiel E.

2014-01-01

Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert performance and subjective assessment of difficulty in fingerprint comparisons. PMID:24788812

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies.

PubMed

Koch, Lisa M; Rajchl, Martin; Bai, Wenjia; Baumgartner, Christian F; Tong, Tong; Passerat-Palmbach, Jonathan; Aljabar, Paul; Rueckert, Daniel

2017-08-22

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.

Automatic quantitative analysis of in-stent restenosis using FD-OCT in vivo intra-arterial imaging.

PubMed

Mandelias, Kostas; Tsantis, Stavros; Spiliopoulos, Stavros; Katsakiori, Paraskevi F; Karnabatidis, Dimitris; Nikiforidis, George C; Kagadis, George C

2013-06-01

A new segmentation technique is implemented for automatic lumen area extraction and stent strut detection in intravascular optical coherence tomography (OCT) images for the purpose of quantitative analysis of in-stent restenosis (ISR). In addition, a user-friendly graphical user interface (GUI) is developed based on the employed algorithm toward clinical use. Four clinical datasets of frequency-domain OCT scans of the human femoral artery were analyzed. First, a segmentation method based on fuzzy C means (FCM) clustering and wavelet transform (WT) was applied toward inner luminal contour extraction. Subsequently, stent strut positions were detected by utilizing metrics derived from the local maxima of the wavelet transform into the FCM membership function. The inner lumen contour and the position of stent strut were extracted with high precision. Compared to manual segmentation by an expert physician, the automatic lumen contour delineation had an average overlap value of 0.917 ± 0.065 for all OCT images included in the study. The strut detection procedure achieved an overall accuracy of 93.80% and successfully identified 9.57 ± 0.5 struts for every OCT image. Processing time was confined to approximately 2.5 s per OCT frame. A new fast and robust automatic segmentation technique combining FCM and WT for lumen border extraction and strut detection in intravascular OCT images was designed and implemented. The proposed algorithm integrated in a GUI represents a step forward toward the employment of automated quantitative analysis of ISR in clinical practice.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity

PubMed Central

Wittenberg, Leah A.; Jonsson, Nina J.; Chan, RV Paul; Chiang, Michael F.

2014-01-01

Presence of plus disease in retinopathy of prematurity (ROP) is an important criterion for identifying treatment-requiring ROP. Plus disease is defined by a standard published photograph selected over 20 years ago by expert consensus. However, diagnosis of plus disease has been shown to be subjective and qualitative. Computer-based image analysis, using quantitative methods, has potential to improve the objectivity of plus disease diagnosis. The objective was to review the published literature involving computer-based image analysis for ROP diagnosis. The PubMed and Cochrane library databases were searched for the keywords “retinopathy of prematurity” AND “image analysis” AND/OR “plus disease.” Reference lists of retrieved articles were searched to identify additional relevant studies. All relevant English-language studies were reviewed. There are four main computer-based systems, ROPtool (AU ROC curve, plus tortuosity 0.95, plus dilation 0.87), RISA (AU ROC curve, arteriolar TI 0.71, venular diameter 0.82), Vessel Map (AU ROC curve, arteriolar dilation 0.75, venular dilation 0.96), and CAIAR (AU ROC curve, arteriole tortuosity 0.92, venular dilation 0.91), attempting to objectively analyze vessel tortuosity and dilation in plus disease in ROP. Some of them show promise for identification of plus disease using quantitative methods. This has potential to improve the diagnosis of plus disease, and may contribute to the management of ROP using both traditional binocular indirect ophthalmoscopy and image-based telemedicine approaches. PMID:21366159

Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears.

PubMed

Luengo-Oroz, Miguel Angel; Arranz, Asier; Frean, John

2012-11-29

There are 600,000 new malaria cases daily worldwide. The gold standard for estimating the parasite burden and the corresponding severity of the disease consists in manually counting the number of parasites in blood smears through a microscope, a process that can take more than 20 minutes of an expert microscopist's time. This research tests the feasibility of a crowdsourced approach to malaria image analysis. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count malaria parasites in digitized images of thick blood smears by playing a Web-based game. The experimental system consisted of a Web-based game where online volunteers were tasked with detecting parasites in digitized blood sample images coupled with a decision algorithm that combined the analyses from several players to produce an improved collective detection outcome. Data were collected through the MalariaSpot website. Random images of thick blood films containing Plasmodium falciparum at medium to low parasitemias, acquired by conventional optical microscopy, were presented to players. In the game, players had to find and tag as many parasites as possible in 1 minute. In the event that players found all the parasites present in the image, they were presented with a new image. In order to combine the choices of different players into a single crowd decision, we implemented an image processing pipeline and a quorum algorithm that judged a parasite tagged when a group of players agreed on its position. Over 1 month, anonymous players from 95 countries played more than 12,000 games and generated a database of more than 270,000 clicks on the test images. Results revealed that combining 22 games from nonexpert players achieved a parasite counting accuracy higher than 99%. This performance could be obtained also by combining 13 games from players trained for 1 minute. Exhaustive computations measured the parasite counting accuracy for all players as a function of the number of games considered and the experience of the players. In addition, we propose a mathematical equation that accurately models the collective parasite counting performance. This research validates the online gaming approach for crowdsourced counting of malaria parasites in images of thick blood films. The findings support the conclusion that nonexperts are able to rapidly learn how to identify the typical features of malaria parasites in digitized thick blood samples and that combining the analyses of several users provides similar parasite counting accuracy rates as those of expert microscopists. This experiment illustrates the potential of the crowdsourced gaming approach for performing routine malaria parasite quantification, and more generally for solving biomedical image analysis problems, with future potential for telediagnosis related to global health challenges.

Crowdsourcing Malaria Parasite Quantification: An Online Game for Analyzing Images of Infected Thick Blood Smears

PubMed Central

Arranz, Asier; Frean, John

2012-01-01

Background There are 600,000 new malaria cases daily worldwide. The gold standard for estimating the parasite burden and the corresponding severity of the disease consists in manually counting the number of parasites in blood smears through a microscope, a process that can take more than 20 minutes of an expert microscopist’s time. Objective This research tests the feasibility of a crowdsourced approach to malaria image analysis. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count malaria parasites in digitized images of thick blood smears by playing a Web-based game. Methods The experimental system consisted of a Web-based game where online volunteers were tasked with detecting parasites in digitized blood sample images coupled with a decision algorithm that combined the analyses from several players to produce an improved collective detection outcome. Data were collected through the MalariaSpot website. Random images of thick blood films containing Plasmodium falciparum at medium to low parasitemias, acquired by conventional optical microscopy, were presented to players. In the game, players had to find and tag as many parasites as possible in 1 minute. In the event that players found all the parasites present in the image, they were presented with a new image. In order to combine the choices of different players into a single crowd decision, we implemented an image processing pipeline and a quorum algorithm that judged a parasite tagged when a group of players agreed on its position. Results Over 1 month, anonymous players from 95 countries played more than 12,000 games and generated a database of more than 270,000 clicks on the test images. Results revealed that combining 22 games from nonexpert players achieved a parasite counting accuracy higher than 99%. This performance could be obtained also by combining 13 games from players trained for 1 minute. Exhaustive computations measured the parasite counting accuracy for all players as a function of the number of games considered and the experience of the players. In addition, we propose a mathematical equation that accurately models the collective parasite counting performance. Conclusions This research validates the online gaming approach for crowdsourced counting of malaria parasites in images of thick blood films. The findings support the conclusion that nonexperts are able to rapidly learn how to identify the typical features of malaria parasites in digitized thick blood samples and that combining the analyses of several users provides similar parasite counting accuracy rates as those of expert microscopists. This experiment illustrates the potential of the crowdsourced gaming approach for performing routine malaria parasite quantification, and more generally for solving biomedical image analysis problems, with future potential for telediagnosis related to global health challenges. PMID:23196001

Video-based teleradiology for intraosseous lesions. A receiver operating characteristic analysis.

PubMed

Tyndall, D A; Boyd, K S; Matteson, S R; Dove, S B

1995-11-01

Immediate access to off-site expert diagnostic consultants regarding unusual radiographic findings or radiographic quality assurance issues could be a current problem for private dental practitioners. Teleradiology, a system for transmitting radiographic images, offers a potential solution to this problem. Although much research has been done to evaluate feasibility and utilization of teleradiology systems in medical imaging, little research on dental applications has been performed. In this investigation 47 panoramic films with an equal distribution of images with intraosseous jaw lesions and no disease were viewed by a panel of observers with teleradiology and conventional viewing methods. The teleradiology system consisted of an analog video-based system simulating remote radiographic consultation between a general dentist and a dental imaging specialist. Conventional viewing consisted of traditional viewbox methods. Observers were asked to identify the presence or absence of 24 intraosseous lesions and to determine their locations. No statistically significant differences in modalities or observers were identified between methods at the 0.05 level. The results indicate that viewing intraosseous lesions of video-based panoramic images is equal to conventional light box viewing.

The aware toolbox for the detection of law infringements on web pages

NASA Astrophysics Data System (ADS)

Shahab, Asif; Kieninger, Thomas; Dengel, Andreas

2010-01-01

In the project Aware we aim to develop an automatic assistant for the detection of law infringements on web pages. The motivation for this project is that many authors of web pages are at some points infringing copyrightor other laws, mostly without being aware of that fact, and are more and more often confronted with costly legal warnings. As the legal environment is constantly changing, an important requirement of Aware is that the domain knowledge can be maintained (and initially defined) by numerous legal experts remotely working without further assistance of the computer scientists. Consequently, the software platform was chosen to be a web-based generic toolbox that can be configured to suit individual analysis experts, definitions of analysis flow, information gathering and report generation. The report generated by the system summarizes all critical elements of a given web page and provides case specific hints to the page author and thus forms a new type of service. Regarding the analysis subsystems, Aware mainly builds on existing state-of-the-art technologies. Their usability has been evaluated for each intended task. In order to control the heterogeneous analysis components and to gather the information, a lightweight scripting shell has been developed. This paper describes the analysis technologies, ranging from text based information extraction, over optical character recognition and phonetic fuzzy string matching to a set of image analysis and retrieval tools; as well as the scripting language to define the analysis flow.

Economic analysis of complementary, alternative, and integrative medicine: considerations raised by an expert panel.

PubMed

Coulter, Ian D; Herman, Patricia M; Nataraj, Shanthi

2013-07-25

An international panel of experts was convened to examine the challenges faced in conducting economic analyses of Complementary, Alternative and Integrative Medicine (CAIM). A one and a half-day panel of experts was convened in early 2011 to discuss what was needed to bring about robust economic analysis of CAIM. The goals of the expert panel were to review the current state of the science of economic evaluations in health, and to discuss the issues involved in applying these methods to CAIM, recognizing its unique characteristics. The panel proceedings were audiotaped and a thematic analysis was conducted independently by two researchers. The results were then discussed and differences resolved. This manuscript summarizes the discussions held by the panel members on each theme. The panel identified seven major themes regarding economic evaluation that are particularly salient to determining the economics of CAIM: standardization (in order to compare CAIM with conventional therapies, the same basic economic evaluation methods and framework must be used); identifying the question being asked, the audience targeted for the results and whose perspective is being used (e.g., the patient perspective is especially relevant to CAIM because of the high level of self-referral and out-of-pocket payment); the analytic methods to be used (e.g., the importance of treatment description and fidelity); the outcomes to be measured (e.g., it is important to consider a broad range of outcomes, particularly for CAIM therapies, which often treat the whole person rather than a specific symptom or disease); costs (e.g., again because of treating the whole person, the impact of CAIM on overall healthcare costs, rather than only disease-specific costs, should be measured); implementation (e.g., highlighting studies where CAIM allows cost savings may help offset its image as an "add on" cost); and generalizability (e.g., proper reporting can enable study results to be useful beyond the study sample). The business case for CAIM depends on economic analysis and standard methods for conducting such economic evaluations exist. The challenge for CAIM lies in appropriately applying these methods. The deliberations of this panel provide a list of factors to be considered in meeting that challenge.

Expert Approaches to Analysis

DTIC Science & Technology

1999-03-01

of epistemic forms and games , which can form the basis for building a tool to support expert analyses. 15. SUBJECT TERMS Expert analysis Epistemic...forms Epistemic games SECURITY CLASSIFICATION OF 16. REPORT Unclassified 17. ABSTRACT Unclassified 18. THIS PAGE Unclassified 19. LIMITATION OF...1998 Principal Investigators: Allan Collins & William Ferguson BBN Technologies Introduction 1 Prior Work 2 Structural-Analysis Games 2 Functional

First "glass" education: telementored cardiac ultrasonography using Google Glass- a pilot study.

PubMed

Russell, Patrick M; Mallin, Michael; Youngquist, Scott T; Cotton, Jennifer; Aboul-Hosn, Nael; Dawson, Matt

2014-11-01

The objective of this study was to determine the feasibility of telementored instruction in bedside ultrasonography (US) using Google Glass. The authors sought to examine whether first-time US users could obtain adequate parasternal long axis (PSLA) views to approximate ejection fraction (EF) using Google Glass telementoring. This was a prospective, randomized, single-blinded study. Eighteen second-year medical students were randomized into three groups and tasked with obtaining PSLA cardiac imaging. Group A received real-time telementored education through Google Glass via Google Hangout from a remotely located expert. Group B received bedside education from the same expert. Group C represented the control and received no instruction. Each subject was given 3 minutes to obtain a best PSLA cardiac imaging using a portable GE Vscan. Image clips obtained by each subject were stored. A second expert, blinded to instructional mode, evaluated images for adequacy and assigned an image quality rating on a 0 to 10 scale. Group A was able to obtain adequate images six out of six times (100%) with a median image quality rating of 7.5 (interquartile range [IQR] = 6 to 10) out of 10. Group B was also able to obtain adequate views six out of six times (100%), with a median image quality rating of 8 (IQR = 7 to 9). Group C was able to obtain adequate views one out of six times (17%), with a median image quality of 0 (IQR = 0 to 2). There were no statistically significant differences between Group A and Group B in the achievement of adequate images for E-point septal separation measurement or in image quality. In this pilot/feasibility study, novice US users were able to obtain adequate imaging to determine a healthy patient's EF through telementored education using Google Glass. These preliminary data suggest telementoring as an adequate means of medical education in bedside US. This conclusion will need to be validated with larger, more powerful studies including evaluation of pathologic findings and varying body habitus among models. © 2014 by the Society for Academic Emergency Medicine.

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

NASA Astrophysics Data System (ADS)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group

PubMed Central

Shenkin, Susan D.; Pernet, Cyril; Nichols, Thomas E.; Poline, Jean-Baptiste; Matthews, Paul M.; van der Lugt, Aad; Mackay, Clare; Lanyon, Linda; Mazoyer, Bernard; Boardman, James P.; Thompson, Paul M.; Fox, Nick; Marcus, Daniel S.; Sheikh, Aziz; Cox, Simon R.; Anblagan, Devasuda; Job, Dominic E.; Dickie, David Alexander; Rodriguez, David; Wardlaw, Joanna M.

2017-01-01

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining ‘normality’); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function. PMID:28232121

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group.

PubMed

Shenkin, Susan D; Pernet, Cyril; Nichols, Thomas E; Poline, Jean-Baptiste; Matthews, Paul M; van der Lugt, Aad; Mackay, Clare; Lanyon, Linda; Mazoyer, Bernard; Boardman, James P; Thompson, Paul M; Fox, Nick; Marcus, Daniel S; Sheikh, Aziz; Cox, Simon R; Anblagan, Devasuda; Job, Dominic E; Dickie, David Alexander; Rodriguez, David; Wardlaw, Joanna M

2017-06-01

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining 'normality'); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function. Copyright © 2017 Elsevier Inc. All rights reserved.

MR and CT data with multiobserver delineations of organs in the pelvic area-Part of the Gold Atlas project.

PubMed

Nyholm, Tufve; Svensson, Stina; Andersson, Sebastian; Jonsson, Joakim; Sohlin, Maja; Gustafsson, Christian; Kjellén, Elisabeth; Söderström, Karin; Albertsson, Per; Blomqvist, Lennart; Zackrisson, Björn; Olsson, Lars E; Gunnlaugsson, Adalsteinn

2018-03-01

We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT). T1- and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset. The dataset has been made publically available to be used for academic purposes, and can be accessed from https://zenodo.org/record/583096. The dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm. © 2018 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Clinical validation of different echocardiographic motion pictures expert group-4 algorythms and compression levels for telemedicine.

PubMed

Barbier, Paolo; Alimento, Marina; Berna, Giovanni; Cavoretto, Dario; Celeste, Fabrizio; Muratori, Manuela; Guazzi, Maurizio D

2004-01-01

Tele-echocardiography is not widely used because of lengthy transmission times when using standard Motion Pictures Expert Groups (MPEG)-2 lossy compression algorythms, unless expensive high bandwidth lines are used. We sought to validate the newer MPEG-4 algorythms to allow further reduction in echocardiographic motion video file size. Four cardiologists expert in echocardiography read blindly 165 randomized uncompressed and compressed 2D and color Doppler normal and pathologic motion images. One Digital Video and 3 MPEG-4 compression algorythms were tested, the latter at 3 decreasing compression quality levels (100%, 65% and 40%). Mean diagnostic and image quality scores were computed for each file and compared across the 3 compression levels using uncompressed files as controls. File dimensions decreased from a range of uncompressed 12-83 MB to MPEG-4 0.03-2.3 MB. All algorythms showed mean scores that were not significantly different from uncompressed source, except the MPEG-4 DivX algorythm at the highest selected compression (40%, p=.002). These data support the use of MPEG-4 compression to reduce echocardiographic motion image size for transmission purposes, allowing cost reduction through use of low bandwidth lines.

New telemedicine techniques in dermatology - evaluation with reflectance confocal microscopy via cloud-based platform.

PubMed

Łudzik, Joanna; Witkowski, Alexander Michael; Roterman-Konieczna, Irena

Dermoscopically equivocal skin lesions may present a diagnostic challenge in daily clinical practice and are regularly sent for second expert opinion. We present a new approach to handling these cases in a consultation referral system that enables communication between the initial doctor at the image upload site and dermatology experts at a distance via cloud-based telemedicine. In our study we retrospectively evaluated 100 equivocal cases with complete digital dermoscopy-reflectance confocal microscopy image sets and compared suggested management of the initial doctor to a second expert confocal reader. We evaluated the effect of reader concordance on final management of these lesions resulting in a single reader overall sensitivity of 89% and specificity of 66% and double reader concordance method sensitivity of 98% and specificity of 54%. In conclusion, we found that application of double reader evaluation of these image sets with automatic referral of lesions for removal in the case of discordant diagnosis between two doctors improved the sensitivity of diagnosis in this subset of lesions and may increase the safety threshold of management choice reducing potential misdiagnosis in telemedicine settings. This paper concerns the application of telemedicine in practical medicine.

Automatic co-segmentation of lung tumor based on random forest in PET-CT images

NASA Astrophysics Data System (ADS)

Jiang, Xueqing; Xiang, Dehui; Zhang, Bin; Zhu, Weifang; Shi, Fei; Chen, Xinjian

2016-03-01

In this paper, a fully automatic method is proposed to segment the lung tumor in clinical 3D PET-CT images. The proposed method effectively combines PET and CT information to make full use of the high contrast of PET images and superior spatial resolution of CT images. Our approach consists of three main parts: (1) initial segmentation, in which spines are removed in CT images and initial connected regions achieved by thresholding based segmentation in PET images; (2) coarse segmentation, in which monotonic downhill function is applied to rule out structures which have similar standardized uptake values (SUV) to the lung tumor but do not satisfy a monotonic property in PET images; (3) fine segmentation, random forests method is applied to accurately segment the lung tumor by extracting effective features from PET and CT images simultaneously. We validated our algorithm on a dataset which consists of 24 3D PET-CT images from different patients with non-small cell lung cancer (NSCLC). The average TPVF, FPVF and accuracy rate (ACC) were 83.65%, 0.05% and 99.93%, respectively. The correlation analysis shows our segmented lung tumor volumes has strong correlation ( average 0.985) with the ground truth 1 and ground truth 2 labeled by a clinical expert.

Expectations of medical specialists about image-based teleconsultation – A qualitative study on acute burns in South Africa

PubMed Central

Laflamme, Lucie; Mölsted Alvesson, Helle

2018-01-01

Background Image-based teleconsultation between medical experts and healthcare staff at remote emergency centres can improve the diagnosis of conditions which are challenging to assess. One such condition is burns. Knowledge is scarce regarding how medical experts perceive the influence of such teleconsultation on their roles and relations to colleagues at point of care. Methods In this qualitative study, semi-structured interviews were conducted with 15 medical experts to explore their expectations of a newly developed App for burns diagnostics and care prior to its implementation. Purposive sampling included male and female physicians at different stages of their career, employed at different referral hospitals and all potential future tele-experts in remote teleconsultation using the App. Positioning theory was used to analyse the data. Results The experts are already facing changes in their diagnostic practices due to the informal use of open access applications like WhatsApp. Additional changes are expected when the new App is launched. Four positions of medical experts were identified in situations of diagnostic advice, two related to patient flow–clinical specialist and gatekeeper–and two to point of care staff–educator and mentor. The experts move flexibly between the positions during diagnostic practices with remote colleagues. A new position in relation to previous research on medical roles–the mentor–came to light in this setting. The App is expected to have an important educational impact, streamline the diagnostic process, improve both triage and referrals and be a more secure option for remote diagnosis compared to current practices. Verbal communication is however expected to remain important for certain situations, in particular those related to the mentor position. Conclusion The quality and security of referrals are expected to be improved through the App but the medical experts see less potential for conveying moral support via the App during remote consultations. Experts’ reflections on remote consultations highlight the embedded social and cultural dimensions of implementing new technology. PMID:29543847

Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation.

PubMed

Wu, Jing; Philip, Ana-Maria; Podkowinski, Dominika; Gerendas, Bianca S; Langs, Georg; Simader, Christian; Waldstein, Sebastian M; Schmidt-Erfurth, Ursula M

2016-01-01

Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF) segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation, applicable to other pathological structures, is presented. Integral to this work is the dataset used which must be fit for purpose for IRF segmentation algorithm training and testing. We describe here a multivendor dataset comprised of 30 scans. Each OCT scan for system training has been annotated by multiple graders using a proprietary system. Evaluation of the intergrader annotations shows a good correlation, thus making the reproducibly annotated scans suitable for the training and validation of image processing and machine learning based segmentation methods. The dataset will be made publicly available in the form of a segmentation Grand Challenge.

Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation

PubMed Central

Wu, Jing; Philip, Ana-Maria; Podkowinski, Dominika; Gerendas, Bianca S.; Langs, Georg; Simader, Christian

2016-01-01

Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF) segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation, applicable to other pathological structures, is presented. Integral to this work is the dataset used which must be fit for purpose for IRF segmentation algorithm training and testing. We describe here a multivendor dataset comprised of 30 scans. Each OCT scan for system training has been annotated by multiple graders using a proprietary system. Evaluation of the intergrader annotations shows a good correlation, thus making the reproducibly annotated scans suitable for the training and validation of image processing and machine learning based segmentation methods. The dataset will be made publicly available in the form of a segmentation Grand Challenge. PMID:27579177

A European Sustainable Tourism Labels proposal using a composite indicator

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

Blancas, Francisco Javier, E-mail: fjblaper@upo.es; Lozano-Oyola, Macarena, E-mail: mlozoyo@upo.es; González, Mercedes, E-mail: m_gonzalez@uma.es

The tourism sector in Europe faces important challenges which it must deal with to promote its future development. In this context, the European Commission considers that two key issues must be addressed. On the one hand, a better base of socio-economic knowledge about tourism and its relationship with the environment is needed, and, on the other hand, it is necessary to improve the image of European areas as quality sustainable tourism destinations. In this paper we present analytical tools that cover these needs. Specifically, we define a system of sustainable tourism indicators and we obtain a composite indicator incorporating weightsmore » quantified using a panel of experts. Employing the values of this global indicator as a basis, we define a Sustainable Tourism Country-Brand Ranking which assesses the perception of each country-brand depending on its degree of sustainability, and a system of sustainable tourism labels which reward the management carried out. - Highlights: • We define a system of indicators to improve the knowledge about sustainable tourism. • We obtain composite indicators based on expert knowledge. • The Sustainable Tourism Country-Brand Ranking would improve the image of destinations. • We define a Sustainable Tourism Labels System to assess country-brands. • The conclusions of the empirical analysis can be extrapolated to other tourist areas.« less

[Development and Application of a Performance Prediction Model for Home Care Nursing Based on a Balanced Scorecard using the Bayesian Belief Network].

PubMed

Noh, Wonjung; Seomun, Gyeongae

2015-06-01

This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

WRIST: A WRist Image Segmentation Toolkit for carpal bone delineation from MRI.

PubMed

Foster, Brent; Joshi, Anand A; Borgese, Marissa; Abdelhafez, Yasser; Boutin, Robert D; Chaudhari, Abhijit J

2018-01-01

Segmentation of the carpal bones from 3D imaging modalities, such as magnetic resonance imaging (MRI), is commonly performed for in vivo analysis of wrist morphology, kinematics, and biomechanics. This crucial task is typically carried out manually and is labor intensive, time consuming, subject to high inter- and intra-observer variability, and may result in topologically incorrect surfaces. We present a method, WRist Image Segmentation Toolkit (WRIST), for 3D semi-automated, rapid segmentation of the carpal bones of the wrist from MRI. In our method, the boundary of the bones were iteratively found using prior known anatomical constraints and a shape-detection level set. The parameters of the method were optimized using a training dataset of 48 manually segmented carpal bones and evaluated on 112 carpal bones which included both healthy participants without known wrist conditions and participants with thumb basilar osteoarthritis (OA). Manual segmentation by two expert human observers was considered as a reference. On the healthy subject dataset we obtained a Dice overlap of 93.0 ± 3.8, Jaccard Index of 87.3 ± 6.2, and a Hausdorff distance of 2.7 ± 3.4 mm, while on the OA dataset we obtained a Dice overlap of 90.7 ± 8.6, Jaccard Index of 83.0 ± 10.6, and a Hausdorff distance of 4.0 ± 4.4 mm. The short computational time of 20.8 s per bone (or 5.1 s per bone in the parallelized version) and the high agreement with the expert observers gives WRIST the potential to be utilized in musculoskeletal research. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated Liver Elasticity Calculation for 3D MRE

PubMed Central

Dzyubak, Bogdan; Glaser, Kevin J.; Manduca, Armando; Ehman, Richard L.

2017-01-01

Magnetic Resonance Elastography (MRE) is a phase-contrast MRI technique which calculates quantitative stiffness images, called elastograms, by imaging the propagation of acoustic waves in tissues. It is used clinically to diagnose liver fibrosis. Automated analysis of MRE is difficult as the corresponding MRI magnitude images (which contain anatomical information) are affected by intensity inhomogeneity, motion artifact, and poor tissue- and edge-contrast. Additionally, areas with low wave amplitude must be excluded. An automated algorithm has already been successfully developed and validated for clinical 2D MRE. 3D MRE acquires substantially more data and, due to accelerated acquisition, has exacerbated image artifacts. Also, the current 3D MRE processing does not yield a confidence map to indicate MRE wave quality and guide ROI selection, as is the case in 2D. In this study, extension of the 2D automated method, with a simple wave-amplitude metric, was developed and validated against an expert reader in a set of 57 patient exams with both 2D and 3D MRE. The stiffness discrepancy with the expert for 3D MRE was −0.8% ± 9.45% and was better than discrepancy with the same reader for 2D MRE (−3.2% ± 10.43%), and better than the inter-reader discrepancy observed in previous studies. There were no automated processing failures in this dataset. Thus, the automated liver elasticity calculation (ALEC) algorithm is able to calculate stiffness from 3D MRE data with minimal bias and good precision, while enabling stiffness measurements to be fully reproducible and to be easily performed on the large 3D MRE datasets. PMID:29033488

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.

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

The Effects of Peer-Like and Expert-Like Pedagogical Agents on Learners' Agent Perceptions, Task-Related Attitudes, and Learning Achievement

ERIC Educational Resources Information Center

Liew, Tze Wei; Tan, Su-Mae; Jayothisa, Chandrika

2013-01-01

The present study examined the impact of peer-like and expert-like agent stereotypes, as operationalized by agent's image and voice, on learners' agent perceptions, task-related attitudes, and learning achievement. 56 university freshmen (23 males and 33 females) interacted with either the peer-like agent (female college student) or the…

What to Tell the Public? Information Design as Interpretation in Corridor Planning

ERIC Educational Resources Information Center

Lebeaux, Pamela M.

2012-01-01

Providing information to the public is a widely recognized function of planning. Yet little attention has been paid to how expert information is characterized for citizens participating in a planning process. The text, maps and images used to tell the story in a planning process can help to bridge the divide between experts and citizens, or act to…

Seeing Fluid Physics via Visual Expertise Training

NASA Astrophysics Data System (ADS)

Hertzberg, Jean; Goodman, Katherine; Curran, Tim

2016-11-01

In a course on Flow Visualization, students often expressed that their perception of fluid flows had increased, implying the acquisition of a type of visual expertise, akin to that of radiologists or dog show judges. In the first steps towards measuring this expertise, we emulated an experimental design from psychology. The study had two groups of participants: "novices" with no formal fluids education, and "experts" who had passed as least one fluid mechanics course. All participants were trained to place static images of fluid flows into two categories (laminar and turbulent). Half the participants were trained on flow images with a specific format (Von Kármán vortex streets), and the other half on a broader group. Novices' results were in line with past perceptual expertise studies, showing that it is easier to transfer learning from a broad category to a new specific format than vice versa. In contrast, experts did not have a significant difference between training conditions, suggesting the experts did not undergo the same learning process as the novices. We theorize that expert subjects were able to access their conceptual knowledge about fluids to perform this new, visual task. This finding supports new ways of understanding conceptual learning.

Semiautomated analysis of embryoscope images: Using localized variance of image intensity to detect embryo developmental stages.

PubMed

Mölder, Anna; Drury, Sarah; Costen, Nicholas; Hartshorne, Geraldine M; Czanner, Silvester

2015-02-01

Embryo selection in in vitro fertilization (IVF) treatment has traditionally been done manually using microscopy at intermittent time points during embryo development. Novel technique has made it possible to monitor embryos using time lapse for long periods of time and together with the reduced cost of data storage, this has opened the door to long-term time-lapse monitoring, and large amounts of image material is now routinely gathered. However, the analysis is still to a large extent performed manually, and images are mostly used as qualitative reference. To make full use of the increased amount of microscopic image material, (semi)automated computer-aided tools are needed. An additional benefit of automation is the establishment of standardization tools for embryo selection and transfer, making decisions more transparent and less subjective. Another is the possibility to gather and analyze data in a high-throughput manner, gathering data from multiple clinics and increasing our knowledge of early human embryo development. In this study, the extraction of data to automatically select and track spatio-temporal events and features from sets of embryo images has been achieved using localized variance based on the distribution of image grey scale levels. A retrospective cohort study was performed using time-lapse imaging data derived from 39 human embryos from seven couples, covering the time from fertilization up to 6.3 days. The profile of localized variance has been used to characterize syngamy, mitotic division and stages of cleavage, compaction, and blastocoel formation. Prior to analysis, focal plane and embryo location were automatically detected, limiting precomputational user interaction to a calibration step and usable for automatic detection of region of interest (ROI) regardless of the method of analysis. The results were validated against the opinion of clinical experts. © 2015 International Society for Advancement of Cytometry. © 2015 International Society for Advancement of Cytometry.

A new machine classification method applied to human peripheral blood leukocytes

NASA Technical Reports Server (NTRS)

Rorvig, Mark E.; Fitzpatrick, Steven J.; Vitthal, Sanjay; Ladoulis, Charles T.

1994-01-01

Human beings judge images by complex mental processes, whereas computing machines extract features. By reducing scaled human judgments and machine extracted features to a common metric space and fitting them by regression, the judgments of human experts rendered on a sample of images may be imposed on an image population to provide automatic classification.

iRENEX: a clinically informed decision support system for the interpretation of ⁹⁹mTc-MAG3 scans to detect renal obstruction.

PubMed

Garcia, Ernest V; Taylor, Andrew; Folks, Russell; Manatunga, Daya; Halkar, Raghuveer; Savir-Baruch, Bital; Dubovsky, Eva

2012-09-01

Decision support systems for imaging analysis and interpretation are rapidly being developed and will have an increasing impact on the practice of medicine. RENEX is a renal expert system to assist physicians evaluate suspected obstruction in patients undergoing mercaptoacetyltriglycine (MAG3) renography. RENEX uses quantitative parameters extracted from the dynamic renal scan data using QuantEM™II and heuristic rules in the form of a knowledge base gleaned from experts to determine if a kidney is obstructed; however, RENEX does not have access to and could not consider the clinical information available to diagnosticians interpreting these studies. We designed and implemented a methodology to incorporate clinical information into RENEX, implemented motion detection and evaluated this new comprehensive system (iRENEX) in a pilot group of 51 renal patients. To reach a conclusion as to whether a kidney is obstructed, 56 new clinical rules were added to the previously reported 60 rules used to interpret quantitative MAG3 parameters. All the clinical rules were implemented after iRENEX reached a conclusion on obstruction based on the quantitative MAG3 parameters, and the evidence of obstruction was then modified by the new clinical rules. iRENEX consisted of a library to translate parameter values to certainty factors, a knowledge base with 116 heuristic interpretation rules, a forward chaining inference engine to determine obstruction and a justification engine. A clinical database was developed containing patient histories and imaging report data obtained from the hospital information system associated with the pertinent MAG3 studies. The system was fine-tuned and tested using a pilot group of 51 patients (21 men, mean age 58.2 ± 17.1 years, 100 kidneys) deemed by an expert panel to have 61 unobstructed and 39 obstructed kidneys. iRENEX, using only quantitative MAG3 data agreed with the expert panel in 87 % (34/39) of obstructed and 90 % (55/61) of unobstructed kidneys. iRENEX, using both quantitative and clinical data agreed with the expert panel in 95 % (37/39) of obstructed and 92 % (56/61) of unobstructed kidneys. The clinical information significantly (p < 0.001) increased iRENEX certainty in detecting obstruction over using the quantitative data alone. Our renal expert system for detecting renal obstruction has been substantially expanded to incorporate the clinical information available to physicians as well as advanced quality control features and was shown to interpret renal studies in a pilot group at a standardized expert level. These encouraging results warrant a prospective study in a large population of patients with and without renal obstruction to establish the diagnostic performance of iRENEX.

Repeatability of Diagnostic Features and Scoring Systems for Hepatocellular Carcinoma by Using MR Imaging

PubMed Central

Khalatbari, Shokoufeh; Liu, Peter S. C.; Maturen, Katherine E.; Kaza, Ravi K.; Wasnik, Ashish P.; Al-Hawary, Mahmoud M.; Glazer, Daniel I.; Stein, Erica B.; Patel, Jeet; Somashekar, Deepak K.; Viglianti, Benjamin L.; Hussain, Hero K.

2014-01-01

Purpose To determine for expert and novice radiologists repeatability of major diagnostic features and scoring systems (ie, Liver Imaging Reporting and Data System [LI-RADS], Organ Procurement and Transplantation Network [OPTN], and American Association for the Study of Liver Diseases [AASLD]) for hepatocellular carcinoma (HCC) by using magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained and patient consent was waived for this HIPAA-compliant, retrospective study. The LI-RADS discussed in this article refers to version 2013.1. Ten blinded readers reviewed 100 liver MR imaging studies that demonstrated observations preliminarily assigned LI-RADS scores of LR1–LR5. Diameter and major HCC features (arterial hyperenhancement, washout appearance, pseudocapsule) were recorded for each observation. LI-RADS, OPTN, and AASLD scores were assigned. Interreader agreement was assessed by using intraclass correlation coefficients and κ statistics. Scoring rates were compared by using McNemar test. Results Overall interreader agreement was substantial for arterial hyperenhancement (0.67 [95% confidence interval {CI}: 0.65, 0.69]), moderate for washout appearance (0.48 [95%CI: 0.46, 0.50]), moderate for pseudocapsule (0.52 [95% CI: 050, 0.54]), fair for LI-RADS (0.35 [95% CI: 0.34, 0.37]), fair for AASLD (0.39 [95% CI: 0.37, 0.42]), and moderate for OPTN (0.53 [95% CI: 0.51, 0.56]). Agreement for measured diameter was almost perfect (range, 0.95–0.97). There was substantial agreement for most scores consistent with HCC. Experts agreed significantly more than did novices and were significantly more likely than were novices to assign a diagnosis of HCC (P < .001). Conclusion Two of three major features for HCC (washout appearance and pseudocapsule) have only moderate interreader agreement. Experts and novices who assigned scores consistent with HCC had substantial but not perfect agreement. Expert agreement is substantial for OPTN, but moderate for LI-RADS and AASLD. Novices were less consistent and less likely to diagnose HCC than were experts. © RSNA, 2014 Online supplemental material is available for this article. PMID:24555636

A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging

PubMed Central

2013-01-01

Background Infectious diseases are the second leading cause of death worldwide. In order to better understand and treat them, an accurate evaluation using multi-modal imaging techniques for anatomical and functional characterizations is needed. For non-invasive imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), there have been many engineering improvements that have significantly enhanced the resolution and contrast of the images, but there are still insufficient computational algorithms available for researchers to use when accurately quantifying imaging data from anatomical structures and functional biological processes. Since the development of such tools may potentially translate basic research into the clinic, this study focuses on the development of a quantitative and qualitative image analysis platform that provides a computational radiology perspective for pulmonary infections in small animal models. Specifically, we designed (a) a fast and robust automated and semi-automated image analysis platform and a quantification tool that can facilitate accurate diagnostic measurements of pulmonary lesions as well as volumetric measurements of anatomical structures, and incorporated (b) an image registration pipeline to our proposed framework for volumetric comparison of serial scans. This is an important investigational tool for small animal infectious disease models that can help advance researchers’ understanding of infectious diseases. Methods We tested the utility of our proposed methodology by using sequentially acquired CT and PET images of rabbit, ferret, and mouse models with respiratory infections of Mycobacterium tuberculosis (TB), H1N1 flu virus, and an aerosolized respiratory pathogen (necrotic TB) for a total of 92, 44, and 24 scans for the respective studies with half of the scans from CT and the other half from PET. Institutional Administrative Panel on Laboratory Animal Care approvals were obtained prior to conducting this research. First, the proposed computational framework registered PET and CT images to provide spatial correspondences between images. Second, the lungs from the CT scans were segmented using an interactive region growing (IRG) segmentation algorithm with mathematical morphology operations to avoid false positive (FP) uptake in PET images. Finally, we segmented significant radiotracer uptake from the PET images in lung regions determined from CT and computed metabolic volumes of the significant uptake. All segmentation processes were compared with expert radiologists’ delineations (ground truths). Metabolic and gross volume of lesions were automatically computed with the segmentation processes using PET and CT images, and percentage changes in those volumes over time were calculated. (Continued on next page)(Continued from previous page) Standardized uptake value (SUV) analysis from PET images was conducted as a complementary quantitative metric for disease severity assessment. Thus, severity and extent of pulmonary lesions were examined through both PET and CT images using the aforementioned quantification metrics outputted from the proposed framework. Results Each animal study was evaluated within the same subject class, and all steps of the proposed methodology were evaluated separately. We quantified the accuracy of the proposed algorithm with respect to the state-of-the-art segmentation algorithms. For evaluation of the segmentation results, dice similarity coefficient (DSC) as an overlap measure and Haussdorf distance as a shape dissimilarity measure were used. Significant correlations regarding the estimated lesion volumes were obtained both in CT and PET images with respect to the ground truths (R2=0.8922,p<0.01 and R2=0.8664,p<0.01, respectively). The segmentation accuracy (DSC (%)) was 93.4±4.5% for normal lung CT scans and 86.0±7.1% for pathological lung CT scans. Experiments showed excellent agreements (all above 85%) with expert evaluations for both structural and functional imaging modalities. Apart from quantitative analysis of each animal, we also qualitatively showed how metabolic volumes were changing over time by examining serial PET/CT scans. Evaluation of the registration processes was based on precisely defined anatomical landmark points by expert clinicians. An average of 2.66, 3.93, and 2.52 mm errors was found in rabbit, ferret, and mouse data (all within the resolution limits), respectively. Quantitative results obtained from the proposed methodology were visually related to the progress and severity of the pulmonary infections as verified by the participating radiologists. Moreover, we demonstrated that lesions due to the infections were metabolically active and appeared multi-focal in nature, and we observed similar patterns in the CT images as well. Consolidation and ground glass opacity were the main abnormal imaging patterns and consistently appeared in all CT images. We also found that the gross and metabolic lesion volume percentage follow the same trend as the SUV-based evaluation in the longitudinal analysis. Conclusions We explored the feasibility of using PET and CT imaging modalities in three distinct small animal models for two diverse pulmonary infections. We concluded from the clinical findings, derived from the proposed computational pipeline, that PET-CT imaging is an invaluable hybrid modality for tracking pulmonary infections longitudinally in small animals and has great potential to become routinely used in clinics. Our proposed methodology showed that automated computed-aided lesion detection and quantification of pulmonary infections in small animal models are efficient and accurate as compared to the clinical standard of manual and semi-automated approaches. Automated analysis of images in pre-clinical applications can increase the efficiency and quality of pre-clinical findings that ultimately inform downstream experimental design in human clinical studies; this innovation will allow researchers and clinicians to more effectively allocate study resources with respect to research demands without compromising accuracy. PMID:23879987

Improving the quality of preclinical research echocardiography: Observations, training and guidelines for measurement.

PubMed

Donner, Daniel G; Kiriazis, Helen; Du, Xiao-Jun; Marwick, Thomas H; McMullen, Julie R

2018-04-20

Informal training in preclinical research may be a contributor to the poor reproducibility of preclinical cardiology research and low rates of translation into clinical research and practice. Mouse echocardiography is a widely used technique to assess cardiac structure and function in drug intervention studies using disease models. The inter-observer variability (IOV) of clinical echocardiographic measurements has been shown to improve with formalized training, but preclinical echocardiography lacks similarly critical standardization of training. The aims of this investigation were to assess the IOV of echocardiographic measurements from studies in mice, and address any technical impediments to reproducibility by implementing standardized guidelines through formalized training. In this prospective, single-site, observational cohort study, 13 scientists performing preclinical echocardiographic image analysis were assessed for measurement of short-axis M-mode-derived dimensions and calculated left ventricular mass (LVMass). Ten M-mode images of mouse hearts acquired and analyzed by an expert researcher with a spectrum of LVMass were selected for assessment, and validated by autopsy weight. Following the initial observation, a structured formal training program was introduced, and accuracy and reproducibility were re-evaluated. Mean absolute percentage error (MAPE) for Expert-calculated LVMass was 6{plus minus}4% compared to autopsy LVMass, and 25{plus minus}21% for participants before training. Standardized formal training improved participant MAPE by approximately 30% relative to expert-calculated LVMass (p<0.001). Participants initially categorized with high-range error (25-45%) improved to low-moderate error ranges (<15-25%). This report reveals an example of technical skill training insufficiency likely endemic to preclinical research and provides validated guidelines for echocardiographic measurement for adaptation to formalized in-training programs.

Comparison of 3T and 7T susceptibility-weighted angiography of the substantia nigra in diagnosing Parkinson disease.

PubMed

Cosottini, M; Frosini, D; Pesaresi, I; Donatelli, G; Cecchi, P; Costagli, M; Biagi, L; Ceravolo, R; Bonuccelli, U; Tosetti, M

2015-03-01

Standard neuroimaging fails in defining the anatomy of the substantia nigra and has a marginal role in the diagnosis of Parkinson disease. Recently 7T MR target imaging of the substantia nigra has been useful in diagnosing Parkinson disease. We performed a comparative study to evaluate whether susceptibility-weighted angiography can diagnose Parkinson disease with a 3T scanner. Fourteen patients with Parkinson disease and 13 healthy subjects underwent MR imaging examination at 3T and 7T by using susceptibility-weighted angiography. Two expert blinded observers and 1 neuroradiology fellow evaluated the 3T and 7T images of the sample to identify substantia nigra abnormalities indicative of Parkinson disease. Diagnostic accuracy and intra- and interobserver agreement were calculated separately for 3T and 7T acquisitions. Susceptibility-weighted angiography 7T MR imaging can diagnose Parkinson disease with a mean sensitivity of 93%, specificity of 100%, and diagnostic accuracy of 96%. 3T MR imaging diagnosed Parkinson disease with a mean sensitivity of 79%, specificity of 94%, and diagnostic accuracy of 86%. Intraobserver and interobserver agreement was excellent at 7T. At 3T, intraobserver agreement was excellent for experts, and interobserver agreement ranged between good and excellent. The less expert reader obtained a diagnostic accuracy of 89% at 3T. Susceptibility-weighted angiography images obtained at 3T and 7T differentiate controls from patients with Parkinson disease with a higher diagnostic accuracy at 7T. The capability of 3T in diagnosing Parkinson disease might encourage its use in clinical practice. The use of the more accurate 7T should be supported by a dedicated cost-effectiveness study. © 2015 by American Journal of Neuroradiology.

An Electronic Tree Inventory for Arboriculture Management

NASA Astrophysics Data System (ADS)

Tait, Roger J.; Allen, Tony J.; Sherkat, Nasser; Bellett-Travers, Marcus D.

The integration of Global Positioning System (GPS) technology into mobile devices provides them with an awareness of their physical location. This geospatial context can be employed in a wide range of applications including locating nearby places of interest as well as guiding emergency services to incidents. In this research, a GPS-enabled Personal Digital Assistant (PDA) is used to create a computerised tree inventory for the management of arboriculture. Using the General Packet Radio Service (GPRS), GPS information and arboreal image data are sent to a web-server. An office-based PC running customised Geographical Information Software (GIS) then automatically retrieves the GPS tagged image data for display and analysis purposes. The resulting application allows an expert user to view the condition of individual trees in greater detail than is possible using remotely sensed imagery.

Development of an Expert Judgement Elicitation and Calibration Methodology for Risk Analysis in Conceptual Vehicle Design

NASA Technical Reports Server (NTRS)

Unal, Resit; Keating, Charles; Conway, Bruce; Chytka, Trina

2004-01-01

A comprehensive expert-judgment elicitation methodology to quantify input parameter uncertainty and analysis tool uncertainty in a conceptual launch vehicle design analysis has been developed. The ten-phase methodology seeks to obtain expert judgment opinion for quantifying uncertainties as a probability distribution so that multidisciplinary risk analysis studies can be performed. The calibration and aggregation techniques presented as part of the methodology are aimed at improving individual expert estimates, and provide an approach to aggregate multiple expert judgments into a single probability distribution. The purpose of this report is to document the methodology development and its validation through application to a reference aerospace vehicle. A detailed summary of the application exercise, including calibration and aggregation results is presented. A discussion of possible future steps in this research area is given.

Dog Experts' Brains Distinguish Socially Relevant Body Postures Similarly in Dogs and Humans

PubMed Central

Kujala, Miiamaaria V.; Kujala, Jan; Carlson, Synnöve; Hari, Riitta

2012-01-01

We read conspecifics' social cues effortlessly, but little is known about our abilities to understand social gestures of other species. To investigate the neural underpinnings of such skills, we used functional magnetic resonance imaging to study the brain activity of experts and non-experts of dog behavior while they observed humans or dogs either interacting with, or facing away from a conspecific. The posterior superior temporal sulcus (pSTS) of both subject groups dissociated humans facing toward each other from humans facing away, and in dog experts, a distinction also occurred for dogs facing toward vs. away in a bilateral area extending from the pSTS to the inferior temporo-occipital cortex: the dissociation of dog behavior was significantly stronger in expert than control group. Furthermore, the control group had stronger pSTS responses to humans than dogs facing toward a conspecific, whereas in dog experts, the responses were of similar magnitude. These findings suggest that dog experts' brains distinguish socially relevant body postures similarly in dogs and humans. PMID:22720054

Developing a Web-Based Advisory Expert System for Implementing Traffic Calming Strategies

PubMed Central

Falamarzi, Amir; Borhan, Muhamad Nazri; Rahmat, Riza Atiq O. K.

2014-01-01

Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented. PMID:25276861

Developing a web-based advisory expert system for implementing traffic calming strategies.

PubMed

Falamarzi, Amir; Borhan, Muhamad Nazri; Rahmat, Riza Atiq O K

2014-01-01

Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented.

Training models of anatomic shape variability

PubMed Central

Merck, Derek; Tracton, Gregg; Saboo, Rohit; Levy, Joshua; Chaney, Edward; Pizer, Stephen; Joshi, Sarang

2008-01-01

Learning probability distributions of the shape of anatomic structures requires fitting shape representations to human expert segmentations from training sets of medical images. The quality of statistical segmentation and registration methods is directly related to the quality of this initial shape fitting, yet the subject is largely overlooked or described in an ad hoc way. This article presents a set of general principles to guide such training. Our novel method is to jointly estimate both the best geometric model for any given image and the shape distribution for the entire population of training images by iteratively relaxing purely geometric constraints in favor of the converging shape probabilities as the fitted objects converge to their target segmentations. The geometric constraints are carefully crafted both to obtain legal, nonself-interpenetrating shapes and to impose the model-to-model correspondences required for useful statistical analysis. The paper closes with example applications of the method to synthetic and real patient CT image sets, including same patient male pelvis and head and neck images, and cross patient kidney and brain images. Finally, we outline how this shape training serves as the basis for our approach to IGRT∕ART. PMID:18777919

Visual versus semi-quantitative analysis of 18F-FDG-PET in amnestic MCI: an European Alzheimer's Disease Consortium (EADC) project.

PubMed

Morbelli, Silvia; Brugnolo, Andrea; Bossert, Irene; Buschiazzo, Ambra; Frisoni, Giovanni B; Galluzzi, Samantha; van Berckel, Bart N M; Ossenkoppele, Rik; Perneczky, Robert; Drzezga, Alexander; Didic, Mira; Guedj, Eric; Sambuceti, Gianmario; Bottoni, Gianluca; Arnaldi, Dario; Picco, Agnese; De Carli, Fabrizio; Pagani, Marco; Nobili, Flavio

2015-01-01

We aimed to investigate the accuracy of FDG-PET to detect the Alzheimer's disease (AD) brain glucose hypometabolic pattern in 142 patients with amnestic mild cognitive impairment (aMCI) and 109 healthy controls. aMCI patients were followed for at least two years or until conversion to dementia. Images were evaluated by means of visual read by either moderately-skilled or expert readers, and by means of a summary metric of AD-like hypometabolism (PALZ score). Seventy-seven patients converted to AD-dementia after 28.6 ± 19.3 months of follow-up. Expert reading was the most accurate tool to detect these MCI converters from healthy controls (sensitivity 89.6%, specificity 89.0%, accuracy 89.2%) while two moderately-skilled readers were less (p < 0.05) specific (sensitivity 85.7%, specificity 79.8%, accuracy 82.3%) and PALZ score was less (p < 0.001) sensitive (sensitivity 62.3%, specificity 91.7%, accuracy 79.6%). Among the remaining 67 aMCI patients, 50 were confirmed as aMCI after an average of 42.3 months, 12 developed other dementia, and 3 reverted to normalcy. In 30/50 persistent MCI patients, the expert recognized the AD hypometabolic pattern. In 13/50 aMCI, both the expert and PALZ score were negative while in 7/50, only the PALZ score was positive due to sparse hypometabolic clusters mainly in frontal lobes. Visual FDG-PET reads by an expert is the most accurate method but an automated, validated system may be particularly helpful to moderately-skilled readers because of high specificity, and should be mandatory when even a moderately-skilled reader is unavailable.

Linked color imaging improves the visibility of colorectal polyps: a video study

PubMed Central

Yoshida, Naohisa; Naito, Yuji; Murakami, Takaaki; Hirose, Ryohei; Ogiso, Kiyoshi; Inada, Yutaka; Dohi, Osamu; Kamada, Kazuhiro; Uchiyama, Kazuhiko; Handa, Osamu; Konishi, Hideyuki; Siah, Kewin Tien Ho; Yagi, Nobuaki; Fujita, Yasuko; Kishimoto, Mitsuo; Yanagisawa, Akio; Itoh, Yoshito

2017-01-01

Background/study aim  Linked color imaging (LCI) by a laser endoscope (Fujifilm Co, Tokyo, Japan) is a novel narrow band light observation. In this study, we aimed to investigate whether LCI could improve the visibility of colorectal polyps using endoscopic videos. Patients and methods  We prospectively recorded videos of consecutive polyps 2 – 20 mm in size diagnosed as neoplastic polyps. Three videos, white light (WL), blue laser imaging (BLI)-bright, and LCI, were recorded for each polyp by one expert. After excluding inappropriate videos, all videos were evaluated in random order by two experts and two non-experts according to a published polyp visibility score from four (excellent visibility) to one (poor visibility). Additionally, the relationship between polyp visibility scores in LCI and various clinical characteristics including location, size, histology, morphology, and preparation were analyzed compared to WL and BLI-bright. Results  We analyzed 101 colorectal polyps (94 neoplastic) in 66 patients (303 videos). The mean polyp size was 9.0 ± 8.1 mm and 54 polyps were non-polypoid. The mean polyp visibility scores for LCI (2.86 ± 1.08) were significantly higher than for WL and BLI-bright (2.53 ± 1.15, P  < 0.001; 2.73 ± 1.47, P  < 0.041). The ratio of poor visibility (score 1 and 2) was significantly lower in LCI for experts and non-experts (35.6 %, 33.6 %) compared with WL (49.6 %, P  = 0.015, 50.5 %, P  = 0.046). The polyp visibility scores for LCI were significantly higher than those for WL for all of the factors. With respect to the comparison between BLI-bright and WL, the polyp visibility scores for BLI-bright were not higher than WL for right-sided location, < 10 mm size, sessile serrated adenoma and polyp histology, and poor preparation. For those characteristics, LCI improved the lesions with right-sided location, SSA/P histology, and poor preparation significantly better than BLI. Conclusions  LCI improved polyp visibility compared to WL for both expert and non-expert endoscopists. It is useful for improving polyp visibility in any location, any size, any morphology, any histology, and any preparation level. PMID:28596985

Developing an interactive teleradiology system for SARS diagnosis

NASA Astrophysics Data System (ADS)

Sun, Jianyong; Zhang, Jianguo; Zhuang, Jun; Chen, Xiaomeng; Yong, Yuanyuan; Tan, Yongqiang; Chen, Liu; Lian, Ping; Meng, Lili; Huang, H. K.

2004-04-01

Severe acute respiratory syndrome (SARS) is a respiratory illness that had been reported in Asia, North America, and Europe in last spring. Most of the China cases of SARS have occurred by infection in hospitals or among travelers. To protect the physicians, experts and nurses from the SARS during the diagnosis and treatment procedures, the infection control mechanisms were built in SARS hospitals. We built a Web-based interactive teleradiology system to assist the radiologists and physicians both in side and out side control area to make image diagnosis. The system consists of three major components: DICOM gateway (GW), Web-based image repository server (Server), and Web-based DICOM viewer (Viewer). This system was installed and integrated with CR, CT and the hospital information system (HIS) in Shanghai Xinhua hospital to provide image-based ePR functions for SARS consultation between the radiologists, physicians and experts inside and out side control area. The both users inside and out side the control area can use the system to process and manipulate the DICOM images interactively, and the system provide the remote control mechanism to synchronize their operations on images and display.

Interactive tele-radiological segmentation systems for treatment and diagnosis.

PubMed

Zimeras, S; Gortzis, L G

2012-01-01

Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

Update Of The ACR-NEMA Standard Committee

NASA Astrophysics Data System (ADS)

Wang, Yen; Best, D. E.; Morse, R. R.; Horii, S. C.; Lehr, J. L.; Lodwick, G. S.; Fuscoe, C.; Nelson, O. L.; Perry, J. R.; Thompson, B. G.; Wessell, W. R.

1988-06-01

In January, 1984, the American College of Radiology (ACR) representing the users of imaging equipment and the National Electrical Manufacturers Association (NEMA) representing the manufacturers of imaging equipment joined forces to create a committee that could solve the compatibility issues surrounding the exchange of digital medical images. This committee, the ACR-NEMA Digital Imaging and Communication Standards Committee was composed of radiologists and experts from industry who addressed the problems involved in interfacing different digital imaging modalities. In just two years, the committee and three of its working groups created an industry standard interface, ACR-NEMA Digital Imaging and Communications Standard, Publication No. 300-1985. The ACR-NEMA interface allows digital medical images and related information to be communicated between different imaging devices, regardless of manufacturer or use of differing image formats. The interface is modeled on the International Standards Organization's Open Systems Interconnection sever-layer reference model. It is believed that the development of the Interface was the first step in the development of standards for Medical Picture Archiving and Communications Systems (PACS). Developing the interface Standard has required intensive technical analysis and examination of the future trends for digital imaging in order to design a model which would not be quickly outmoded. To continue the enhancement and future development of image management systems, various working groups have been created under the direction of the ACR-NEMA Committee.

The necessity of repeated assessment of imaging studies contained in medical records in medico-legal opinions.

PubMed

Borowska-Solonynko, A

The paper presents two case reports of living victims, in which imaging studies of the chest conducted at a medical facility were an essential part of the medico-legal opinion. The first case was that of a young male hospitalized due to CT evidence of bilateral rib fractions, who claimed to have been assaulted by police officers. The other case was that of a six week old baby hospitalized due to chest X-ray evidence of right hand side rib fractions. The chest X-ray was performed due to one bruise found on the baby's forehead and two small bruises on the back, which gave rise to suspicions of child abuse. In both cases, expert witnesses in radiology definitively excluded the presence of any fractures. These cases indicate that a new assessment of imaging studies contained in medical records is needed. Expert opinions based solely on the description of imaging studies may result in grave consequences.

Reliability of Classifying Multiple Sclerosis Disease Activity Using Magnetic Resonance Imaging in a Multiple Sclerosis Clinic

PubMed Central

Altay, Ebru Erbayat; Fisher, Elizabeth; Jones, Stephen E.; Hara-Cleaver, Claire; Lee, Jar-Chi; Rudick, Richard A.

2013-01-01

Objective To assess the reliability of new magnetic resonance imaging (MRI) lesion counts by clinicians in a multiple sclerosis specialty clinic. Design An observational study. Setting A multiple sclerosis specialty clinic. Patients Eighty-five patients with multiple sclerosis participating in a National Institutes of Health–supported longitudinal study were included. Intervention Each patient had a brain MRI scan at entry and 6 months later using a standardized protocol. Main Outcome Measures The number of new T2 lesions, newly enlarging T2 lesions, and gadolinium-enhancing lesions were measured on the 6-month MRI using a computer-based image analysis program for the original study. For this study, images were reanalyzed by an expert neuroradiologist and 3 clinician raters. The neuroradiologist evaluated the original image pairs; the clinicians evaluated image pairs that were modified to simulate clinical practice. New lesion counts were compared across raters, as was classification of patients as MRI active or inactive. Results Agreement on lesion counts was highest for gadolinium-enhancing lesions, intermediate for new T2 lesions, and poor for enlarging T2 lesions. In 18% to 25% of the cases, MRI activity was classified differently by the clinician raters compared with the neuroradiologist or computer program. Variability among the clinical raters for estimates of new T2 lesions was affected most strongly by the image modifications that simulated low image quality and different head position. Conclusions Between-rater variability in new T2 lesion counts may be reduced by improved standardization of image acquisitions, but this approach may not be practical in most clinical environments. Ultimately, more reliable, robust, and accessible image analysis methods are needed for accurate multiple sclerosis disease-modifying drug monitoring and decision making in the routine clinic setting. PMID:23599930

ARCOCT: Automatic detection of lumen border in intravascular OCT images.

PubMed

Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

2017-11-01

Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border detection in OCT images. Copyright © 2017 Elsevier B.V. All rights reserved.

When should video be added to conventional urodynamics in adults and is it justified by the evidence? ICI-RS 2014.

PubMed

Anding, Ralf; Rosier, Peter; Smith, Phillip; Gammie, Andrew; Giarenis, Ilias; Rantell, Angela; Thiruchelvam, Nikesh; Arlandis, Salvador; Cardozo, Linda

2016-02-01

To debate and evaluate the evidence base regarding the added value of video to urodynamics in adults and to define research questions. In the ICI-RS Meeting 2014 a Think Tank analyzed the current guidelines recommending video urodynamics (VUD) and performed a literature search to determine the level of evidence for the additional value of the imaging with urodynamic assessment of both neurogenic and non-neurogenic lower urinary tract dysfunction. Current guidelines do not specify the added value of imaging to urodynamics. Recommendations are based on single center series and expert opinion. Standard imaging protocols are not available and evidence regarding the balance between number and timing of pictures, patient positioning, and exposure time on the one hand and diagnosis on the other hand is lacking. On the basis of expert consensus VUD is relevant in the follow-up of patients with spinal dysraphism. Evidence for the value of VUD in non-neurogenic lower urinary tract dysfunction is sparse. There is some evidence that VUD is not necessary in uncomplicated female SUI, but expert opinion suggests it might improve the evaluation of patients with recurrent SUI. There is only low level evidence for the addition of video to urodynamics. The ICI-RS Think Tank encourages better reporting of results of imaging and systematic reporting of X-ray doses. Specific research hypotheses regarding the added value of imaging are recommended. The panel suggests the development of standards for technically optimal VUD that is practically achievable with machines that are on the market. © 2016 Wiley Periodicals, Inc.

Probabilistic structural analysis methods for select space propulsion system components

NASA Technical Reports Server (NTRS)

Millwater, H. R.; Cruse, T. A.

1989-01-01

The Probabilistic Structural Analysis Methods (PSAM) project developed at the Southwest Research Institute integrates state-of-the-art structural analysis techniques with probability theory for the design and analysis of complex large-scale engineering structures. An advanced efficient software system (NESSUS) capable of performing complex probabilistic analysis has been developed. NESSUS contains a number of software components to perform probabilistic analysis of structures. These components include: an expert system, a probabilistic finite element code, a probabilistic boundary element code and a fast probability integrator. The NESSUS software system is shown. An expert system is included to capture and utilize PSAM knowledge and experience. NESSUS/EXPERT is an interactive menu-driven expert system that provides information to assist in the use of the probabilistic finite element code NESSUS/FEM and the fast probability integrator (FPI). The expert system menu structure is summarized. The NESSUS system contains a state-of-the-art nonlinear probabilistic finite element code, NESSUS/FEM, to determine the structural response and sensitivities. A broad range of analysis capabilities and an extensive element library is present.

Identifying ideal brow vector position: empirical analysis of three brow archetypes.

PubMed

Hamamoto, Ashley A; Liu, Tiffany W; Wong, Brian J

2013-02-01

Surgical browlifts counteract the effects of aging, correct ptosis, and optimize forehead aesthetics. While surgeons have control over brow shape, the metrics defining ideal brow shape are subjective. This study aims to empirically determine whether three expert brow design strategies are aesthetically equivalent by using expert focus group analysis and relating these findings to brow surgery. Comprehensive literature search identified three dominant brow design methods (Westmore, Lamas and Anastasia) that are heavily cited, referenced or internationally recognized in either medical literature or by the lay media. Using their respective guidelines, brow shape was modified for 10 synthetic female faces, yielding 30 images. A focus group of 50 professional makeup artists ranked the three images for each of the 10 faces to generate ordinal attractiveness scores. The contemporary methods employed by Anastasia and Lamas produce a brow arch more lateral than Westmore's classic method. Although the more laterally located brow arch is considered the current trend in facial aesthetics, this style was not empirically supported. No single method was consistently rated most or least attractive by the focus group, and no significant difference in attractiveness score for the different methods was observed (p = 0.2454). Although each method of brow placement has been promoted as the "best" approach, no single brow design method achieved statistical significance in optimizing attractiveness. Each can be used effectively as a guide in designing eyebrow shape during browlift procedures, making it possible to use the three methods interchangeably. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Early esophageal cancer detection using RF classifiers

NASA Astrophysics Data System (ADS)

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

2016-03-01

Esophageal cancer is one of the fastest rising forms of cancer in the Western world. Using High-Definition (HD) endoscopy, gastroenterology experts can identify esophageal cancer at an early stage. Recent research shows that early cancer can be found using a state-of-the-art computer-aided detection (CADe) system based on analyzing static HD endoscopic images. Our research aims at extending this system by applying Random Forest (RF) classification, which introduces a confidence measure for detected cancer regions. To visualize this data, we propose a novel automated annotation system, employing the unique characteristics of the previous confidence measure. This approach allows reliable modeling of multi-expert knowledge and provides essential data for real-time video processing, to enable future use of the system in a clinical setting. The performance of the CADe system is evaluated on a 39-patient dataset, containing 100 images annotated by 5 expert gastroenterologists. The proposed system reaches a precision of 75% and recall of 90%, thereby improving the state-of-the-art results by 11 and 6 percentage points, respectively.

Applied algorithm in the liner inspection of solid rocket motors

NASA Astrophysics Data System (ADS)

Hoffmann, Luiz Felipe Simões; Bizarria, Francisco Carlos Parquet; Bizarria, José Walter Parquet

2018-03-01

In rocket motors, the bonding between the solid propellant and thermal insulation is accomplished by a thin adhesive layer, known as liner. The liner application method involves a complex sequence of tasks, which includes in its final stage, the surface integrity inspection. Nowadays in Brazil, an expert carries out a thorough visual inspection to detect defects on the liner surface that may compromise the propellant interface bonding. Therefore, this paper proposes an algorithm that uses the photometric stereo technique and the K-nearest neighbor (KNN) classifier to assist the expert in the surface inspection. Photometric stereo allows the surface information recovery of the test images, while the KNN method enables image pixels classification into two classes: non-defect and defect. Tests performed on a computer vision based prototype validate the algorithm. The positive results suggest that the algorithm is feasible and when implemented in a real scenario, will be able to help the expert in detecting defective areas on the liner surface.

A comparison of two methods for expert elicitation in health technology assessments.

PubMed

Grigore, Bogdan; Peters, Jaime; Hyde, Christopher; Stein, Ken

2016-07-26

When data needed to inform parameters in decision models are lacking, formal elicitation of expert judgement can be used to characterise parameter uncertainty. Although numerous methods for eliciting expert opinion as probability distributions exist, there is little research to suggest whether one method is more useful than any other method. This study had three objectives: (i) to obtain subjective probability distributions characterising parameter uncertainty in the context of a health technology assessment; (ii) to compare two elicitation methods by eliciting the same parameters in different ways; (iii) to collect subjective preferences of the experts for the different elicitation methods used. Twenty-seven clinical experts were invited to participate in an elicitation exercise to inform a published model-based cost-effectiveness analysis of alternative treatments for prostate cancer. Participants were individually asked to express their judgements as probability distributions using two different methods - the histogram and hybrid elicitation methods - presented in a random order. Individual distributions were mathematically aggregated across experts with and without weighting. The resulting combined distributions were used in the probabilistic analysis of the decision model and mean incremental cost-effectiveness ratios and the expected values of perfect information (EVPI) were calculated for each method, and compared with the original cost-effectiveness analysis. Scores on the ease of use of the two methods and the extent to which the probability distributions obtained from each method accurately reflected the expert's opinion were also recorded. Six experts completed the task. Mean ICERs from the probabilistic analysis ranged between £162,600-£175,500 per quality-adjusted life year (QALY) depending on the elicitation and weighting methods used. Compared to having no information, use of expert opinion decreased decision uncertainty: the EVPI value at the £30,000 per QALY threshold decreased by 74-86 % from the original cost-effectiveness analysis. Experts indicated that the histogram method was easier to use, but attributed a perception of more accuracy to the hybrid method. Inclusion of expert elicitation can decrease decision uncertainty. Here, choice of method did not affect the overall cost-effectiveness conclusions, but researchers intending to use expert elicitation need to be aware of the impact different methods could have.

Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging

PubMed Central

Lauzon, Carolyn B.; Asman, Andrew J.; Esparza, Michael L.; Burns, Scott S.; Fan, Qiuyun; Gao, Yurui; Anderson, Adam W.; Davis, Nicole; Cutting, Laurie E.; Landman, Bennett A.

2013-01-01

Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible. PMID:23637895

Simultaneous analysis and quality assurance for diffusion tensor imaging.

PubMed

Lauzon, Carolyn B; Asman, Andrew J; Esparza, Michael L; Burns, Scott S; Fan, Qiuyun; Gao, Yurui; Anderson, Adam W; Davis, Nicole; Cutting, Laurie E; Landman, Bennett A

2013-01-01

Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible.

On the SAR derived alert in the detection of oil spills according to the analysis of the EGEMP.

PubMed

Ferraro, Guido; Baschek, Björn; de Montpellier, Geraldine; Njoten, Ove; Perkovic, Marko; Vespe, Michele

2010-01-01

Satellite services that deliver information about possible oil spills at sea currently use different labels of "confidence" to describe the detections based on radar image processing. A common approach is to use a classification differentiating between low, medium and high levels of confidence. There is an ongoing discussion on the suitability of the existing classification systems of possible oil spills detected by radar satellite images with regard to the relevant significance and correspondence to user requirements. This paper contains a basic analysis of user requirements, current technical possibilities of satellite services as well as proposals for a redesign of the classification system as an evolution towards a more structured alert system. This research work offers a first review of implemented methodologies for the categorisation of detected oil spills, together with the proposal of explorative ideas evaluated by the European Group of Experts on satellite Monitoring of sea-based oil Pollution (EGEMP). Copyright 2009 Elsevier Ltd. All rights reserved.

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

Miller, M.; Kempner, L. Jr.; Mueller, W. III

The concept of an Expert System is not new. It has been around since the days of the early computers when scientists had dreams of robot automation to do everything from washing windows to automobile design. This paper discusses an application of an expert system and addresses software development issues and various levels of expert system development form a structural engineering viewpoint. An expert system designed to aid the structural engineer in first order inelastic analysis of latticed steel transmission powers is presented. The utilization of expert systems with large numerical analysis programs is discussed along with the software developmentmore » of such a system.« less

Data reduction expert assistant

NASA Technical Reports Server (NTRS)

Miller, Glenn E.; Johnston, Mark D.; Hanisch, Robert J.

1991-01-01

Viewgraphs on data reduction expert assistant are presented. Topics covered include: data analysis systems; philosophy of these systems; disadvantages; expert assistant; useful goals; and implementation considerations.

Just truth? Carefully applying history, philosophy and sociology of science to the forensic use of CCTV images.

PubMed

Edmond, Gary

2013-03-01

Using as a case study the forensic comparison of images for purposes of identification, this essay considers how the history, philosophy and sociology of science might help courts to improve their responses to scientific and technical forms of expert opinion evidence in ways that are more consistent with legal system goals and values. It places an emphasis on the need for more sophisticated models of science and expertise that are capable of helping judges to identify sufficiently reliable types of expert evidence and to reflexively incorporate the weakness of trial safeguards and personnel into their admissibility decision making. Copyright © 2013. Published by Elsevier Ltd.

Toward computer-assisted image-guided congenital heart defect repair: an initial phantom analysis.

PubMed

Kwartowitz, David M; Mefleh, Fuad N; Baker, G Hamilton

2017-10-01

Radiation exposure in interventional cardiology is an important consideration, due to risk of cancer and other morbidity to the patient and clinical staff. Cardiac catheterizations rely heavily on fluoroscopic imaging exposing both patient and clinician to ionizing radiation. An image-guided surgery system capable of facilitating cardiac catheterizations was developed and tested to evaluate dose reduction. Several electromagnetically tracked tools were constructed specifically a 7-Fr catheter with five 5-degree-of-freedom magnetic seeds. Catheter guidance was accomplished using our image guidance system Kit for Navigation by Image-Focused Exploration and fluoroscopy alone. A cardiac phantom was designed and 3D printed to validate the image guidance procedure. In mock procedures, an expert clinician guided and deployed an occluder across the septal defect of the phantom heart. The image guidance method resulted in a dose of 1.26 mSv of radiation dose per procedure, while traditional guidance resulted in a dose of 3.33 mSv. Average overall dose savings for the image-guided method was nearly 2.07 mSv or 62 %. The work showed significant ([Formula: see text]) decrease in radiation dose with use of image guidance methods at the expense of a modest increase in procedure time. This study lays the groundwork for further exploration of image guidance applications in pediatric cardiology.

Denoising and 4D visualization of OCT images

PubMed Central

Gargesha, Madhusudhana; Jenkins, Michael W.; Rollins, Andrew M.; Wilson, David L.

2009-01-01

We are using Optical Coherence Tomography (OCT) to image structure and function of the developing embryonic heart in avian models. Fast OCT imaging produces very large 3D (2D + time) and 4D (3D volumes + time) data sets, which greatly challenge ones ability to visualize results. Noise in OCT images poses additional challenges. We created an algorithm with a quick, data set specific optimization for reduction of both shot and speckle noise and applied it to 3D visualization and image segmentation in OCT. When compared to baseline algorithms (median, Wiener, orthogonal wavelet, basic non-orthogonal wavelet), a panel of experts judged the new algorithm to give much improved volume renderings concerning both noise and 3D visualization. Specifically, the algorithm provided a better visualization of the myocardial and endocardial surfaces, and the interaction of the embryonic heart tube with surrounding tissue. Quantitative evaluation using an image quality figure of merit also indicated superiority of the new algorithm. Noise reduction aided semi-automatic 2D image segmentation, as quantitatively evaluated using a contour distance measure with respect to an expert segmented contour. In conclusion, the noise reduction algorithm should be quite useful for visualization and quantitative measurements (e.g., heart volume, stroke volume, contraction velocity, etc.) in OCT embryo images. With its semi-automatic, data set specific optimization, we believe that the algorithm can be applied to OCT images from other applications. PMID:18679509

Infrared Spectroscopic Imaging for Prostate Pathology Practice

DTIC Science & Technology

2011-04-01

features – geometric properties of epithelial cells/nuclei and lumens – that are quantified based on H&E stained images as well as FT-IR images of...the samples. By restricting the features used to geometric measures, we sought to mimic the pattern recognition process employed by human experts, and...relatively dark and can be modeled as small elliptical areas in the stained images. This geometrical model is often confounded as multiple nuclei can be