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Sample records for liver vessels segmentation

  1. Liver vessel segmentation based on extreme learning machine.

    PubMed

    Zeng, Ye Zhan; Zhao, Yu Qian; Liao, Miao; Zou, Bei Ji; Wang, Xiao Fang; Wang, Wei

    2016-05-01

    Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.

  2. Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images

    NASA Astrophysics Data System (ADS)

    Luu, Ha Manh; Klink, Camiel; Moelker, Adriaan; Niessen, Wiro; van Walsum, Theo

    2015-05-01

    Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p  <  0.05) Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.

  3. Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images.

    PubMed

    Luu, Ha Manh; Klink, Camiel; Moelker, Adriaan; Niessen, Wiro; van Walsum, Theo

    2015-05-21

    Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p  <  0.05); Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.

  4. Segmentation of liver, its vessels and lesions from CT images for surgical planning.

    PubMed

    Oliveira, Dário Ab; Feitosa, Raul Q; Correia, Mauro M

    2011-04-20

    Cancer treatments are complex and involve different actions, which include many times a surgical procedure. Medical imaging provides important information for surgical planning, and it usually demands a proper segmentation, i.e., the identification of meaningful objects, such as organs and lesions. This study proposes a methodology to segment the liver, its vessels and nodules from computer tomography images for surgical planning. The proposed methodology consists of four steps executed sequentially: segmentation of liver, segmentation of vessels and nodules, identification of hepatic and portal veins, and segmentation of Couinaud anatomical segments. Firstly, the liver is segmented by a method based on a deformable model implemented through level sets, of which parameters are adjusted by using a supervised optimization procedure. Secondly, a mixture model is used to segment nodules and vessels through a region growing process. Then, the identification of hepatic and portal veins is performed using liver anatomical knowledge and a vein tracking algorithm. Finally, the Couinaud anatomical segments are identified according to the anatomical liver model proposed by Couinaud. Experiments were conducted using data and metrics brought from the liver segmentation competition held in the Sliver07 conference. A subset of five exams was used for estimation of segmentation parameter values, while 15 exams were used for evaluation. The method attained a good performance in 17 of the 20 exams, being ranked as the 6th best semi-automatic method when comparing to the methods described on the Sliver07 website (2008). It attained visual consistent results for nodules and veins segmentation, and we compiled the results, showing the best, worst, and mean results for all dataset. The method for liver segmentation performed well, according to the results of the numerical evaluation implemented, and the segmentation of liver internal structures were consistent with the anatomy of the

  5. Effect of blood vessel segmentation on the outcome of electroporation-based treatments of liver tumors.

    PubMed

    Marčan, Marija; Kos, Bor; Miklavčič, Damijan

    2015-01-01

    Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses applied to tissue via electrodes. To ensure that the whole tumor is covered with sufficiently high electric field, accurate numerical models are built based on individual patient anatomy. Extraction of patient's anatomy through segmentation of medical images inevitably produces some errors. In order to ensure the robustness of treatment planning, it is necessary to evaluate the potential effect of such errors on the electric field distribution. In this work we focus on determining the effect of errors in automatic segmentation of hepatic vessels on the electric field distribution in electroporation-based treatments in the liver. First, a numerical analysis was performed on a simple 'sphere and cylinder' model for tumors and vessels of different sizes and relative positions. Second, an analysis of two models extracted from medical images of real patients in which we introduced variations of an error of the automatic vessel segmentation method was performed. The results obtained from a simple model indicate that ignoring the vessels when calculating the electric field distribution can cause insufficient coverage of the tumor with electric fields. Results of this study indicate that this effect happens for small (10 mm) and medium-sized (30 mm) tumors, especially in the absence of a central electrode inserted in the tumor. The results obtained from the real-case models also show higher negative impact of automatic vessel segmentation errors on the electric field distribution when the central electrode is absent. However, the average error of the automatic vessel segmentation did not have an impact on the electric field distribution if the central electrode was present. This suggests the algorithm is robust enough to be used in creating a model for treatment parameter optimization, but with a central electrode.

  6. Blood vessel-based liver segmentation through the portal phase of a CT dataset

    NASA Astrophysics Data System (ADS)

    Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Moriyama, Noriyuki; Utsunomiya, Toru; Shimada, Mitsuo

    2013-02-01

    Blood vessels are dispersed throughout the human body organs and carry unique information for each person. This information can be used to delineate organ boundaries. The proposed method relies on abdominal blood vessels (ABV) to segment the liver considering the potential presence of tumors through the portal phase of a CT dataset. ABV are extracted and classified into hepatic (HBV) and nonhepatic (non-HBV) with a small number of interactions. HBV and non-HBV are used to guide an automatic segmentation of the liver. HBV are used to individually segment the core region of the liver. This region and non-HBV are used to construct a boundary surface between the liver and other organs to separate them. The core region is classified based on extracted posterior distributions of its histogram into low intensity tumor (LIT) and non-LIT core regions. Non-LIT case includes normal part of liver, HBV, and high intensity tumors if exist. Each core region is extended based on its corresponding posterior distribution. Extension is completed when it reaches either a variation in intensity or the constructed boundary surface. The method was applied to 80 datasets (30 Medical Image Computing and Computer Assisted Intervention (MICCAI) and 50 non-MICCAI data) including 60 datasets with tumors. Our results for the MICCAI-test data were evaluated by sliver07 [1] with an overall score of 79.7, which ranks seventh best on the site (December 2013). This approach seems a promising method for extraction of liver volumetry of various shapes and sizes and low intensity hepatic tumors.

  7. Probabilistic retinal vessel segmentation

    NASA Astrophysics Data System (ADS)

    Wu, Chang-Hua; Agam, Gady

    2007-03-01

    Optic fundus assessment is widely used for diagnosing vascular and non-vascular pathology. Inspection of the retinal vasculature may reveal hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. Due to various imaging conditions retinal images may be degraded. Consequently, the enhancement of such images and vessels in them is an important task with direct clinical applications. We propose a novel technique for vessel enhancement in retinal images that is capable of enhancing vessel junctions in addition to linear vessel segments. This is an extension of vessel filters we have previously developed for vessel enhancement in thoracic CT scans. The proposed approach is based on probabilistic models which can discern vessels and junctions. Evaluation shows the proposed filter is better than several known techniques and is comparable to the state of the art when evaluated on a standard dataset. A ridge-based vessel tracking process is applied on the enhanced image to demonstrate the effectiveness of the enhancement filter.

  8. New Technique for Automatic Segmentation of Blood Vessels in CT Scan Images of Liver Based on Optimized Fuzzy C-Means Method

    PubMed Central

    Ahmadi, Katayoon; Fouladi Nia, Babak

    2016-01-01

    Automatic segmentation of medical CT scan images is one of the most challenging fields in digital image processing. The goal of this paper is to discuss the automatic segmentation of CT scan images to detect and separate vessels in the liver. The segmentation of liver vessels is very important in the liver surgery planning and identifying the structure of vessels and their relationship to tumors. Fuzzy C-means (FCM) method has already been proposed for segmentation of liver vessels. Due to classical optimization process, this method suffers lack of sensitivity to the initial values of class centers and segmentation of local minima. In this article, a method based on FCM in conjunction with genetic algorithms (GA) is applied for segmentation of liver's blood vessels. This method was simulated and validated using 20 CT scan images of the liver. The results showed that the accuracy, sensitivity, specificity, and CPU time of new method in comparison with FCM algorithm reaching up to 91%, 83.62, 94.11%, and 27.17 were achieved, respectively. Moreover, selection of optimal and robust parameters in the initial step led to rapid convergence of the proposed method. The outcome of this research assists medical teams in estimating disease progress and selecting proper treatments. PMID:28044090

  9. New Technique for Automatic Segmentation of Blood Vessels in CT Scan Images of Liver Based on Optimized Fuzzy C-Means Method.

    PubMed

    Ahmadi, Katayoon; Karimi, Abbas; Fouladi Nia, Babak

    2016-01-01

    Automatic segmentation of medical CT scan images is one of the most challenging fields in digital image processing. The goal of this paper is to discuss the automatic segmentation of CT scan images to detect and separate vessels in the liver. The segmentation of liver vessels is very important in the liver surgery planning and identifying the structure of vessels and their relationship to tumors. Fuzzy C-means (FCM) method has already been proposed for segmentation of liver vessels. Due to classical optimization process, this method suffers lack of sensitivity to the initial values of class centers and segmentation of local minima. In this article, a method based on FCM in conjunction with genetic algorithms (GA) is applied for segmentation of liver's blood vessels. This method was simulated and validated using 20 CT scan images of the liver. The results showed that the accuracy, sensitivity, specificity, and CPU time of new method in comparison with FCM algorithm reaching up to 91%, 83.62, 94.11%, and 27.17 were achieved, respectively. Moreover, selection of optimal and robust parameters in the initial step led to rapid convergence of the proposed method. The outcome of this research assists medical teams in estimating disease progress and selecting proper treatments.

  10. Robust vessel segmentation

    NASA Astrophysics Data System (ADS)

    Bock, Susanne; Kühnel, Caroline; Boskamp, Tobias; Peitgen, Heinz-Otto

    2008-03-01

    In the context of cardiac applications, the primary goal of coronary vessel analysis often consists in supporting the diagnosis of vessel wall anomalies, such as coronary plaque and stenosis. Therefore, a fast and robust segmentation of the coronary tree is a very important but challenging task. We propose a new approach for coronary artery segmentation. Our method is based on an earlier proposed progressive region growing. A new growth front monitoring technique controls the segmentation and corrects local leakage by retrospective detection and removal of leakage artifacts. While progressively reducing the region growing threshold for the whole image, the growing process is locally analyzed using criteria based on the assumption of tubular, gradually narrowing vessels. If a voxel volume limit or a certain shape constraint is exceeded, the growing process is interrupted. Voxels affected by a failed segmentation are detected and deleted from the result. To avoid further processing at these positions, a large neighborhood is blocked for growing. Compared to a global region growing without local correction, our new local growth control and the adapted correction can deal with contrast decrease even in very small coronary arteries. Furthermore, our algorithm can efficiently handle noise artifacts and partial volume effects near the myocardium. The enhanced segmentation of more distal vessel parts was tested on 150 CT datasets. Furthermore, a comparison between the pure progressive region growing and our new approach was conducted.

  11. Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver

    PubMed Central

    Marcan, Marija; Pavliha, Denis; Music, Maja Marolt; Fuckan, Igor; Magjarevic, Ratko; Miklavcic, Damijan

    2014-01-01

    Introduction. Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input. Materials and methods. We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data. Results Results show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm. Conclusions The proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation-based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically. Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field. PMID:25177241

  12. Vessel segmentation in screening mammograms

    NASA Astrophysics Data System (ADS)

    Mordang, J. J.; Karssemeijer, N.

    2015-03-01

    Blood vessels are a major cause of false positives in computer aided detection systems for the detection of breast cancer. Therefore, the purpose of this study is to construct a framework for the segmentation of blood vessels in screening mammograms. The proposed framework is based on supervised learning using a cascade classifier. This cascade classifier consists of several stages where in each stage a GentleBoost classifier is trained on Haar-like features. A total of 30 cases were included in this study. In each image, vessel pixels were annotated by selecting pixels on the centerline of the vessel, control samples were taken by annotating a region without any visible vascular structures. This resulted in a total of 31,000 pixels marked as vascular and over 4 million control pixels. After training, the classifier assigns a vesselness likelihood to the pixels. The proposed framework was compared to three other vessel enhancing methods, i) a vesselness filter, ii) a gaussian derivative filter, and iii) a tubeness filter. The methods were compared in terms of area under the receiver operating characteristics curves, the Az values. The Az value of the cascade approach is 0:85. This is superior to the vesselness, Gaussian, and tubeness methods, with Az values of 0:77, 0:81, and 0:78, respectively. From these results, it can be concluded that our proposed framework is a promising method for the detection of vessels in screening mammograms.

  13. Vascular active contour for vessel tree segmentation.

    PubMed

    Shang, Yanfeng; Deklerck, Rudi; Nyssen, Edgard; Markova, Aneta; de Mey, Johan; Yang, Xin; Sun, Kun

    2011-04-01

    In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels. The vector field is derived from the eigenanalysis of the Hessian matrix of the image intensity in a multiscale framework. Finally, a dual curvature strategy, which uses a vesselness measure-dependent function selecting between a minimal principal curvature and a mean curvature criterion, is added to smoothen the surface of the vessel without changing its shape. The developed model is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images. Comparisons are made with several classical active contour models and manual extraction. The experiments show that our model is more accurate and robust than these classical models and is, therefore, more suited for automatic vessel tree extraction.

  14. Iterative Vessel Segmentation of Fundus Images.

    PubMed

    Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

    2015-07-01

    This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets.

  15. A region growing vessel segmentation algorithm based on spectrum information.

    PubMed

    Jiang, Huiyan; He, Baochun; Fang, Di; Ma, Zhiyuan; Yang, Benqiang; Zhang, Libo

    2013-01-01

    We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure's center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention.

  16. Recent Advancements in Retinal Vessel Segmentation.

    PubMed

    L Srinidhi, Chetan; Aparna, P; Rajan, Jeny

    2017-04-01

    Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.

  17. Unsupervised Retinal Vessel Segmentation Using Combined Filters.

    PubMed

    Oliveira, Wendeson S; Teixeira, Joyce Vitor; Ren, Tsang Ing; Cavalcanti, George D C; Sijbers, Jan

    2016-01-01

    Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.

  18. Unsupervised Retinal Vessel Segmentation Using Combined Filters

    PubMed Central

    Oliveira, Wendeson S.; Teixeira, Joyce Vitor; Ren, Tsang Ing; Cavalcanti, George D. C.; Sijbers, Jan

    2016-01-01

    Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels’ appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi’s filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods. PMID:26919587

  19. Robust RANSAC-based blood vessel segmentation

    NASA Astrophysics Data System (ADS)

    Yureidini, Ahmed; Kerrien, Erwan; Cotin, Stéphane

    2012-02-01

    Many vascular clinical applications require a vessel segmentation process that is able to extract both the centerline and the surface of the blood vessels. However, noise and topology issues (such as kissing vessels) prevent existing algorithm from being able to easily retrieve such a complex system as the brain vasculature. We propose here a new blood vessel tracking algorithm that 1) detects the vessel centerline; 2) provides a local radius estimate; and 3) extracts a dense set of points at the blood vessel surface. This algorithm is based on a RANSAC-based robust fitting of successive cylinders along the vessel. Our method was validated against the Multiple Hypothesis Tracking (MHT) algorithm on 10 3DRA patient data of the brain vasculature. Over 744 blood vessels of various sizes were considered for each patient. Our results demonstrated a greater ability of our algorithm to track small, tortuous and touching vessels (96% success rate), compared to MHT (65% success rate). The computed centerline precision was below 1 voxel when compared to MHT. Moreover, our results were obtained with the same set of parameters for all patients and all blood vessels, except for the seed point for each vessel, also necessary for MHT. The proposed algorithm is thereafter able to extract the full intracranial vasculature with little user interaction.

  20. A Review of Coronary Vessel Segmentation Algorithms

    PubMed Central

    Dehkordi, Maryam Taghizadeh; Sadri, Saeed; Doosthoseini, Alimohamad

    2011-01-01

    Coronary heart disease has been one of the main threats to human health. Coronary angiography is taken as the gold standard; for the assessment of coronary artery disease. However, sometimes, the images are difficult to visually interpret because of the crossing and overlapping of vessels in the angiogram. Vessel extraction from X-ray angiograms has been a challenging problem for several years. There are several problems in the extraction of vessels, including: weak contrast between the coronary arteries and the background, unknown and easily deformable shape of the vessel tree, and strong overlapping shadows of the bones. In this article we investigate the coronary vessel extraction and enhancement techniques, and present capabilities of the most important algorithms concerning coronary vessel segmentation. PMID:22606658

  1. An automated method for accurate vessel segmentation

    NASA Astrophysics Data System (ADS)

    Yang, Xin; Liu, Chaoyue; Le Minh, Hung; Wang, Zhiwei; Chien, Aichi; (Tim Cheng, Kwang-Ting

    2017-05-01

    Vessel segmentation is a critical task for various medical applications, such as diagnosis assistance of diabetic retinopathy, quantification of cerebral aneurysm’s growth, and guiding surgery in neurosurgical procedures. Despite technology advances in image segmentation, existing methods still suffer from low accuracy for vessel segmentation in the two challenging while common scenarios in clinical usage: (1) regions with a low signal-to-noise-ratio (SNR), and (2) at vessel boundaries disturbed by adjacent non-vessel pixels. In this paper, we present an automated system which can achieve highly accurate vessel segmentation for both 2D and 3D images even under these challenging scenarios. Three key contributions achieved by our system are: (1) a progressive contrast enhancement method to adaptively enhance contrast of challenging pixels that were otherwise indistinguishable, (2) a boundary refinement method to effectively improve segmentation accuracy at vessel borders based on Canny edge detection, and (3) a content-aware region-of-interests (ROI) adjustment method to automatically determine the locations and sizes of ROIs which contain ambiguous pixels and demand further verification. Extensive evaluation of our method is conducted on both 2D and 3D datasets. On a public 2D retinal dataset (named DRIVE (Staal 2004 IEEE Trans. Med. Imaging 23 501-9)) and our 2D clinical cerebral dataset, our approach achieves superior performance to the state-of-the-art methods including a vesselness based method (Frangi 1998 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention) and an optimally oriented flux (OOF) based method (Law and Chung 2008 European Conf. on Computer Vision). An evaluation on 11 clinical 3D CTA cerebral datasets shows that our method can achieve 94% average accuracy with respect to the manual segmentation reference, which is 23% to 33% better than the five baseline methods (Yushkevich 2006 Neuroimage 31 1116-28; Law and Chung 2008

  2. Automated segmentation of hepatic vessel trees in non-contrast x-ray CT images

    NASA Astrophysics Data System (ADS)

    Kawajiri, Suguru; Zhou, Xiangrong; Zhang, Xuejin; Hara, Takeshi; Fujita, Hiroshi; Yokoyama, Ryujiro; Kondo, Hiroshi; Kanematsu, Masayuki; Hoshi, Hiroaki

    2007-03-01

    Hepatic vessel trees are the key structures in the liver. Knowledge of the hepatic vessel trees is important for liver surgery planning and hepatic disease diagnosis such as portal hypertension. However, hepatic vessels cannot be easily distinguished from other liver tissues in non-contrast CT images. Automated segmentation of hepatic vessels in non-contrast CT images is a challenging issue. In this paper, an approach for automated segmentation of hepatic vessels trees in non-contrast X-ray CT images is proposed. Enhancement of hepatic vessels is performed using two techniques: (1) histogram transformation based on a Gaussian window function; (2) multi-scale line filtering based on eigenvalues of Hessian matrix. After the enhancement of hepatic vessels, candidate of hepatic vessels are extracted by thresholding. Small connected regions of size less than 100 voxels are considered as false-positives and are removed from the process. This approach is applied to 20 cases of non-contrast CT images. Hepatic vessel trees segmented from the contrast-enhanced CT images of the same patient are used as the ground truth in evaluating the performance of the proposed segmentation method. Results show that the proposed method can enhance and segment the hepatic vessel regions in non-contrast CT images correctly.

  3. CURVES: curve evolution for vessel segmentation.

    PubMed

    Lorigo, L M; Faugeras, O D; Grimson, W E; Keriven, R; Kikinis, R; Nabavi, A; Westin, C F

    2001-09-01

    The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning and performing neurosurgical procedures. In planning they provide information on where the lesion draws its blood supply and where it drains. During surgery the vessels serve as landmarks and guidelines to the lesion. The more minute the information is, the more precise the navigation and localization of computer guided procedures. Beyond neurosurgery and neurological study, vascular information is also crucial in cardiovascular surgery, diagnosis, and research. This paper addresses the problem of automatic segmentation of complicated curvilinear structures in three-dimensional imagery, with the primary application of segmenting vasculature in magnetic resonance angiography (MRA) images. The method presented is based on recent curve and surface evolution work in the computer vision community which models the object boundary as a manifold that evolves iteratively to minimize an energy criterion. This energy criterion is based both on intensity values in the image and on local smoothness properties of the object boundary, which is the vessel wall in this application. In particular, the method handles curves evolving in 3D, in contrast with previous work that has dealt with curves in 2D and surfaces in 3D. Results are presented on cerebral and aortic MRA data as well as lung computed tomography (CT) data.

  4. Human liver caudate lobe and liver segment.

    PubMed

    Murakami, Gen; Hata, Fumitake

    2002-12-01

    Recently, the caudate lobe has seemed to be the final target for aggressive cancer surgery of the liver. This lobe has five surfaces: the dorsal, left and hilar-free surfaces and the right and ventral-border planes. Surgeons have divided the caudate lobe into three parts: Spiegel's lobe, which is called the 'caudate lobe and papillary process' by anatomists, the caudate process, viewed as almost the same entity by anatomists, and the paracaval portion corresponding to the dorsally located parenchyma in front of the inferior vena cava. All three parts are supplied by primary branches originating from the left and right portal veins, including the hilar bifurcation area. The hilar bifurcation branch often (50%) supplies the paracaval portion and it sometimes (29%) extends its territory to Spiegel's lobe. It was postulated by Couinaud that the paracaval portion or the S9 is not defined by its supplying portal vein branch but by its 'dorsal location' in the liver. Couinaud's caudate lobe or dorsal-liver concept cause, and still now causes, great logical confusion for surgeons. We attempt here to describe the margins of the lobe, border branches of the portal vein, the left/right territorial border of the portal vein or Cantile's line and other topics closely relating to the surgery within these contexts. Finally, the caudate lobe as a liver segment will be discussed.

  5. Robust vessel segmentation in fundus images.

    PubMed

    Budai, A; Bock, R; Maier, A; Hornegger, J; Michelson, G

    2013-01-01

    One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.

  6. Automatic segmentation of abdominal vessels for improved pancreas localization

    NASA Astrophysics Data System (ADS)

    Farag, Amal; Liu, Jiamin; Summers, Ronald M.

    2014-03-01

    Accurate automatic detection and segmentation of abdominal organs from CT images is important for quantitative and qualitative organ tissue analysis as well as computer-aided diagnosis. The large variability of organ locations, the spatial interaction between organs that appear similar in medical scans and orientation and size variations are among the major challenges making the task very difficult. The pancreas poses these challenges in addition to its flexibility which allows for the shape of the tissue to vastly change. Due to the close proximity of the pancreas to numerous surrounding organs within the abdominal cavity the organ shifts according to the conditions of the organs within the abdomen, as such the pancreas is constantly changing. Combining these challenges with typically found patient-to-patient variations and scanning conditions the pancreas becomes harder to localize. In this paper we focus on three abdominal vessels that almost always abut the pancreas tissue and as such useful landmarks to identify the relative location of the pancreas. The splenic and portal veins extend from the hila of the spleen and liver, respectively, travel through the abdominal cavity and join at a position close to the head of the pancreas known as the portal confluence. A third vein, the superior mesenteric vein, anastomoses with the other two veins at the portal confluence. An automatic segmentation framework for obtaining the splenic vein, portal confluence and superior mesenteric vein is proposed using 17 contrast enhanced computed-tomography datasets. The proposed method uses outputs from the multi-organ multi-atlas label fusion and Frangi vesselness filter to obtain automatic seed points for vessel tracking and generation of statistical models of the desired vessels. The approach shows ability to identify the vessels and improve localization of the pancreas within the abdomen.

  7. Liver segmentation in color images (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Ma, Burton; Kingham, T. Peter; Miga, Michael I.; Jarnagin, William R.; Simpson, Amber L.

    2017-03-01

    We describe the use of a deep learning method for semantic segmentation of the liver from color images. Our intent is to eventually embed a semantic segmentation method into a stereo-vision based navigation system for open liver surgery. Semantic segmentation of the stereo images will allow us to reconstruct a point cloud containing the liver surfaces and excluding all other non-liver structures. We trained a deep learning algorithm using 136 images and 272 augmented images computed by rotating the original images. We tested the trained algorithm on 27 images that were not used for training purposes. The method achieves an 88% median pixel labeling accuracy over the test images.

  8. Segmentation and separation of venous vasculatures in liver CT images

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Hansen, Christian; Zidowitz, Stephan; Hahn, Horst K.

    2014-03-01

    Computer-aided analysis of venous vasculatures including hepatic veins and portal veins is important in liver surgery planning. The analysis normally consists of two important pre-processing tasks: segmenting both vasculatures and separating them from each other by assigning different labels. During the acquisition of multi-phase CT images, both of the venous vessels are enhanced by injected contrast agent and acquired either in a common phase or in two individual phases. The enhanced signals established by contrast agent are often not stably acquired due to non-optimal acquisition time. Inadequate contrast and the presence of large lesions in oncological patients, make the segmentation task quite challenging. To overcome these diffculties, we propose a framework with minimal user interactions to analyze venous vasculatures in multi-phase CT images. Firstly, presented vasculatures are automatically segmented adopting an efficient multi-scale Hessian-based vesselness filter. The initially segmented vessel trees are then converted to a graph representation, on which a series of graph filters are applied in post-processing steps to rule out irrelevant structures. Eventually, we develop a semi-automatic workow to refine the segmentation in the areas of inferior vena cava and entrance of portal veins, and to simultaneously separate hepatic veins from portal veins. Segmentation quality was evaluated with intensive tests enclosing 60 CT images from both healthy liver donors and oncological patients. To quantitatively measure the similarities between segmented and reference vessel trees, we propose three additional metrics: skeleton distance, branch coverage, and boundary surface distance, which are dedicated to quantifying the misalignment induced by both branching patterns and radii of two vessel trees.

  9. Accurate vessel segmentation with constrained B-snake.

    PubMed

    Yuanzhi Cheng; Xin Hu; Ji Wang; Yadong Wang; Tamura, Shinichi

    2015-08-01

    We describe an active contour framework with accurate shape and size constraints on the vessel cross-sectional planes to produce the vessel segmentation. It starts with a multiscale vessel axis tracing in a 3D computed tomography (CT) data, followed by vessel boundary delineation on the cross-sectional planes derived from the extracted axis. The vessel boundary surface is deformed under constrained movements on the cross sections and is voxelized to produce the final vascular segmentation. The novelty of this paper lies in the accurate contour point detection of thin vessels based on the CT scanning model, in the efficient implementation of missing contour points in the problematic regions and in the active contour model with accurate shape and size constraints. The main advantage of our framework is that it avoids disconnected and incomplete segmentation of the vessels in the problematic regions that contain touching vessels (vessels in close proximity to each other), diseased portions (pathologic structure attached to a vessel), and thin vessels. It is particularly suitable for accurate segmentation of thin and low contrast vessels. Our method is evaluated and demonstrated on CT data sets from our partner site, and its results are compared with three related methods. Our method is also tested on two publicly available databases and its results are compared with the recently published method. The applicability of the proposed method to some challenging clinical problems, the segmentation of the vessels in the problematic regions, is demonstrated with good results on both quantitative and qualitative experimentations; our segmentation algorithm can delineate vessel boundaries that have level of variability similar to those obtained manually.

  10. Automatic segmentation of vessels in in-vivo ultrasound scans

    NASA Astrophysics Data System (ADS)

    Tamimi-Sarnikowski, Philip; Brink-Kjær, Andreas; Moshavegh, Ramin; Arendt Jensen, Jørgen

    2017-03-01

    Ultrasound has become highly popular to monitor atherosclerosis, by scanning the carotid artery. The screening involves measuring the thickness of the vessel wall and diameter of the lumen. An automatic segmentation of the vessel lumen, can enable the determination of lumen diameter. This paper presents a fully automatic segmentation algorithm, for robustly segmenting the vessel lumen in longitudinal B-mode ultrasound images. The automatic segmentation is performed using a combination of B-mode and power Doppler images. The proposed algorithm includes a series of preprocessing steps, and performs a vessel segmentation by use of the marker-controlled watershed transform. The ultrasound images used in the study were acquired using the bk3000 ultrasound scanner (BK Ultrasound, Herlev, Denmark) with two transducers "8L2 Linear" and "10L2w Wide Linear" (BK Ultrasound, Herlev, Denmark). The algorithm was evaluated empirically and applied to a dataset of in-vivo 1770 images recorded from 8 healthy subjects. The segmentation results were compared to manual delineation performed by two experienced users. The results showed a sensitivity and specificity of 90.41+/-11.2 % and 97.93+/-5.7% (mean+/-standard deviation), respectively. The amount of overlap of segmentation and manual segmentation, was measured by the Dice similarity coefficient, which was 91.25+/-11.6%. The empirical results demonstrated the feasibility of segmenting the vessel lumen in ultrasound scans using a fully automatic algorithm.

  11. Extraction of liver volumetry based on blood vessel from the portal phase CT dataset

    NASA Astrophysics Data System (ADS)

    Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Utsunomiya, Tohru; Shimada, Mitsuo

    2012-02-01

    At liver surgery planning stage, the liver volumetry would be essential for surgeons. Main problem at liver extraction is the wide variability of livers in shapes and sizes. Since, hepatic blood vessels structure varies from a person to another and covers liver region, the present method uses that information for extraction of liver in two stages. The first stage is to extract abdominal blood vessels in the form of hepatic and nonhepatic blood vessels. At the second stage, extracted vessels are used to control extraction of liver region automatically. Contrast enhanced CT datasets at only the portal phase of 50 cases is used. Those data include 30 abnormal livers. A reference for all cases is done through a comparison of two experts labeling results and correction of their inter-reader variability. Results of the proposed method agree with the reference at an average rate of 97.8%. Through application of different metrics mentioned at MICCAI workshop for liver segmentation, it is found that: volume overlap error is 4.4%, volume difference is 0.3%, average symmetric distance is 0.7 mm, Root mean square symmetric distance is 0.8 mm, and maximum distance is 15.8 mm. These results represent the average of overall data and show an improved accuracy compared to current liver segmentation methods. It seems to be a promising method for extraction of liver volumetry of various shapes and sizes.

  12. Segmentation of vessels cluttered with cells using a physics based model.

    PubMed

    Schmugge, Stephen J; Keller, Steve; Nguyen, Nhat; Souvenir, Richard; Huynh, Toan; Clemens, Mark; Shin, Min C

    2008-01-01

    Segmentation of vessels in biomedical images is important as it can provide insight into analysis of vascular morphology, topology and is required for kinetic analysis of flow velocity and vessel permeability. Intravital microscopy is a powerful tool as it enables in vivo imaging of both vasculature and circulating cells. However, the analysis of vasculature in those images is difficult due to the presence of cells and their image gradient. In this paper, we provide a novel method of segmenting vessels with a high level of cell related clutter. A set of virtual point pairs ("vessel probes") are moved reacting to forces including Vessel Vector Flow (VVF) and Vessel Boundary Vector Flow (VBVF) forces. Incorporating the cell detection, the VVF force attracts the probes toward the vessel, while the VBVF force attracts the virtual points of the probes to localize the vessel boundary without being distracted by the image features of the cells. The vessel probes are moved according to Newtonian Physics reacting to the net of forces applied on them. We demonstrate the results on a set of five real in vivo images of liver vasculature cluttered by white blood cells. When compared against the ground truth prepared by the technician, the Root Mean Squared Error (RMSE) of segmentation with VVF and VBVF was 55% lower than the method without VVF and VBVF.

  13. Segmentation of hepatic artery in multi-phase liver CT using directional dilation and connectivity analysis

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Schnurr, Alena-Kathrin; Zidowitz, Stephan; Georgii, Joachim; Zhao, Yue; Razavi, Mohammad; Schwier, Michael; Hahn, Horst K.; Hansen, Christian

    2016-03-01

    Segmentation of hepatic arteries in multi-phase computed tomography (CT) images is indispensable in liver surgery planning. During image acquisition, the hepatic artery is enhanced by the injection of contrast agent. The enhanced signals are often not stably acquired due to non-optimal contrast timing. Other vascular structure, such as hepatic vein or portal vein, can be enhanced as well in the arterial phase, which can adversely affect the segmentation results. Furthermore, the arteries might suffer from partial volume effects due to their small diameter. To overcome these difficulties, we propose a framework for robust hepatic artery segmentation requiring a minimal amount of user interaction. First, an efficient multi-scale Hessian-based vesselness filter is applied on the artery phase CT image, aiming to enhance vessel structures with specified diameter range. Second, the vesselness response is processed using a Bayesian classifier to identify the most probable vessel structures. Considering the vesselness filter normally performs not ideally on the vessel bifurcations or the segments corrupted by noise, two vessel-reconnection techniques are proposed. The first technique uses a directional morphological operator to dilate vessel segments along their centerline directions, attempting to fill the gap between broken vascular segments. The second technique analyzes the connectivity of vessel segments and reconnects disconnected segments and branches. Finally, a 3D vessel tree is reconstructed. The algorithm has been evaluated using 18 CT images of the liver. To quantitatively measure the similarities between segmented and reference vessel trees, the skeleton coverage and mean symmetric distance are calculated to quantify the agreement between reference and segmented vessel skeletons, resulting in an average of 0:55+/-0:27 and 12:7+/-7:9 mm (mean standard deviation), respectively.

  14. Segmentation of Retinal Blood Vessels Based on Cake Filter

    PubMed Central

    Bao, Xi-Rong; Ge, Xin; She, Li-Huang; Zhang, Shi

    2015-01-01

    Segmentation of retinal blood vessels is significant to diagnosis and evaluation of ocular diseases like glaucoma and systemic diseases such as diabetes and hypertension. The retinal blood vessel segmentation for small and low contrast vessels is still a challenging problem. To solve this problem, a new method based on cake filter is proposed. Firstly, a quadrature filter band called cake filter band is made up in Fourier field. Then the real component fusion is used to separate the blood vessel from the background. Finally, the blood vessel network is got by a self-adaption threshold. The experiments implemented on the STARE database indicate that the new method has a better performance than the traditional ones on the small vessels extraction, average accuracy rate, and true and false positive rate. PMID:26636095

  15. Segmentation of Retinal Blood Vessels Based on Cake Filter.

    PubMed

    Bao, Xi-Rong; Ge, Xin; She, Li-Huang; Zhang, Shi

    2015-01-01

    Segmentation of retinal blood vessels is significant to diagnosis and evaluation of ocular diseases like glaucoma and systemic diseases such as diabetes and hypertension. The retinal blood vessel segmentation for small and low contrast vessels is still a challenging problem. To solve this problem, a new method based on cake filter is proposed. Firstly, a quadrature filter band called cake filter band is made up in Fourier field. Then the real component fusion is used to separate the blood vessel from the background. Finally, the blood vessel network is got by a self-adaption threshold. The experiments implemented on the STARE database indicate that the new method has a better performance than the traditional ones on the small vessels extraction, average accuracy rate, and true and false positive rate.

  16. Robust Retinal Blood Vessel Segmentation Based on Reinforcement Local Descriptions

    PubMed Central

    Li, Meng; Ma, Zhenshen; Liu, Chao; Han, Zhe

    2017-01-01

    Retinal blood vessels segmentation plays an important role for retinal image analysis. In this paper, we propose robust retinal blood vessel segmentation method based on reinforcement local descriptions. A novel line set based feature is firstly developed to capture local shape information of vessels by employing the length prior of vessels, which is robust to intensity variety. After that, local intensity feature is calculated for each pixel, and then morphological gradient feature is extracted for enhancing the local edge of smaller vessel. At last, line set based feature, local intensity feature, and morphological gradient feature are combined to obtain the reinforcement local descriptions. Compared with existing local descriptions, proposed reinforcement local description contains more local information of local shape, intensity, and edge of vessels, which is more robust. After feature extraction, SVM is trained for blood vessel segmentation. In addition, we also develop a postprocessing method based on morphological reconstruction to connect some discontinuous vessels and further obtain more accurate segmentation result. Experimental results on two public databases (DRIVE and STARE) demonstrate that proposed reinforcement local descriptions outperform the state-of-the-art method. PMID:28194407

  17. Robust Retinal Blood Vessel Segmentation Based on Reinforcement Local Descriptions.

    PubMed

    Li, Meng; Ma, Zhenshen; Liu, Chao; Zhang, Guang; Han, Zhe

    2017-01-01

    Retinal blood vessels segmentation plays an important role for retinal image analysis. In this paper, we propose robust retinal blood vessel segmentation method based on reinforcement local descriptions. A novel line set based feature is firstly developed to capture local shape information of vessels by employing the length prior of vessels, which is robust to intensity variety. After that, local intensity feature is calculated for each pixel, and then morphological gradient feature is extracted for enhancing the local edge of smaller vessel. At last, line set based feature, local intensity feature, and morphological gradient feature are combined to obtain the reinforcement local descriptions. Compared with existing local descriptions, proposed reinforcement local description contains more local information of local shape, intensity, and edge of vessels, which is more robust. After feature extraction, SVM is trained for blood vessel segmentation. In addition, we also develop a postprocessing method based on morphological reconstruction to connect some discontinuous vessels and further obtain more accurate segmentation result. Experimental results on two public databases (DRIVE and STARE) demonstrate that proposed reinforcement local descriptions outperform the state-of-the-art method.

  18. Lung vessel segmentation in CT images using graph-cuts

    NASA Astrophysics Data System (ADS)

    Zhai, Zhiwei; Staring, Marius; Stoel, Berend C.

    2016-03-01

    Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.

  19. Fuzzy pulmonary vessel segmentation in contrast enhanced CT data

    NASA Astrophysics Data System (ADS)

    Kaftan, Jens N.; Kiraly, Atilla P.; Bakai, Annemarie; Das, Marco; Novak, Carol L.; Aach, Til

    2008-03-01

    Pulmonary vascular tree segmentation has numerous applications in medical imaging and computer-aided diagnosis (CAD), including detection and visualization of pulmonary emboli (PE), improved lung nodule detection, and quantitative vessel analysis. We present a novel approach to pulmonary vessel segmentation based on a fuzzy segmentation concept, combining the strengths of both threshold and seed point based methods. The lungs of the original image are first segmented and a threshold-based approach identifies core vessel components with a high specificity. These components are then used to automatically identify reliable seed points for a fuzzy seed point based segmentation method, namely fuzzy connectedness. The output of the method consists of the probability of each voxel belonging to the vascular tree. Hence, our method provides the possibility to adjust the sensitivity/specificity of the segmentation result a posteriori according to application-specific requirements, through definition of a minimum vessel-probability required to classify a voxel as belonging to the vascular tree. The method has been evaluated on contrast-enhanced thoracic CT scans from clinical PE cases and demonstrates overall promising results. For quantitative validation we compare the segmentation results to randomly selected, semi-automatically segmented sub-volumes and present the resulting receiver operating characteristic (ROC) curves. Although we focus on contrast enhanced chest CT data, the method can be generalized to other regions of the body as well as to different imaging modalities.

  20. Blood vessel segmentation methodologies in retinal images--a survey.

    PubMed

    Fraz, M M; Remagnino, P; Hoppe, A; Uyyanonvara, B; Rudnicka, A R; Owen, C G; Barman, S A

    2012-10-01

    Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal vessel segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve.

  1. Brain blood vessel segmentation using line-shaped profiles.

    PubMed

    Babin, Danilo; Pižurica, Aleksandra; De Vylder, Jonas; Vansteenkiste, Ewout; Philips, Wilfried

    2013-11-21

    Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

  2. Brain blood vessel segmentation using line-shaped profiles

    NASA Astrophysics Data System (ADS)

    Babin, Danilo; Pižurica, Aleksandra; De Vylder, Jonas; Vansteenkiste, Ewout; Philips, Wilfried

    2013-11-01

    Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

  3. Robust hepatic vessel segmentation using multi deep convolution network

    NASA Astrophysics Data System (ADS)

    Kitrungrotsakul, Titinunt; Han, Xian-Hua; Iwamoto, Yutaro; Foruzan, Amir Hossein; Lin, Lanfen; Chen, Yen-Wei

    2017-03-01

    Extraction of blood vessels of the organ is a challenging task in the area of medical image processing. It is really difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty of vessels segmentation is the complicated structure of blood vessels and its large variations that make them hard to recognize. In this paper, we present deep artificial neural network architecture to automatically segment the hepatic vessels from computed tomography (CT) image. We proposed novel deep neural network (DNN) architecture for vessel segmentation from a medical CT volume, which consists of three deep convolution neural networks to extract features from difference planes of CT data. The three networks have share features at the first convolution layer but will separately learn their own features in the second layer. All three networks will join again at the top layer. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 12 CT volumes which training data are randomly generate from 5 CT volumes and 7 using for test. Our network can yield an average dice coefficient 0.830, while 3D deep convolution neural network can yield around 0.7 and multi-scale can yield only 0.6.

  4. Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification.

    PubMed

    Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

    2015-05-01

    This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions. Next, the regions common to both the binary images are extracted as the major vessels. In the second stage, all remaining pixels in the two binary images are classified using a Gaussian mixture model (GMM) classifier using a set of eight features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods. The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15%, and 95.3% in an average of 3.1, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE_DB1, respectively.

  5. Automatic segmentation of blood vessels from dynamic MRI datasets.

    PubMed

    Kubassova, Olga

    2007-01-01

    In this paper we present an approach for blood vessel segmentation from dynamic contrast-enhanced MRI datasets of the hand joints acquired from patients with active rheumatoid arthritis. Exclusion of the blood vessels is needed for accurate visualisation of the activation events and objective evaluation of the degree of inflammation. The segmentation technique is based on statistical modelling motivated by the physiological properties of the individual tissues, such as speed of uptake and concentration of the contrast agent; it incorporates Markov random field probabilistic framework and principal component analysis. The algorithm was tested on 60 temporal slices and has shown promising results.

  6. Segmentation of the liver from abdominal MR images: a level-set approach

    NASA Astrophysics Data System (ADS)

    Abdalbari, Anwar; Huang, Xishi; Ren, Jing

    2015-03-01

    The usage of prior knowledge in segmentation of abdominal MR images enables more accurate and comprehensive interpretation about the organ to segment. Prior knowledge about abdominal organ like liver vessels can be employed to get an accurate segmentation of the liver that leads to accurate diagnosis or treatment plan. In this paper, a new method for segmenting the liver from abdominal MR images using liver vessels as prior knowledge is proposed. This paper employs the technique of level set method to segment the liver from MR abdominal images. The speed image used in the level set method is responsible for propagating and stopping region growing at boundaries. As a result of the poor contrast of the MR images between the liver and the surrounding organs i.e. stomach, kidneys, and heart causes leak of the segmented liver to those organs that lead to inaccurate or incorrect segmentation. For that reason, a second speed image is developed, as an extra term to the level set, to control the front propagation at weak edges with the help of the original speed image. The basic idea of the proposed approach is to use the second speed image as a boundary surface which is approximately orthogonal to the area of the leak. The aim of the new speed image is to slow down the level set propagation and prevent the leak in the regions close to liver boundary. The new speed image is a surface created by filling holes to reconstruct the liver surface. These holes are formed as a result of the exit and the entry of the liver vessels, and are considered the main cause of the segmentation leak. The result of the proposed method shows superior outcome than other methods in the literature.

  7. Improvement of a retinal blood vessel segmentation method using the Insight Segmentation and Registration Toolkit (ITK).

    PubMed

    Martinez-Perez, M; Hughes, Alun D; Thom, Simon A; Parker, Kim H

    2007-01-01

    We describe an improved implementation of a segmentation method for retinal blood vessels based on a multi-scale approach and region growing employing modules from the Insight Segmentation and Registration Toolkit (ITK). We present the results of segmentation of retinal blood vessels using this improved method and compare these with results obtained using the original implementation in Matlab, as well as with expert manual segmentations obtained from a public database. We show that the ITK implementation achieves high quality segmentations with markedly improved computational efficiency. The ITK version has greater segmentation accuracy, from 0.94 to 0.96, than the Matlab version due to a decrease in FPR values and it is between 8 and 12 times faster than the original version. Furthermore, the ITK implementation is able to segment high-resolution images in an acceptable timescale.

  8. [Segmentation of retinal blood vessels based on centerline extraction].

    PubMed

    Zhou, Lin; Shen, Jianxin; Liao, Wenhe; Wang, Yuliang

    2012-02-01

    The precise estimation of blood vessel centerline and width is a prerequisite condition for the quantitative and visualized diagnosis of blood vessel disease in fundus images. In this paper, a retinal blood vessel segmentation algorithm based on centerline extraction is proposed. According to the characteristics of the fundus image and retinal blood vessels, the image is convoluted with the masks of discrete Gaussian partial derivative kernels. The centerline is determined by differential geometric properties of the blood vessels and the width is also calculated. The precision of our method can reach sub-pixel level with a fast computation speed. The experiments on several kinds of fundus images showed that the method worked quickly and accurately.

  9. Segmentation of vessel structures in serial whole slide sections using region-based context features

    NASA Astrophysics Data System (ADS)

    Schwier, Michael; Hahn, Horst K.; Dahmen, Uta; Dirsch, Olaf

    2016-03-01

    We present a method for the automatic segmentation of vascular structures in stacks of serial sections. It was initially motivated within the Virtual Liver Network research project that aims at creating a multi-scale virtual model of the liver. For this the vascular systems of several murine livers under different conditions need to be analyzed. To get highly detailed datasets, stacks of serial sections of the whole organs are prepared. Due to the huge amount of image data an automatic approach for segmenting the vessels is required. After registering the slides with an established method we use a set of Random Forest classifiers to distinguish vessels from tissue. Instead of a pixel-wise approach we perform the classification on small regions. This allows us to use more meaningful features. Besides basic intensity and texture features we introduce the concept of context features, which allow the classifiers to also consider the neighborhood of a region. Classification is performed in two stages. In the second stage the previous classification result of a region and its neighbors is used to refine the decision for a particular region. The context features and two stage classification process make our method very successful. It can handle different stainings and also detect vessels in which residue like blood cells remained. The specificity reaches 95%-99% for pure tissue, depending on staining and zoom level. Only in the direct vicinity of vessels the specificity declines to 88%-96%. The sensitivity rates reach between 89% and 98%.

  10. Vessel-guided airway tree segmentation: A voxel classification approach.

    PubMed

    Lo, Pechin; Sporring, Jon; Ashraf, Haseem; Pedersen, Jesper J H; de Bruijne, Marleen

    2010-08-01

    This paper presents a method for airway tree segmentation that uses a combination of a trained airway appearance model, vessel and airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between airway and non-airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, airway tree segmentations. A vessel orientation similarity measure is introduced, which indicates how similar the orientation of an airway candidate is to the orientation of the neighboring vessel. We use this vessel orientation similarity measure to overcome regions in the airway tree that have a low response from the appearance model. The proposed method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the airway appearance model produces more complete airway segmentations, leading to on average 20% longer trees, and 50% less leakage. When combining the airway appearance model with vessel orientation similarity, the improvement is even more significant (p<0.01) than only using the airway appearance model, with on average 7% increase in the total length of branches extracted correctly. Copyright 2010 Elsevier B.V. All rights reserved.

  11. Performance comparison of publicly available retinal blood vessel segmentation methods.

    PubMed

    Vostatek, Pavel; Claridge, Ela; Uusitalo, Hannu; Hauta-Kasari, Markku; Fält, Pauli; Lensu, Lasse

    2017-01-01

    Retinal blood vessel structure is an important indicator of many retinal and systemic diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and three unsupervised segmentation methods with a publicly available implementation are reviewed and quantitatively compared with each other on five public databases with ground truth segmentation of the vessels. Each method is tested under consistent conditions with two types of preprocessing, and the parameters of the methods are optimized for each database. Additionally, possibility to predict the parameters of the methods by the linear regression model is tested for each database. Resolution of the input images and amount of the vessel pixels in the ground truth are used as predictors. The results show the positive influence of preprocessing on the performance of the unsupervised methods. The methods show similar performance for segmentation accuracy, with the best performance achieved by the method by Azzopardi et al. (Acc 94.0) on ARIADB, the method by Soares et al. (Acc 94.6, 94.7) on CHASEDB1 and DRIVE, and the method by Nguyen et al. (Acc 95.8, 95.5) on HRF and STARE. The method by Soares et al. performed better with regard to the area under the ROC curve. Qualitative differences between the methods are discussed. Finally, it was possible to predict the parameter settings that give performance close to the optimized performance of each method.

  12. Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods

    SciTech Connect

    Beichel, Reinhard; Bornik, Alexander; Bauer, Christian; Sorantin, Erich

    2012-03-15

    Purpose: Liver segmentation is an important prerequisite for the assessment of liver cancer treatment options like tumor resection, image-guided radiation therapy (IGRT), radiofrequency ablation, etc. The purpose of this work was to evaluate a new approach for liver segmentation. Methods: A graph cuts segmentation method was combined with a three-dimensional virtual reality based segmentation refinement approach. The developed interactive segmentation system allowed the user to manipulate volume chunks and/or surfaces instead of 2D contours in cross-sectional images (i.e, slice-by-slice). The method was evaluated on twenty routinely acquired portal-phase contrast enhanced multislice computed tomography (CT) data sets. An independent reference was generated by utilizing a currently clinically utilized slice-by-slice segmentation method. After 1 h of introduction to the developed segmentation system, three experts were asked to segment all twenty data sets with the proposed method. Results: Compared to the independent standard, the relative volumetric segmentation overlap error averaged over all three experts and all twenty data sets was 3.74%. Liver segmentation required on average 16 min of user interaction per case. The calculated relative volumetric overlap errors were not found to be significantly different [analysis of variance (ANOVA) test, p = 0.82] between experts who utilized the proposed 3D system. In contrast, the time required by each expert for segmentation was found to be significantly different (ANOVA test, p = 0.0009). Major differences between generated segmentations and independent references were observed in areas were vessels enter or leave the liver and no accepted criteria for defining liver boundaries exist. In comparison, slice-by-slice based generation of the independent standard utilizing a live wire tool took 70.1 min on average. A standard 2D segmentation refinement approach applied to all twenty data sets required on average 38.2 min of

  13. Optic Disc Boundary and Vessel Origin Segmentation of Fundus Images.

    PubMed

    Roychowdhury, Sohini; Koozekanani, Dara D; Kuchinka, Sam N; Parhi, Keshab K

    2016-11-01

    This paper presents a novel classification-based optic disc (OD) segmentation algorithm that detects the OD boundary and the location of vessel origin (VO) pixel. First, the green plane of each fundus image is resized and morphologically reconstructed using a circular structuring element. Bright regions are then extracted from the morphologically reconstructed image that lie in close vicinity of the major blood vessels. Next, the bright regions are classified as bright probable OD regions and non-OD regions using six region-based features and a Gaussian mixture model classifier. The classified bright probable OD region with maximum Vessel-Sum and Solidity is detected as the best candidate region for the OD. Other bright probable OD regions within 1-disc diameter from the centroid of the best candidate OD region are then detected as remaining candidate regions for the OD. A convex hull containing all the candidate OD regions is then estimated, and a best-fit ellipse across the convex hull becomes the segmented OD boundary. Finally, the centroid of major blood vessels within the segmented OD boundary is detected as the VO pixel location. The proposed algorithm has low computation time complexity and it is robust to variations in image illumination, imaging angles, and retinal abnormalities. This algorithm achieves 98.8%-100% OD segmentation success and OD segmentation overlap score in the range of 72%-84% on images from the six public datasets of DRIVE, DIARETDB1, DIARETDB0, CHASE_DB1, MESSIDOR, and STARE in less than 2.14 s per image. Thus, the proposed algorithm can be used for automated detection of retinal pathologies, such as glaucoma, diabetic retinopathy, and maculopathy.

  14. Novel algorithm by low complexity filter on retinal vessel segmentation

    NASA Astrophysics Data System (ADS)

    Rostampour, Samad

    2011-10-01

    This article shows a new method to detect blood vessels in the retina by digital images. Retinal vessel segmentation is important for detection of side effect of diabetic disease, because diabetes can form new capillaries which are very brittle. The research has been done in two phases: preprocessing and processing. Preprocessing phase consists to apply a new filter that produces a suitable output. It shows vessels in dark color on white background and make a good difference between vessels and background. The complexity is very low and extra images are eliminated. The second phase is processing and used the method is called Bayesian. It is a built-in in supervision classification method. This method uses of mean and variance of intensity of pixels for calculate of probability. Finally Pixels of image are divided into two classes: vessels and background. Used images are related to the DRIVE database. After performing this operation, the calculation gives 95 percent of efficiency average. The method also was performed from an external sample DRIVE database which has retinopathy, and perfect result was obtained

  15. Segmentation of liver and liver tumor for the Liver-Workbench

    NASA Astrophysics Data System (ADS)

    Zhou, Jiayin; Ding, Feng; Xiong, Wei; Huang, Weimin; Tian, Qi; Wang, Zhimin; Venkatesh, Sudhakar K.; Leow, Wee Kheng

    2011-03-01

    Robust and efficient segmentation tools are important for the quantification of 3D liver and liver tumor volumes which can greatly help clinicians in clinical decision-making and treatment planning. A two-module image analysis procedure which integrates two novel semi-automatic algorithms has been developed to segment 3D liver and liver tumors from multi-detector computed tomography (MDCT) images. The first module is to segment the liver volume using a flippingfree mesh deformation model. In each iteration, before mesh deformation, the algorithm detects and avoids possible flippings which will cause the self-intersection of the mesh and then the undesired segmentation results. After flipping avoidance, Laplacian mesh deformation is performed with various constraints in geometry and shape smoothness. In the second module, the segmented liver volume is used as the ROI and liver tumors are segmented by using support vector machines (SVMs)-based voxel classification and propagational learning. First a SVM classifier was trained to extract tumor region from one single 2D slice in the intermediate part of a tumor by voxel classification. Then the extracted tumor contour, after some morphological operations, was projected to its neighboring slices for automated sampling, learning and further voxel classification in neighboring slices. This propagation procedure continued till all tumorcontaining slices were processed. The performance of the whole procedure was tested using 20 MDCT data sets and the results were promising: Nineteen liver volumes were successfully segmented out, with the mean relative absolute volume difference (RAVD), volume overlap error (VOE) and average symmetric surface distance (ASSD) to reference segmentation of 7.1%, 12.3% and 2.5 mm, respectively. For live tumors segmentation, the median RAVD, VOE and ASSD were 7.3%, 18.4%, 1.7 mm, respectively.

  16. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

    PubMed Central

    Rudyanto, Rina D.; Kerkstra, Sjoerd; van Rikxoort, Eva M.; Fetita, Catalin; Brillet, Pierre-Yves; Lefevre, Christophe; Xue, Wenzhe; Zhu, Xiangjun; Liang, Jianming; Öksüz, İlkay; Ünay, Devrim; Kadipaşaogandcaron;lu, Kamuran; Estépar, Raúl San José; Ross, James C.; Washko, George R.; Prieto, Juan-Carlos; Hoyos, Marcela Hernández; Orkisz, Maciej; Meine, Hans; Hüllebrand, Markus; Stöcker, Christina; Mir, Fernando Lopez; Naranjo, Valery; Villanueva, Eliseo; Staring, Marius; Xiao, Changyan; Stoel, Berend C.; Fabijanska, Anna; Smistad, Erik; Elster, Anne C.; Lindseth, Frank; Foruzan, Amir Hossein; Kiros, Ryan; Popuri, Karteek; Cobzas, Dana; Jimenez-Carretero, Daniel; Santos, Andres; Ledesma-Carbayo, Maria J.; Helmberger, Michael; Urschler, Martin; Pienn, Michael; Bosboom, Dennis G.H.; Campo, Arantza; Prokop, Mathias; de Jong, Pim A.; Ortiz-de-Solorzano, Carlos; Muñoz-Barrutia, Arrate; van Ginneken, Bram

    2016-01-01

    The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases. PMID:25113321

  17. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study.

    PubMed

    Rudyanto, Rina D; Kerkstra, Sjoerd; van Rikxoort, Eva M; Fetita, Catalin; Brillet, Pierre-Yves; Lefevre, Christophe; Xue, Wenzhe; Zhu, Xiangjun; Liang, Jianming; Öksüz, Ilkay; Ünay, Devrim; Kadipaşaoğlu, Kamuran; Estépar, Raúl San José; Ross, James C; Washko, George R; Prieto, Juan-Carlos; Hoyos, Marcela Hernández; Orkisz, Maciej; Meine, Hans; Hüllebrand, Markus; Stöcker, Christina; Mir, Fernando Lopez; Naranjo, Valery; Villanueva, Eliseo; Staring, Marius; Xiao, Changyan; Stoel, Berend C; Fabijanska, Anna; Smistad, Erik; Elster, Anne C; Lindseth, Frank; Foruzan, Amir Hossein; Kiros, Ryan; Popuri, Karteek; Cobzas, Dana; Jimenez-Carretero, Daniel; Santos, Andres; Ledesma-Carbayo, Maria J; Helmberger, Michael; Urschler, Martin; Pienn, Michael; Bosboom, Dennis G H; Campo, Arantza; Prokop, Mathias; de Jong, Pim A; Ortiz-de-Solorzano, Carlos; Muñoz-Barrutia, Arrate; van Ginneken, Bram

    2014-10-01

    The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

  18. Segmentation of brain blood vessels using projections in 3-D CT angiography images.

    PubMed

    Babin, Danilo; Vansteenkiste, Ewout; Pizurica, Aleksandra; Philips, Wilfried

    2011-01-01

    Segmenting cerebral blood vessels is of great importance in diagnostic and clinical applications, especially in quantitative diagnostics and surgery on aneurysms and arteriovenous malformations (AVM). Segmentation of CT angiography images requires algorithms robust to high intensity noise, while being able to segment low-contrast vessels. Because of this, most of the existing methods require user intervention. In this work we propose an automatic algorithm for efficient segmentation of 3-D CT angiography images of cerebral blood vessels. Our method is robust to high intensity noise and is able to accurately segment blood vessels with high range of luminance values, as well as low-contrast vessels.

  19. Selective Search and Intensity Context Based Retina Vessel Image Segmentation.

    PubMed

    Tang, Zhaohui; Zhang, Jin; Gui, Weihua

    2017-03-01

    In the framework of computer-aided diagnosis of eye disease, a new contextual image feature named influence degree of average intensity is proposed for retinal vessel image segmentation. This new feature evaluates the influence degree of current detected pixel decreasing the average intensity of the local row where that pixel located. Firstly, Hessian matrix is introduced to detect candidate regions, for the reason of accelerating segmentation. Then, the influence degree of average intensity of each pixel is extracted. Next, contextual feature vector for each pixel is constructed by concatenating the 8 feature neighbors. Finally, a classifier is built to classify each pixel into vessel or non-vessel based on its contextual feature. The effectiveness of the proposed method is demonstrated through receiver operating characteristic analysis on the benchmarked databases of DRIVE and STARE. Experiment results show that our method is comparable with the state-of-the-art methods. For example, the average accuracy, sensitivity, specificity achieved on the database DRIVE and STARE are 0.9611, 0.8174, 0.9747 and 0.9547, 0.7768, 0.9751, respectively.

  20. Bayesian method with spatial constraint for retinal vessel segmentation.

    PubMed

    Xiao, Zhiyong; Adel, Mouloud; Bourennane, Salah

    2013-01-01

    A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can identify blood vessels in the retinal image. Evaluation of the developed method is done on real retinal images which are from the DRIVE database and the STARE database. The performance is analyzed and compared to other published methods using a number of measures which include accuracy, sensitivity, and specificity. The proposed approach is proved to be effective on these two databases. The average accuracy, sensitivity, and specificity on the DRIVE database are 0.9529, 0.7513, and 0.9792, respectively, and for the STARE database 0.9476, 0.7147, and 0.9735, respectively. The performance is better than that of other vessel segmentation methods.

  1. Segmenting Retinal Blood Vessels With Deep Neural Networks.

    PubMed

    Liskowski, Pawel; Krawiec, Krzysztof

    2016-11-01

    The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400[Formula: see text]000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to > 0.99) and accuracy of classification (up to > 0.97 ). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( sensitivity > 0.87 ), and fares well on pathological cases.

  2. Reactor Vessel and Reactor Vessel Internals Segmentation at Zion Nuclear Power Station - 13230

    SciTech Connect

    Cooke, Conrad; Spann, Holger

    2013-07-01

    Zion Nuclear Power Station (ZNPS) is a dual-unit Pressurized Water Reactor (PWR) nuclear power plant located on the Lake Michigan shoreline, in the city of Zion, Illinois approximately 64 km (40 miles) north of Chicago, Illinois and 67 km (42 miles) south of Milwaukee, Wisconsin. Each PWR is of the Westinghouse design and had a generation capacity of 1040 MW. Exelon Corporation operated both reactors with the first unit starting production of power in 1973 and the second unit coming on line in 1974. The operation of both reactors ceased in 1996/1997. In 2010 the Nuclear Regulatory Commission approved the transfer of Exelon Corporation's license to ZionSolutions, the Long Term Stewardship subsidiary of EnergySolutions responsible for the decommissioning of ZNPS. In October 2010, ZionSolutions awarded Siempelkamp Nuclear Services, Inc. (SNS) the contract to plan, segment, remove, and package both reactor vessels and their respective internals. This presentation discusses the tools employed by SNS to remove and segment the Reactor Vessel Internals (RVI) and Reactor Vessels (RV) and conveys the recent progress. SNS's mechanical segmentation tooling includes the C-HORCE (Circumferential Hydraulically Operated Cutting Equipment), BMT (Bolt Milling Tool), FaST (Former Attachment Severing Tool) and the VRS (Volume Reduction Station). Thermal segmentation of the reactor vessels will be accomplished using an Oxygen- Propane cutting system. The tools for internals segmentation were designed by SNS using their experience from other successful reactor and large component decommissioning and demolition (D and D) projects in the US. All of the designs allow for the mechanical segmentation of the internals remotely in the water-filled reactor cavities. The C-HORCE is designed to saw seven circumferential cuts through the Core Barrel and Thermal Shield walls with individual thicknesses up to 100 mm (4 inches). The BMT is designed to remove the bolts that fasten the Baffle Plates to

  3. Feature Learning Based Random Walk for Liver Segmentation

    PubMed Central

    Zheng, Yongchang; Ai, Danni; Zhang, Pan; Gao, Yefei; Xia, Likun; Du, Shunda; Sang, Xinting; Yang, Jian

    2016-01-01

    Liver segmentation is a significant processing technique for computer-assisted diagnosis. This method has attracted considerable attention and achieved effective result. However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between the liver and adjacent organs. This paper proposes a feature-learning-based random walk method for liver segmentation using CT images. Four texture features were extracted and then classified to determine the classification probability corresponding to the test images. Seed points on the original test image were automatically selected and further used in the random walk (RW) algorithm to achieve comparable results to previous segmentation methods. PMID:27846217

  4. Vessel Enhancement and Segmentation of 4D CT Lung Image Using Stick Tensor Voting

    NASA Astrophysics Data System (ADS)

    Cong, Tan; Hao, Yang; Jingli, Shi; Xuan, Yang

    2016-12-01

    Vessel enhancement and segmentation plays a significant role in medical image analysis. This paper proposes a novel vessel enhancement and segmentation method for 4D CT lung image using stick tensor voting algorithm, which focuses on addressing the vessel distortion issue of vessel enhancement diffusion (VED) method. Furthermore, the enhanced results are easily segmented using level-set segmentation. In our method, firstly, vessels are filtered using Frangi's filter to reduce intrapulmonary noises and extract rough blood vessels. Secondly, stick tensor voting algorithm is employed to estimate the correct direction along the vessel. Then the estimated direction along the vessel is used as the anisotropic diffusion direction of vessel in VED algorithm, which makes the intensity diffusion of points locating at the vessel wall be consistent with the directions of vessels and enhance the tubular features of vessels. Finally, vessels can be extracted from the enhanced image by applying level-set segmentation method. A number of experiments results show that our method outperforms traditional VED method in vessel enhancement and results in satisfied segmented vessels.

  5. A boosted optimal linear learner for retinal vessel segmentation

    NASA Astrophysics Data System (ADS)

    Poletti, E.; Grisan, E.

    2014-03-01

    Ocular fundus images provide important information about retinal degeneration, which may be related to acute pathologies or to early signs of systemic diseases. An automatic and quantitative assessment of vessel morphological features, such as diameters and tortuosity, can improve clinical diagnosis and evaluation of retinopathy. At variance with available methods, we propose a data-driven approach, in which the system learns a set of optimal discriminative convolution kernels (linear learner). The set is progressively built based on an ADA-boost sample weighting scheme, providing seamless integration between linear learner estimation and classification. In order to capture the vessel appearance changes at different scales, the kernels are estimated on a pyramidal decomposition of the training samples. The set is employed as a rotating bank of matched filters, whose response is used by the boosted linear classifier to provide a classification of each image pixel into the two classes of interest (vessel/background). We tested the approach fundus images available from the DRIVE dataset. We show that the segmentation performance yields an accuracy of 0.94.

  6. Optic disc segmentation: level set methods and blood vessels inpainting

    NASA Astrophysics Data System (ADS)

    Almazroa, A.; Sun, Weiwei; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

    2017-03-01

    Segmenting the optic disc (OD) is an important and essential step in creating a frame of reference for diagnosing optic nerve head (ONH) pathology such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of ONH abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique is applied. The algorithm is evaluated using a new retinal fundus image dataset called RIGA (Retinal Images for Glaucoma Analysis). In the case of low quality images, a double level set is applied in which the first level set is considered to be a localization for the OD. Five hundred and fifty images are used to test the algorithm accuracy as well as its agreement with manual markings by six ophthalmologists. The accuracy of the algorithm in marking the optic disc area and centroid is 83.9%, and the best agreement is observed between the results of the algorithm and manual markings in 379 images.

  7. Liver segmentation for CT images using GVF snake

    SciTech Connect

    Liu Fan; Zhao Binsheng; Kijewski, Peter K.; Wang Liang; Schwartz, Lawrence H.

    2005-12-15

    Accurate liver segmentation on computed tomography (CT) images is a challenging task especially at sites where surrounding tissues (e.g., stomach, kidney) have densities similar to that of the liver and lesions reside at the liver edges. We have developed a method for semiautomatic delineation of the liver contours on contrast-enhanced CT images. The method utilizes a snake algorithm with a gradient vector flow (GVF) field as its external force. To improve the performance of the GVF snake in the segmentation of the liver contour, an edge map was obtained with a Canny edge detector, followed by modifications using a liver template and a concavity removal algorithm. With the modified edge map, for which unwanted edges inside the liver were eliminated, the GVF field was computed and an initial liver contour was formed. The snake algorithm was then applied to obtain the actual liver contour. This algorithm was extended to segment the liver volume in a slice-by-slice fashion, where the result of the preceding slice constrained the segmentation of the adjacent slice. 551 two-dimensional liver images from 20 volumetric images with colorectal metastases spreading throughout the livers were delineated using this method, and also manually by a radiologist for evaluation. The difference ratio, which is defined as the percentage ratio of mismatching volume between the computer and the radiologist's results, ranged from 2.9% to 7.6% with a median value of 5.3%.

  8. Automatic segmentation of the aorta and the adjoining vessels

    NASA Astrophysics Data System (ADS)

    Stutzmann, Tobias; Hesser, Jürgen; Völker, Wolfram; Dobhan, Matthias

    2010-03-01

    Diseases of the cardiovascular system are one of the main causes of death in the Western world. Especially the aorta and its main descending vessels are of high importance for diagnosis and treatment. Today, minimally invasive interventions are becoming increasingly popular due to their advantages like cost effectiveness and minimized risk for the patient. The training of such interventions, which require much of coordination skills, can be trained by task training systems, which are operation simualtion units. These systems require a data model that can be reconstructed from given patient data sets. In this paper, we present a method that allows to segment and classify aorta, carotides, and ostium (including coronary arteries) in one run, fully automatic and highly robust. The system tolerates changes in topology, streak artifacts in CT caused by calcification and inhomogeneous distribution of contrast agent. Both CT and MRI-Images can be processed. The underlying algorithm is based on a combination of Vesselness Enhancement Diffusion, Region Growing, and the Level Set Method. The system showed good results on all 15 real patient data sets whereby the deviation was smaller than two voxels.

  9. Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications

    SciTech Connect

    Zhou Chuan; Chan, H.-P.; Sahiner, Berkman; Hadjiiski, Lubomir M.; Chughtai, Aamer; Patel, Smita; Wei Jun; Ge Jun; Cascade, Philip N.; Kazerooni, Ella A.

    2007-12-15

    The authors are developing a computerized pulmonary vessel segmentation method for a computer-aided pulmonary embolism (PE) detection system on computed tomographic pulmonary angiography (CTPA) images. Because PE only occurs inside pulmonary arteries, an automatic and accurate segmentation of the pulmonary vessels in 3D CTPA images is an essential step for the PE CAD system. To segment the pulmonary vessels within the lung, the lung regions are first extracted using expectation-maximization (EM) analysis and morphological operations. The authors developed a 3D multiscale filtering technique to enhance the pulmonary vascular structures based on the analysis of eigenvalues of the Hessian matrix at multiple scales. A new response function of the filter was designed to enhance all vascular structures including the vessel bifurcations and suppress nonvessel structures such as the lymphoid tissues surrounding the vessels. An EM estimation is then used to segment the vascular structures by extracting the high response voxels at each scale. The vessel tree is finally reconstructed by integrating the segmented vessels at all scales based on a 'connected component' analysis. Two CTPA cases containing PEs were used to evaluate the performance of the system. One of these two cases also contained pleural effusion disease. Two experienced thoracic radiologists provided the gold standard of pulmonary vessels including both arteries and veins by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. The accuracy of vessel tree segmentation was evaluated by the percentage of the 'gold standard' vessel center points overlapping with the segmented vessels. The results show that 96.2% (2398/2494) and 96.3% (1910/1984) of the manually marked center points in the arteries overlapped with segmented vessels for the case without and with other lung diseases. For the manually marked center points in all vessels including arteries

  10. Automated segmentation of retinal blood vessels in spectral domain optical coherence tomography scans.

    PubMed

    Pilch, Matthäus; Wenner, Yaroslava; Strohmayr, Elisabeth; Preising, Markus; Friedburg, Christoph; Meyer Zu Bexten, Erdmuthe; Lorenz, Birgit; Stieger, Knut

    2012-07-01

    The correct segmentation of blood vessels in optical coherence tomography (OCT) images may be an important requirement for the analysis of intra-retinal layer thickness in human retinal diseases. We developed a shape model based procedure for the automatic segmentation of retinal blood vessels in spectral domain (SD)-OCT scans acquired with the Spectralis OCT system. The segmentation procedure is based on a statistical shape model that has been created through manual segmentation of vessels in a training phase. The actual segmentation procedure is performed after the approximate vessel position has been defined by a shadowgraph that assigns the lateral vessel positions. The active shape model method is subsequently used to segment blood vessel contours in axial direction. The automated segmentation results were validated against the manual segmentation of the same vessels by three expert readers. Manual and automated segmentations of 168 blood vessels from 34 B-scans were analyzed with respect to the deviations in the mean Euclidean distance and surface area. The mean Euclidean distance between the automatically and manually segmented contours (on average 4.0 pixels respectively 20 µm against all three experts) was within the range of the manually marked contours among the three readers (approximately 3.8 pixels respectively 18 µm for all experts). The area deviations between the automated and manual segmentation also lie within the range of the area deviations among the 3 clinical experts. Intra reader variability for the experts was between 0.9 and 0.94. We conclude that the automated segmentation approach is able to segment blood vessels with comparable accuracy as expert readers and will provide a useful tool in vessel analysis of whole C-scans, and in particular in multicenter trials.

  11. An improved retinal vessel segmentation method based on high level features for pathological images.

    PubMed

    Ganjee, Razieh; Azmi, Reza; Gholizadeh, Behrouz

    2014-09-01

    Most of the retinal blood vessel segmentation approaches use low level features, resulting in segmenting non-vessel structures together with vessel structures in pathological retinal images. In this paper, a new segmentation method based on high level features is proposed which can process the structure of vessel and non-vessel independently. In this method, segmentation is done in two steps. First, using low level features segmentation is accomplished. Second, using high level features, the non-vessel components are removed. For evaluation, STARE database is used which is publicly available in this field. The results show that the proposed method has 0.9536 accuracy and 0.0191 false positive average on all images of the database and 0.9542 accuracy and 0.0236 false positive average on pathological images. Therefore, the proposed approach shows acceptable accuracy on all images compared to other state of the art methods, and the least false positive average on pathological images.

  12. Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Hu, Chaoen; Hui, Hui; Wang, Shuo; Dong, Di; Liu, Xia; Yang, Xin; Tian, Jie

    2017-03-01

    Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.

  13. Vessel segmentation in 3D spectral OCT scans of the retina

    NASA Astrophysics Data System (ADS)

    Niemeijer, Meindert; Garvin, Mona K.; van Ginneken, Bram; Sonka, Milan; Abràmoff, Michael D.

    2008-03-01

    The latest generation of spectral optical coherence tomography (OCT) scanners is able to image 3D cross-sectional volumes of the retina at a high resolution and high speed. These scans offer a detailed view of the structure of the retina. Automated segmentation of the vessels in these volumes may lead to more objective diagnosis of retinal vascular disease including hypertensive retinopathy, retinopathy of prematurity. Additionally, vessel segmentation can allow color fundus images to be registered to these 3D volumes, possibly leading to a better understanding of the structure and localization of retinal structures and lesions. In this paper we present a method for automatically segmenting the vessels in a 3D OCT volume. First, the retina is automatically segmented into multiple layers, using simultaneous segmentation of their boundary surfaces in 3D. Next, a 2D projection of the vessels is produced by only using information from certain segmented layers. Finally, a supervised, pixel classification based vessel segmentation approach is applied to the projection image. We compared the influence of two methods for the projection on the performance of the vessel segmentation on 10 optic nerve head centered 3D OCT scans. The method was trained on 5 independent scans. Using ROC analysis, our proposed vessel segmentation system obtains an area under the curve of 0.970 when compared with the segmentation of a human observer.

  14. Multimodal retinal vessel segmentation from spectral-domain optical coherence tomography and fundus photography.

    PubMed

    Hu, Zhihong; Niemeijer, Meindert; Abràmoff, Michael D; Garvin, Mona K

    2012-10-01

    Segmenting retinal vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes is particularly challenging due to the projected neural canal opening (NCO) and relatively low visibility in the ONH center. Color fundus photographs provide a relatively high vessel contrast in the region inside the NCO, but have not been previously used to aid the SD-OCT vessel segmentation process. Thus, in this paper, we present two approaches for the segmentation of retinal vessels in SD-OCT volumes that each take advantage of complimentary information from fundus photographs. In the first approach (referred to as the registered-fundus vessel segmentation approach), vessels are first segmented on the fundus photograph directly (using a k-NN pixel classifier) and this vessel segmentation result is mapped to the SD-OCT volume through the registration of the fundus photograph to the SD-OCT volume. In the second approach (referred to as the multimodal vessel segmentation approach), after fundus-to-SD-OCT registration, vessels are simultaneously segmented with a k -NN classifier using features from both modalities. Three-dimensional structural information from the intraretinal layers and neural canal opening obtained through graph-theoretic segmentation approaches of the SD-OCT volume are used in combination with Gaussian filter banks and Gabor wavelets to generate the features. The approach is trained on 15 and tested on 19 randomly chosen independent image pairs of SD-OCT volumes and fundus images from 34 subjects with glaucoma. Based on a receiver operating characteristic (ROC) curve analysis, the present registered-fundus and multimodal vessel segmentation approaches [area under the curve (AUC) of 0.85 and 0.89, respectively] both perform significantly better than the two previous OCT-based approaches (AUC of 0.78 and 0.83, p < 0.05). The multimodal approach overall performs significantly better than the other three approaches (p < 0.05).

  15. Performance benchmarking of liver CT image segmentation and volume estimation

    NASA Astrophysics Data System (ADS)

    Xiong, Wei; Zhou, Jiayin; Tian, Qi; Liu, Jimmy J.; Qi, Yingyi; Leow, Wee Kheng; Han, Thazin; Wang, Shih-chang

    2008-03-01

    In recent years more and more computer aided diagnosis (CAD) systems are being used routinely in hospitals. Image-based knowledge discovery plays important roles in many CAD applications, which have great potential to be integrated into the next-generation picture archiving and communication systems (PACS). Robust medical image segmentation tools are essentials for such discovery in many CAD applications. In this paper we present a platform with necessary tools for performance benchmarking for algorithms of liver segmentation and volume estimation used for liver transplantation planning. It includes an abdominal computer tomography (CT) image database (DB), annotation tools, a ground truth DB, and performance measure protocols. The proposed architecture is generic and can be used for other organs and imaging modalities. In the current study, approximately 70 sets of abdominal CT images with normal livers have been collected and a user-friendly annotation tool is developed to generate ground truth data for a variety of organs, including 2D contours of liver, two kidneys, spleen, aorta and spinal canal. Abdominal organ segmentation algorithms using 2D atlases and 3D probabilistic atlases can be evaluated on the platform. Preliminary benchmark results from the liver segmentation algorithms which make use of statistical knowledge extracted from the abdominal CT image DB are also reported. We target to increase the CT scans to about 300 sets in the near future and plan to make the DBs built available to medical imaging research community for performance benchmarking of liver segmentation algorithms.

  16. A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data

    NASA Astrophysics Data System (ADS)

    Spiegel, M.; Redel, T.; Struffert, T.; Hornegger, J.; Doerfler, A.

    2011-10-01

    Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling.

  17. An automatic method for fast and accurate liver segmentation in CT images using a shape detection level set method

    NASA Astrophysics Data System (ADS)

    Lee, Jeongjin; Kim, Namkug; Lee, Ho; Seo, Joon Beom; Won, Hyung Jin; Shin, Yong Moon; Shin, Yeong Gil

    2007-03-01

    Automatic liver segmentation is still a challenging task due to the ambiguity of liver boundary and the complex context of nearby organs. In this paper, we propose a faster and more accurate way of liver segmentation in CT images with an enhanced level set method. The speed image for level-set propagation is smoothly generated by increasing number of iterations in anisotropic diffusion filtering. This prevents the level-set propagation from stopping in front of local minima, which prevails in liver CT images due to irregular intensity distributions of the interior liver region. The curvature term of shape modeling level-set method captures well the shape variations of the liver along the slice. Finally, rolling ball algorithm is applied for including enhanced vessels near the liver boundary. Our approach are tested and compared to manual segmentation results of eight CT scans with 5mm slice distance using the average distance and volume error. The average distance error between corresponding liver boundaries is 1.58 mm and the average volume error is 2.2%. The average processing time for the segmentation of each slice is 5.2 seconds, which is much faster than the conventional ones. Accurate and fast result of our method will expedite the next stage of liver volume quantification for liver transplantations.

  18. Fpga based hardware synthesis for automatic segmentation of retinal blood vessels in diabetic retinopathy images.

    PubMed

    Sivakamasundari, J; Kavitha, G; Sujatha, C M; Ramakrishnan, S

    2014-01-01

    Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Real-Time mass screening system for DR is vital for timely diagnosis and periodic screening to prevent the patient from severe visual loss. Human retinal fundus images are widely used for an automated segmentation of blood vessel and diagnosis of various blood vessel disorders. In this work, an attempt has been made to perform hardware synthesis of Kirsch template based edge detection for segmentation of blood vessels. This method is implemented using LabVIEW software and is synthesized in field programmable gate array board to yield results in real-time application. The segmentation of blood vessels using Kirsch based edge detection is compared with other edge detection methods such as Sobel, Prewitt and Canny. The texture features such as energy, entropy, contrast, mean, homogeneity and structural feature namely ratio of vessel to vessel free area are obtained from the segmented images. The performance of segmentation is analysed in terms of sensitivity, specificity and accuracy. It is observed from the results that the Kirsch based edge detection technique segmented the edges of blood vessels better than other edge detection techniques. The ratio of vessel to vessel free area classified the normal and DR affected retinal images more significantly than other texture based features. FPGA based hardware synthesis of Kirsch edge detection method is able to differentiate normal and diseased images with high specificity (93%). This automated segmentation of retinal blood vessels system could be used in computer-assisted diagnosis for diabetic retinopathy screening in real-time application.

  19. Three-dimensional vessel segmentation using a novel combinatory filter framework

    NASA Astrophysics Data System (ADS)

    Ding, Y.; Ward, W. O. C.; Wästerlid, T.; Gowland, P. A.; Peters, A. M.; Yang, J.; Nakagawa, S.; Bai, L.

    2014-11-01

    Blood vessel segmentation is of great importance in medical diagnostic applications. Filter based methods that make use of Hessian matrices have been found to be very useful for blood vessel segmentation in both 2D and 3D medical images. However, these methods often fail on images that contain high density microvessels and background noise. The errors in the form of missing, undesired broken or incorrectly merged vessels eventually lead to poor segmentation results. In this paper, we present a novel method for 3D vessel segmentation that is also suitable for segmenting microvessels, incorporating the advantages of a line filter and a Hessian-based vessel filter to overcome the problems. The proposed method is shown to be reliable for noisy and inhomogeneous images. Vessels can also be separated based on their scale/thickness so that the method can be used for different medical applications. Furthermore, a quantitative vessel analysis method based on the multifractal analysis is performed on the segmented vasculature and fractal properties are found in all images.

  20. Three-dimensional vessel segmentation using a novel combinatory filter framework.

    PubMed

    Ding, Y; Ward, W O C; Wästerlid, T; Gowland, P A; Peters, A M; Yang, J; Bai, L

    2014-11-21

    Blood vessel segmentation is of great importance in medical diagnostic applications. Filter based methods that make use of Hessian matrices have been found to be very useful for blood vessel segmentation in both 2D and 3D medical images. However, these methods often fail on images that contain high density microvessels and background noise. The errors in the form of missing, undesired broken or incorrectly merged vessels eventually lead to poor segmentation results. In this paper, we present a novel method for 3D vessel segmentation that is also suitable for segmenting microvessels, incorporating the advantages of a line filter and a Hessian-based vessel filter to overcome the problems. The proposed method is shown to be reliable for noisy and inhomogeneous images. Vessels can also be separated based on their scale/thickness so that the method can be used for different medical applications. Furthermore, a quantitative vessel analysis method based on the multifractal analysis is performed on the segmented vasculature and fractal properties are found in all images.

  1. A new strategy to obtain robust markers for blood vessels segmentation by using the watersheds method.

    PubMed

    Rodríguez, Roberto; Alarcón, Teresa E; Pacheco, Oriana

    2005-10-01

    The watersheds method is a powerful segmentation tool developed in mathematical morphology. In order to prevent its over-segmentation, in this paper, we present a new strategy to obtain robust markers for segmentation of blood vessels from malignant tumors. For this purpose, we introduced a new algorithm. We propose a two-stage segmentation strategy which involves: (1) extracting an approximate region containing the blood vessel and part of the background near the blood vessel, and (2) segmenting the blood vessel from the background within this region. The approach effectively reduces the influence of peripheral background intensities on the extraction of a blood vessel region. In this application the important information to be extracted from images is only the number of blood vessels present in the images. The proposed strategy was tested on manual segmentation, where segmentation errors less than 10% for false positives and 0% for false negatives are observed. It is demonstrated by extensive experimentation, by using real images, that the proposed strategy was suitable for our application in the environment of a personal computer.

  2. Human liver territories: Think beyond the 8-segments scheme.

    PubMed

    Fasel, Jean H D

    2017-10-01

    Worldwide, compartmentalization of the human liver into portal venous territories today follows the eight-segments scheme credited to Couinaud. However, there are increasing reports of anatomical, radiological and surgical observations that contradict this concept. This paper presents a viewpoint that enhances understanding of these inconsistencies and can serve as a basis for customized liver interventions. Clin. Anat. 30:974-977, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  3. A general approach to liver lesion segmentation in CT images

    NASA Astrophysics Data System (ADS)

    Cao, Li; Udupa, Jayaram K.; Odhner, Dewey; Huang, Lidong; Tong, Yubing; Torigian, Drew A.

    2016-03-01

    Lesion segmentation has remained a challenge in different body regions. Generalizability is lacking in published methods as variability in results is common, even for a given organ and modality, such that it becomes difficult to establish standardized methods of disease quantification and reporting. This paper makes an attempt at a generalizable method based on classifying lesions along with their background into groups using clinically used visual attributes. Using an Iterative Relative Fuzzy Connectedness (IRFC) delineation engine, the ideas are implemented for the task of liver lesion segmentation in computed tomography (CT) images. For lesion groups with the same background properties, a few subjects are chosen as the training set to obtain the optimal IRFC parameters for the background tissue components. For lesion groups with similar foreground properties, optimal foreground parameters for IRFC are set as the median intensity value of the training lesion subset. To segment liver lesions belonging to a certain group, the devised method requires manual loading of the corresponding parameters, and correct setting of the foreground and background seeds. The segmentation is then completed in seconds. Segmentation accuracy and repeatability with respect to seed specification are evaluated. Accuracy is assessed by the assignment of a delineation quality score (DQS) to each case. Inter-operator repeatability is assessed by the difference between segmentations carried out independently by two operators. Experiments on 80 liver lesion cases show that the proposed method achieves a mean DQS score of 4.03 and inter-operator repeatability of 92.3%.

  4. A Vessel Active Contour Model for Vascular Segmentation

    PubMed Central

    Chen, Qingli; Wang, Wei; Peng, Yu; Wang, Qingjun; Wu, Zhongke; Zhou, Mingquan

    2014-01-01

    This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images. PMID:25101262

  5. Magnetic resonance imaging of water ascent in embolized xylem vessels of grapevine stem segments

    Treesearch

    Mingtao Wang; Melvin T. Tyree; Roderick E. Wasylishen

    2013-01-01

    Temporal and spatial information about water refilling of embolized xylem vessels and the rate of water ascent in these vessels is critical for understanding embolism repair in intact living vascular plants. High-resolution 1H magnetic resonance imaging (MRI) experiments have been performed on embolized grapevine stem segments while they were...

  6. Semiautomatic segmentation of liver metastases on volumetric CT images

    SciTech Connect

    Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng

    2015-11-15

    Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accurately delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation

  7. Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data

    PubMed Central

    Kajić, Vedran; Esmaeelpour, Marieh; Glittenberg, Carl; Kraus, Martin F.; Honegger, Joachim; Othara, Richu; Binder, Susanne; Fujimoto, James G.; Drexler, Wolfgang

    2012-01-01

    A fully automated, robust vessel segmentation algorithm has been developed for choroidal OCT, employing multiscale 3D edge filtering and projection of “probability cones” to determine the vessel “core”, even in the tomograms with low signal-to-noise ratio (SNR). Based on the ideal vessel response after registration and multiscale filtering, with computed depth related SNR, the vessel core estimate is dilated to quantify the full vessel diameter. As a consequence, various statistics can be computed using the 3D choroidal vessel information, such as ratios of inner (smaller) to outer (larger) choroidal vessels or the absolute/relative volume of choroid vessels. Choroidal vessel quantification can be displayed in various forms, focused and averaged within a special region of interest, or analyzed as the function of image depth. In this way, the proposed algorithm enables unique visualization of choroidal watershed zones, as well as the vessel size reduction when investigating the choroid from the sclera towards the retinal pigment epithelium (RPE). To the best of our knowledge, this is the first time that an automatic choroidal vessel segmentation algorithm is successfully applied to 1060 nm 3D OCT of healthy and diseased eyes. PMID:23304653

  8. Robotic liver resection including the posterosuperior segments: initial experience.

    PubMed

    Nota, Carolijn L M A; Molenaar, I Quintus; van Hillegersberg, Richard; Borel Rinkes, Inne H M; Hagendoorn, Jeroen

    2016-11-01

    Robot-assisted laparoscopy has been introduced to overcome the limitations of conventional laparoscopy. This technique has potential advantages over laparoscopy, such as increased dexterity, three-dimensional view, and a magnified view of the operative field. Therefore, improved dexterity may make a robotic system particularly suited for liver resections, which require nonlinear manipulation, such as curved parenchymal transection, hilar dissection, and resection of posterosuperior segments. Between August 2014 and March 2016, 16 patients underwent robot-assisted laparoscopic liver resection at University Medical Center Utrecht. Fifteen robot-assisted laparoscopic liver resections were performed in a minimally invasive manner. One procedure was converted. In eight patients, we performed a resection of a posterosuperior segment (segment 7 or 8). Median operating time was 146 (60-265) min, and median blood loss was 150 (5-600) mL. Four patients had a Clavien-Dindo grade III complication. Median length of stay was 4 (1-8) days. There was no mortality. This prospective study reporting on our initial experience with robot-assisted laparoscopic liver resection demonstrates that this technique is easily adopted, safe, and feasible for minor hepatectomies in selected patients. Moreover, it shows that the robotic platform also enables fully laparoscopic resections of the posterior segments. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Endoscopic ultrasound description of liver segmentation and anatomy.

    PubMed

    Bhatia, Vikram; Hijioka, Susumu; Hara, Kazuo; Mizuno, Nobumasa; Imaoka, Hiroshi; Yamao, Kenji

    2014-05-01

    Endoscopic ultrasound (EUS) can demonstrate the detailed anatomy of the liver from the transgastric and transduodenal routes. Most of the liver segments can be imaged with EUS, except the right posterior segments. The intrahepatic vascular landmarks include the major hepatic veins, portal vein radicals, hepatic arterial branches, and the inferior vena cava, and the venosum and teres ligaments are other important intrahepatic landmarks. The liver hilum and gallbladder serve as useful surface landmarks. Deciphering liver segmentation and anatomy by EUS requires orienting the scan planes with these landmarkstructures, and is different from the static cross-sectional radiological images. Orientation during EUS requires appreciation of the numerous scan planes possible in real-time, and the direction of scanning from the stomach and duodenal bulb. We describe EUS imaging of the liver with a curved linear probe in a step-by-step approach, with the relevant anatomical details, potential applications, and pitfalls of this novel EUS application. © 2013 The Authors. Digestive Endoscopy © 2013 Japan Gastroenterological Endoscopy Society.

  10. Metastatic liver tumour segmentation from discriminant Grassmannian manifolds

    NASA Astrophysics Data System (ADS)

    Kadoury, Samuel; Vorontsov, Eugene; Tang, An

    2015-08-01

    The early detection, diagnosis and monitoring of liver cancer progression can be achieved with the precise delineation of metastatic tumours. However, accurate automated segmentation remains challenging due to the presence of noise, inhomogeneity and the high appearance variability of malignant tissue. In this paper, we propose an unsupervised metastatic liver tumour segmentation framework using a machine learning approach based on discriminant Grassmannian manifolds which learns the appearance of tumours with respect to normal tissue. First, the framework learns within-class and between-class similarity distributions from a training set of images to discover the optimal manifold discrimination between normal and pathological tissue in the liver. Second, a conditional optimisation scheme computes non-local pairwise as well as pattern-based clique potentials from the manifold subspace to recognise regions with similar labelings and to incorporate global consistency in the segmentation process. The proposed framework was validated on a clinical database of 43 CT images from patients with metastatic liver cancer. Compared to state-of-the-art methods, our method achieves a better performance on two separate datasets of metastatic liver tumours from different clinical sites, yielding an overall mean Dice similarity coefficient of 90.7+/- 2.4 in over 50 tumours with an average volume of 27.3 mm3.

  11. Comparison of vessel enhancement algorithms applied to Time-of-Flight MRA images for cerebrovascular segmentation.

    PubMed

    Phellan, Renzo; Forkert, Nils D

    2017-09-07

    Vessel enhancement algorithms are often used as a preprocessing step for vessel segmentation in medical images to improve the overall segmentation accuracy. Each algorithm uses different characteristics to enhance vessels, such that the most suitable algorithm may vary for different applications. This paper presents a comparative analysis of the accuracy gains in vessel segmentation generated by the use of nine vessel enhancement algorithms: Multiscale vesselness using the formulas described by Erdt (MSE), Frangi (MSF), and Sato (MSS), optimally oriented ux (OOF), ranking orientations responses path operator (RORPO), the regularized Perona-Malik approach (RPM), vessel enhanced diffusion (VED), hybrid diffusion with continuous switch (HDCS), and the white top hat algorithm (WTH). The filters were evaluated and compared based on time-of-flight MRA datasets and corresponding manual segmentations from _ve healthy subjects and ten patients with an arteriovenous malformation. Additionally, five synthetic angiographic datasets with corresponding ground truth segmentation were generated with three different noise levels (low, medium, high) and also used for comparison. The parameters for each algorithm and subsequent segmentation were optimized using leave-one-out cross evaluation. The Dice coefficient, Matthews correlation coefficient, area under the ROC curve, number of connected components, and true positives were used for comparison. The results of this study suggest that vessel enhancement algorithms do not always lead to more accurate segmentation results compared to segmenting non-enhanced images directly. Multiscale vesselness algorithms, such as MSE, MSF, and MSS proved to be robust to noise, while diffusion-based filters, such as RPM, VED, and HDCS ranked in the top of the list in scenarios with medium or no noise. Filters that assume tubular-shapes, such as MSE, MSF, MSS, OOF, RORPO, and VED show a decrease in accuracy when considering patients with an AVM

  12. Segmentation and length measurement of the abdominal blood vessels in 3-D MRI images.

    PubMed

    Babin, Danilo; Vansteenkiste, Ewout; Pizurica, Aleksandra; Philips, Wilfried

    2009-01-01

    In diagnosing diseases and planning surgeries the structure and length of blood vessels is of great importance. In this research we develop a novel method for the segmentation of 2-D and 3-D images with an application to blood vessel length measurements in 3-D abdominal MRI images. Our approach is robust to noise and does not require contrast-enhanced images for segmentation. We use an effective algorithm for skeletonization, graph construction and shortest path estimation to measure the length of blood vessels of interest.

  13. Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation.

    PubMed

    Javidi, Malihe; Pourreza, Hamid-Reza; Harati, Ahad

    2017-02-01

    Diabetic retinopathy (DR) is a major cause of visual impairment, and the analysis of retinal image can assist patients to take action earlier when it is more likely to be effective. The accurate segmentation of blood vessels in the retinal image can diagnose DR directly. In this paper, a novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed. The proposed system yields a strong representation which contains the semantic concept of the image. To extract blood vessel, two separate dictionaries, for vessel and non-vessel, capable of providing reconstructive and discriminative information of the retinal image are learned. In the test step, an unseen retinal image is divided into overlapping patches and classified to vessel and non-vessel patches. Then, a voting scheme is applied to generate the binary vessel map. The proposed vessel segmentation method can achieve the accuracy of 95% and a sensitivity of 75% in the same range of specificity 97% on two public datasets. The results show that the proposed method can achieve comparable results to existing methods and decrease false positive vessels in abnormal retinal images with pathological regions. Microaneurysm (MA) is the earliest sign of DR that appears as a small red dot on the surface of the retina. Despite several attempts to develop automated MA detection systems, it is still a challenging problem. In this paper, a method for MA detection, which is similar to our vessel segmentation approach, is proposed. In our method, a candidate detection algorithm based on the Morlet wavelet is applied to identify all possible MA candidates. In the next step, two discriminative dictionaries with the ability to distinguish MA from non-MA object are learned. These dictionaries are then used to classify the detected candidate objects. The evaluations indicate that the proposed MA detection method achieves higher average sensitivity about 2

  14. Sector-based optic cup segmentation with intensity and blood vessel priors.

    PubMed

    Yin, Fengshou; Liu, Jiang; Wong, Damon W K; Tan, Ngan Meng; Cheng, Jun; Cheng, Ching-Yu; Tham, Yih Chung; Wong, Tien Yin

    2012-01-01

    The optic cup segmentation is critical for automated cup-to-disk ratio measurement, and hence computer-aided diagnosis of glaucoma. In this paper, we propose a novel sector-based method for optic cup segmentation. The method comprises two parts: intensity-based cup segmentation with shape constraints and blood vessel-based refinement. The initial estimation of the cup is obtained by applying a statistical deformable model on the vessel free image. At the same time, blood vessels within the optic disk are extracted, after which vessel bendings and vessel boundaries in the nasal side are located. Subsequently, these key points in the blood vessels are used to fine tune the cup. The algorithm is evaluated on 650 fundus images from the ORIGA(-light) database. Experimental results show that the Dice coefficient for the optic cup segmentation can be as high as 0.83, which outperforms other existing methods. The results demonstrate good potential for the proposed method to be used in automated optic cup segmentation and glaucoma diagnosis.

  15. Segmentation of the blood vessels and optic disk in retinal images.

    PubMed

    Salazar-Gonzalez, Ana; Kaba, Djibril; Li, Yongmin; Liu, Xiaohui

    2014-11-01

    Retinal image analysis is increasingly prominent as a nonintrusive diagnosis method in modern ophthalmology. In this paper, we present a novel method to segment blood vessels and optic disk in the fundus retinal images. The method could be used to support nonintrusive diagnosis in modern ophthalmology since the morphology of the blood vessel and the optic disk is an important indicator for diseases like diabetic retinopathy, glaucoma, and hypertension. Our method takes as first step the extraction of the retina vascular tree using the graph cut technique. The blood vessel information is then used to estimate the location of the optic disk. The optic disk segmentation is performed using two alternative methods. The Markov random field (MRF) image reconstruction method segments the optic disk by removing vessels from the optic disk region, and the compensation factor method segments the optic disk using the prior local intensity knowledge of the vessels. The proposed method is tested on three public datasets, DIARETDB1, DRIVE, and STARE. The results and comparison with alternative methods show that our method achieved exceptional performance in segmenting the blood vessel and optic disk.

  16. Segmental anatomy of the liver: poor correlation with CT.

    PubMed

    Fasel, J H; Selle, D; Evertsz, C J; Terrier, F; Peitgen, H O; Gailloud, P

    1998-01-01

    To evaluate qualitatively and quantitatively the current procedures for radiologic delineation of the segmental and subsegmental anatomy of the liver. Vascular casts of 10 livers were examined with helical computed tomography (CT). Liver segmental and subsegmental anatomy were determined on the CT scans according to customary radiologic practice guidelines. CT anatomic findings were compared with authentic anatomic territories seen at anatomic examination. The differences were assessed quantitatively in five of the 10 livers. For the marginal (cranial and caudal) portions of the liver, an average (+/- 1 standard deviation) of 17.3% +/- 6.5 of the hepatic area visualized on axial CT scans was attributed to an incorrect subsegment. For the central zones (those adjacent to the right and left branches of the portal vein), this error amounted to 51.6% +/- 19.9. Expressed in absolute numbers, the error amounted to 40 mm on axial CT scans. The radiologic determination of portal venous territories within the liver must be revised. The indirect landmarks currently used are not reliable for proper delineation. Only procedures that account for the portal venous distribution pattern, including peripheral branches, will result in correct depiction of the complex and variable anatomic reality.

  17. Engineering design and integration of in-vessel single turn segmental coil in vacuum vessel of SST-1

    NASA Astrophysics Data System (ADS)

    Jayswal, Snehal; Chauhan, P.; Santra, P.; Vasava, K.; Perekh, T.; Patel, H.; Biswas, P.; Pradhan, S.

    2017-04-01

    SST-1 tokamak is having the error field due to unsymmetrical positioning of Toroidal field coils which push the plasma to inner side from its major radius of 1100 mm. hence it is required to install the In-vessel Coil (PF6) at a location of 1350 mm radius and elevation of 350 mm above and below the mid plane of the toroidal field coils. The In-Vessel coil was decided to make in eight segments for futuristic use, to control the individual localized error field correction by supplying the different current. A single turn, eight segments, copper conductor with 18 mm diameter with GFRP insulation and in housed in SS304 L casing to carry 8000 A current for 10 s was designed and installed in vacuum vessel of SST-1. This paper will present the design drivers, material selection, advantages and constraints of the in-vessel coils, its conceptual and engineering design, CAD models, finite element analysis using ANSYS, its fabrication, quality assurance/control and assembly/integration aspects inside vacuum vessel of SST-1.

  18. Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography

    PubMed Central

    Shim, Hackjoon; Jeon, Byunghwan; Jang, Yeonggul; Hong, Youngtaek; Jung, Sunghee; Ha, Seongmin; Chang, Hyuk-Jae

    2016-01-01

    We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown. PMID:27536939

  19. Automatic blood vessels segmentation based on different retinal maps from OCTA scans.

    PubMed

    Eladawi, Nabila; Elmogy, Mohammed; Helmy, Omar; Aboelfetouh, Ahmed; Riad, Alaa; Sandhu, Harpal; Schaal, Shlomit; El-Baz, Ayman

    2017-08-07

    The retinal vascular network reflects the health of the retina, which is a useful diagnostic indicator of systemic vascular. Therefore, the segmentation of retinal blood vessels is a powerful method for diagnosing vascular diseases. This paper presents an automatic segmentation system for retinal blood vessels from Optical Coherence Tomography Angiography (OCTA) images. The system segments blood vessels from the superficial and deep retinal maps for normal and diabetic cases. Initially, we reduced the noise and improved the contrast of the OCTA images by using the Generalized Gauss-Markov random field (GGMRF) model. Secondly, we proposed a joint Markov-Gibbs random field (MGRF) model to segment the retinal blood vessels from other background tissues. It integrates both appearance and spatial models in addition to the prior probability model of OCTA images. The higher order MGRF (HO-MGRF) model in addition to the 1(st)-order intensity model are used to consider the spatial information in order to overcome the low contrast between vessels and other tissues. Finally, we refined the segmentation by extracting connected regions using a 2D connectivity filter. The proposed segmentation system was trained and tested on 47 data sets, which are 23 normal data sets and 24 data sets for diabetic patients. To evaluate the accuracy and robustness of the proposed segmentation framework, we used three different metrics, which are Dice similarity coefficient (DSC), absolute vessels volume difference (VVD), and area under the curve (AUC). The results on OCTA data sets (DSC=95.04±3.75%, VVD=8.51±1.49%, and AUC=95.20±1.52%) show the promise of the proposed segmentation approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Blood vessel segmentation using line-direction vector based on Hessian analysis

    NASA Astrophysics Data System (ADS)

    Nimura, Yukitaka; Kitasaka, Takayuki; Mori, Kensaku

    2010-03-01

    For decision of the treatment strategy, grading of stenoses is important in diagnosis of vascular disease such as arterial occlusive disease or thromboembolism. It is also important to understand the vasculature in minimally invasive surgery such as laparoscopic surgery or natural orifice translumenal endoscopic surgery. Precise segmentation and recognition of blood vessel regions are indispensable tasks in medical image processing systems. Previous methods utilize only ``lineness'' measure, which is computed by Hessian analysis. However, difference of the intensity values between a voxel of thin blood vessel and a voxel of surrounding tissue is generally decreased by the partial volume effect. Therefore, previous methods cannot extract thin blood vessel regions precisely. This paper describes a novel blood vessel segmentation method that can extract thin blood vessels with suppressing false positives. The proposed method utilizes not only lineness measure but also line-direction vector corresponding to the largest eigenvalue in Hessian analysis. By introducing line-direction information, it is possible to distinguish between a blood vessel voxel and a voxel having a low lineness measure caused by noise. In addition, we consider the scale of blood vessel. The proposed method can reduce false positives in some line-like tissues close to blood vessel regions by utilization of iterative region growing with scale information. The experimental result shows thin blood vessel (0.5 mm in diameter, almost same as voxel spacing) can be extracted finely by the proposed method.

  1. Automated vessel shadow segmentation of fovea-centered spectral-domain images from multiple OCT devices

    NASA Astrophysics Data System (ADS)

    Wu, Jing; Gerendas, Bianca S.; Waldstein, Sebastian M.; Simader, Christian; Schmidt-Erfurth, Ursula

    2014-03-01

    Spectral-domain Optical Coherence Tomography (SD-OCT) is a non-invasive modality for acquiring high reso- lution, three-dimensional (3D) cross sectional volumetric images of the retina and the subretinal layers. SD-OCT also allows the detailed imaging of retinal pathology, aiding clinicians in the diagnosis of sight degrading diseases such as age-related macular degeneration (AMD) and glaucoma.1 Disease diagnosis, assessment, and treatment requires a patient to undergo multiple OCT scans, possibly using different scanning devices, to accurately and precisely gauge disease activity, progression and treatment success. However, the use of OCT imaging devices from different vendors, combined with patient movement may result in poor scan spatial correlation, potentially leading to incorrect patient diagnosis or treatment analysis. Image registration can be used to precisely compare disease states by registering differing 3D scans to one another. In order to align 3D scans from different time- points and vendors using registration, landmarks are required, the most obvious being the retinal vasculature. Presented here is a fully automated cross-vendor method to acquire retina vessel locations for OCT registration from fovea centred 3D SD-OCT scans based on vessel shadows. Noise filtered OCT scans are flattened based on vendor retinal layer segmentation, to extract the retinal pigment epithelium (RPE) layer of the retina. Voxel based layer profile analysis and k-means clustering is used to extract candidate vessel shadow regions from the RPE layer. In conjunction, the extracted RPE layers are combined to generate a projection image featuring all candidate vessel shadows. Image processing methods for vessel segmentation of the OCT constructed projection image are then applied to optimize the accuracy of OCT vessel shadow segmentation through the removal of false positive shadow regions such as those caused by exudates and cysts. Validation of segmented vessel shadows uses

  2. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.

    PubMed

    Karasawa, Ken'ichi; Oda, Masahiro; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Chu, Chengwen; Zheng, Guoyan; Rueckert, Daniel; Mori, Kensaku

    2017-03-31

    Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%.

  3. Generalization of geometrical flux maximizing flow on Riemannian manifolds for improved volumetric blood vessel segmentation.

    PubMed

    Gooya, Ali; Liao, Hongen; Sakuma, Ichiro

    2012-09-01

    Geometric flux maximizing flow (FLUX) is an active contour based method which evolves an initial surface to maximize the flux of a vector field on the surface. For blood vessel segmentation, the vector field is defined as the vectors specified by vascular edge strengths and orientations. Hence, the segmentation performance depends on the quality of the detected edge vector field. In this paper, we propose a new method for level set based segmentation of blood vessels by generalizing the FLUX on a Riemannian manifold (R-FLUX). We consider a 3D scalar image I(x) as a manifold embedded in the 4D space (x, I(x)) and compute the image metric by pullback from the 4D space, whose metric tensor depends on the vessel enhancing diffusion (VED) tensor. This allows us to devise a non-linear filter which both projects and normalizes the original image gradient vectors under the inverse of local VED tensors. The filtered gradient vectors pertaining to the vessels are less sensitive to the local image contrast and more coherent with the local vessel orientation. The method has been applied to both synthetic and real TOF MRA data sets. Comparisons are made with the FLUX and vesselsness response based segmentations, indicating that the R-FLUX outperforms both methods in terms of leakage minimization and thiner vessel delineation.

  4. A liver registration method for segmented multi-phase CT images

    NASA Astrophysics Data System (ADS)

    Shi, Shuyue; Yuan, Rong; Sun, Zhi; Xie, Qingguo

    2015-03-01

    In order to build high quality geometric models for liver containing vascular system, multi-phase CT series used in a computer-aided diagnosis and surgical planning system aims at liver diseases have to be accurately registered. In this paper we model the segmented liver containing vascular system as a complex shape and propose a two-step registration method. Without any tree modeling for vessel this method can carry out a simultaneous registration for both liver tissue and vascular system inside. Firstly a rigid aligning using vessel as feature is applied on the complex shape model while genetic algorithm is used as the optimization method. Secondly we achieve the elastic shape registration by combine the incremental free form deformation (IFFD) with a modified iterative closest point (ICP) algorithm. Inspired by the concept of demons method, we propose to calculate a fastest diffusion vector (FDV) for each control point on the IFFD lattice to replace the points correspondence needed in ICP iterations. Under the iterative framework of the modified ICP, the optimal solution of control points' displacement in every IFFD level can be obtained efficiently. The method has been quantitatively evaluated on clinical multi-phase CT series.

  5. Segmentation of Opacified Thorax Vessels using Model-driven Active Contour.

    PubMed

    Sebbe, Raphael; Gosselin, Bernard; Coche, Emmanuel; Macq, Benoit

    2005-01-01

    We propose a novel method, guided slice marching to segment opacified vessels tree in 3D image sets (CT scans). It combines a front propagation technique, slice marching, and an anatomical model to guide the propagation for solving the particular case of touching vessels. The formulation of this method, which is based on interface evolution theory, enables easy integration of an a priori model of knowledge of vessels topology to handle the case of touching vessels, where image-based method systematically fails. The a priori knowledge is expressed as parametric curves that model vessels centerline. That information is injected in the fast marching method through the speed of propagation, setting it to zero at missing vessels boundaries. The model is intended to be reused across patients, and must therefore be registered with the image.

  6. Active Contours Driven by Multi-Feature Gaussian Distribution Fitting Energy with Application to Vessel Segmentation

    PubMed Central

    Wang, Lei; Zhang, Huimao; He, Kan; Chang, Yan; Yang, Xiaodong

    2015-01-01

    Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and ‘vesselness values’ from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width. PMID:26571031

  7. Active Contours Driven by Multi-Feature Gaussian Distribution Fitting Energy with Application to Vessel Segmentation.

    PubMed

    Wang, Lei; Zhang, Huimao; He, Kan; Chang, Yan; Yang, Xiaodong

    2015-01-01

    Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and 'vesselness values' from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width.

  8. A novel lung nodules detection scheme based on vessel segmentation on CT images.

    PubMed

    Jia, Tong; Zhang, Hao; Meng, Haixiu

    2014-01-01

    Lung vessels often interfere with the detection of lung nodules. In this paper, a novel computer-aided lung nodule detection scheme on vessel segmentation is proposed. This paper describes an active contour model which can combine image region mean gray value and image edge energy. It is used to segment and remove lung vessels. A selective shape filter based on Hessian Matrix is used to detect suspicious nodules and remove omitted lung vessels. This paper extracts density, shape and position features of suspicious nodules, and uses a Rule-Based Classification (RBC) method to identify true positive nodules. In the experiment results, the detection sensitivity is about 90% and FP is 1/scan.

  9. Leveraging Multiscale Hessian-Based Enhancement With a Novel Exudate Inpainting Technique for Retinal Vessel Segmentation.

    PubMed

    Annunziata, Roberto; Garzelli, Andrea; Ballerini, Lucia; Mecocci, Alessandro; Trucco, Emanuele

    2016-07-01

    Accurate vessel detection in retinal images is an important and difficult task. Detection is made more challenging in pathological images with the presence of exudates and other abnormalities. In this paper, we present a new unsupervised vessel segmentation approach to address this problem. A novel inpainting filter, called neighborhood estimator before filling, is proposed to inpaint exudates in a way that nearby false positives are significantly reduced during vessel enhancement. Retinal vascular enhancement is achieved with a multiple-scale Hessian approach. Experimental results show that the proposed vessel segmentation method outperforms state-of-the-art algorithms reported in the recent literature, both visually and in terms of quantitative measurements, with overall mean accuracy of 95.62% on the STARE dataset and 95.81% on the HRF dataset.

  10. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction.

    PubMed

    Mendonça, Ana Maria; Campilho, Aurélio

    2006-09-01

    This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width dependent morphological filters. Our approach was tested on two publicly available databases and its results are compared with recently published methods. The results demonstrate that our algorithm outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity.

  11. Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering.

    PubMed

    Saffarzadeh, Vahid Mohammadi; Osareh, Alireza; Shadgar, Bita

    2014-04-01

    Detecting blood vessels is a vital task in retinal image analysis. The task is more challenging with the presence of bright and dark lesions in retinal images. Here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. First, the negative impact of bright lesions is reduced by using K-means segmentation in a perceptive space. Then, a multi-scale line operator is utilized to detect vessels while ignoring some of the dark lesions, which have intensity structures different from the line-shaped vessels in the retina. The proposed algorithm is tested on two publicly available STARE and DRIVE databases. The performance of the method is measured by calculating the area under the receiver operating characteristic curve and the segmentation accuracy. The proposed method achieves 0.9483 and 0.9387 localization accuracy against STARE and DRIVE respectively.

  12. IFCM Based Segmentation Method for Liver Ultrasound Images.

    PubMed

    Jain, Nishant; Kumar, Vinod

    2016-11-01

    In this paper we have proposed an iterative Fuzzy C-Mean (IFCM) method which divides the pixels present in the image into a set of clusters. This set of clusters is then used to segment a focal liver lesion from a liver ultrasound image. Advantage of IFCM methods is that n-clusters FCM method may lead to non-uniform distribution of centroids, whereas in IFCM method centroids will always be uniformly distributed. Proposed method is compared with the edge based Active contour Chan-Vese (CV) method, and MAP-MRF method by implementing the methods on MATLAB. Proposed method is also compared with region based active contour region-scalable fitting energy (RSFE) method whose MATLAB code is available in author's website. Since no comparison is available on a common database, the performance of three methods and the proposed method have been compared on liver ultrasound (US) images available with us. Proposed method gives the best accuracy of 99.8 % as compared to accuracy of 99.46 %, 95.81 % and 90.08 % given by CV, MAP-MRF and RSFE methods respectively. Computation time taken by the proposed segmentation method for segmentation is 14.25 s as compared to 44.71, 41.27 and 49.02 s taken by CV, MAP-MRF and RSFE methods respectively.

  13. An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding.

    PubMed

    Saleh, Marwan D; Eswaran, C

    2012-01-01

    Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.

  14. Liver Ultrasound Image Segmentation Using Region-Difference Filters.

    PubMed

    Jain, Nishant; Kumar, Vinod

    2016-12-26

    In this paper, region-difference filters for the segmentation of liver ultrasound (US) images are proposed. Region-difference filters evaluate maximum difference of the average of two regions of the window around the center pixel. Implementing the filters on the whole image gives region-difference image. This image is then converted into binary image and morphologically operated for segmenting the desired lesion from the ultrasound image. The proposed method is compared with the maximum a posteriori-Markov random field (MAP-MRF), Chan-Vese active contour method (CV-ACM), and active contour region-scalable fitting energy (RSFE) methods. MATLAB code available online for the RSFE method is used for comparison whereas MAP-MRF and CV-ACM methods are coded in MATLAB by authors. Since no comparison is available on common database for the performance of the three methods, therefore, performance comparison of the three methods and proposed method was done on liver US images obtained from PGIMER, Chandigarh, India and from online resource. A radiologist blindly analyzed segmentation results of the 4 methods implemented on 56 images and had selected the segmentation result obtained from the proposed method as best for 46 test US images. For the remaining 10 US images, the proposed method performance was very near to the other three segmentation methods. The proposed segmentation method obtained the overall accuracy of 99.32% in comparison to the overall accuracy of 85.9, 98.71, and 68.21% obtained by MAP-MRF, CV-ACM, and RSFE methods, respectively. Computational time taken by the proposed method is 5.05 s compared to the time of 26.44, 24.82, and 28.36 s taken by MAP-MRF, CV-ACM, and RSFE methods, respectively.

  15. Retinal vessel segmentation: an efficient graph cut approach with retinex and local phase.

    PubMed

    Zhao, Yitian; Liu, Yonghuai; Wu, Xiangqian; Harding, Simon P; Zheng, Yalin

    2015-01-01

    Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer.

  16. Dual-wavelength retinal image registration based on vessel segmentation and optic disc detection

    NASA Astrophysics Data System (ADS)

    Xian, Yong-li; Dai, Yun; Gao, Chun-ming; Du, Rui

    2016-09-01

    The dual-wavelength retinal image registration is one of the critical steps in the spectrophotometric measurements of oxygen saturation in the retinal vasculature. The dual-wavelength images (570 nm and 600 nm) are simultaneously captured by dual-wavelength retinal oximeter based on commercial fundus camera. The retinal oxygen saturation is finally measured after vessel segmentation, image registration and calculation of optical density ratio of the two images. Because the dual-wavelength images are acquired from different optical path, it is necessary to go through image registration before they are used to analyze the oxygen saturation. This paper presents a new approach to dual-wavelength retinal image registration based on vessel segmentation and optic disc detection. Firstly, the multi-scale segmentation algorithm based on the Hessian matrix is used to realize vessel segmentation. Secondly, after optic disc is detected by convergence index filter and the center of the optic disc is obtained by centriod algorithm, the translational difference between the images can be determined. The center of the optic disc is used as the center of rotation, and the registration based on mutual information can be achieved using contour and gray information of vessels through segmented image. So the rotational difference between the images can be determined too. The result shows that the algorithm can provide an accurate registration for the dual-wavelength retinal image.

  17. Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy

    NASA Astrophysics Data System (ADS)

    Mann, Kulwinder S.; Kaur, Sukhpreet

    2017-06-01

    There are various eye diseases in the patients suffering from the diabetes which includes Diabetic Retinopathy, Glaucoma, Hypertension etc. These all are the most common sight threatening eye diseases due to the changes in the blood vessel structure. The proposed method using supervised methods concluded that the segmentation of the retinal blood vessels can be performed accurately using neural networks training. It uses features which include Gray level features; Moment Invariant based features, Gabor filtering, Intensity feature, Vesselness feature for feature vector computation. Then the feature vector is calculated using only the prominent features.

  18. A Segmentation Algorithm for X-ray 3D Angiography and Vessel Catheterization

    SciTech Connect

    Franchi, Danilo; Rosa, Luigi; Placidi, Giuseppe

    2008-11-06

    Vessel Catheterization is a clinical procedure usually performed by a specialist by means of X-ray fluoroscopic guide with contrast-media. In the present paper, we present a simple and efficient algorithm for vessel segmentation which allows vessel separation and extraction from the background (noise and signal coming from other organs). This would reduce the number of projections (X-ray scans) to reconstruct a complete and accurate 3D vascular model and the radiological risk, in particular for the patient. In what follows, the algorithm is described and some preliminary experimental results are reported illustrating the behaviour of the proposed method.

  19. Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels.

    PubMed

    Waheed, Amna; Akram, M Usman; Khalid, Shehzad; Waheed, Zahra; Khan, Muazzam A; Shaukat, Arslan

    2015-10-01

    Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic system for retinal diseases. Most popular methods for vessel segmentation are based on matched filters and Gabor wavelets which give good response against blood vessels. One major drawback in these techniques is that they also give strong response for lesion (exudates, hemorrhages) boundaries which give rise to false vessels. These false vessels may lead to incorrect detection of vascular changes. In this paper, we propose a new hybrid feature set along with new classification technique for accurate detection of blood vessels. The main motivation is to lower the false positives especially from retinal images with severe disease level. A novel region based hybrid feature set is presented for proper discrimination between true and false vessels. A new modified m-mediods based classification is also presented which uses most discriminating features to categorize vessel regions into true and false vessels. The evaluation of proposed system is done thoroughly on publicly available databases along with a locally gathered database with images of advanced level of retinal diseases. The results demonstrate the validity of the proposed system as compared to existing state of the art techniques.

  20. Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.

    PubMed

    Du, Jiang; Karimi, Afshin; Wu, Yijing; Korosec, Frank R; Grist, Thomas M; Mistretta, Charles A

    2011-04-01

    Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique.

  1. Segmentation of blood vessels from red-free and fluorescein retinal images.

    PubMed

    Martinez-Perez, M Elena; Hughes, Alun D; Thom, Simon A; Bharath, Anil A; Parker, Kim H

    2007-02-01

    The morphology of the retinal blood vessels can be an important indicator for diseases like diabetes, hypertension and retinopathy of prematurity (ROP). Thus, the measurement of changes in morphology of arterioles and venules can be of diagnostic value. Here we present a method to automatically segment retinal blood vessels based upon multiscale feature extraction. This method overcomes the problem of variations in contrast inherent in these images by using the first and second spatial derivatives of the intensity image that gives information about vessel topology. This approach also enables the detection of blood vessels of different widths, lengths and orientations. The local maxima over scales of the magnitude of the gradient and the maximum principal curvature of the Hessian tensor are used in a multiple pass region growing procedure. The growth progressively segments the blood vessels using feature information together with spatial information. The algorithm is tested on red-free and fluorescein retinal images, taken from two local and two public databases. Comparison with first public database yields values of 75.05% true positive rate (TPR) and 4.38% false positive rate (FPR). Second database values are of 72.46% TPR and 3.45% FPR. Our results on both public databases were comparable in performance with other authors. However, we conclude that these values are not sensitive enough so as to evaluate the performance of vessel geometry detection. Therefore we propose a new approach that uses measurements of vessel diameters and branching angles as a validation criterion to compare our segmented images with those hand segmented from public databases. Comparisons made between both hand segmented images from public databases showed a large inter-subject variability on geometric values. A last evaluation was made comparing vessel geometric values obtained from our segmented images between red-free and fluorescein paired images with the latter as the "ground truth

  2. Atlas-based analysis of 4D flow CMR: automated vessel segmentation and flow quantification.

    PubMed

    Bustamante, Mariana; Petersson, Sven; Eriksson, Jonatan; Alehagen, Urban; Dyverfeldt, Petter; Carlhäll, Carl-Johan; Ebbers, Tino

    2015-10-05

    Flow volume quantification in the great thoracic vessels is used in the assessment of several cardiovascular diseases. Clinically, it is often based on semi-automatic segmentation of a vessel throughout the cardiac cycle in 2D cine phase-contrast Cardiovascular Magnetic Resonance (CMR) images. Three-dimensional (3D), time-resolved phase-contrast CMR with three-directional velocity encoding (4D flow CMR) permits assessment of net flow volumes and flow patterns retrospectively at any location in a time-resolved 3D volume. However, analysis of these datasets can be demanding. The aim of this study is to develop and evaluate a fully automatic method for segmentation and analysis of 4D flow CMR data of the great thoracic vessels. The proposed method utilizes atlas-based segmentation to segment the great thoracic vessels in systole, and registration between different time frames of the cardiac cycle in order to segment these vessels over time. Additionally, net flow volumes are calculated automatically at locations of interest. The method was applied on 4D flow CMR datasets obtained from 11 healthy volunteers and 10 patients with heart failure. Evaluation of the method was performed visually, and by comparison of net flow volumes in the ascending aorta obtained automatically (using the proposed method), and semi-automatically. Further evaluation was done by comparison of net flow volumes obtained automatically at different locations in the aorta, pulmonary artery, and caval veins. Visual evaluation of the generated segmentations resulted in good outcomes for all the major vessels in all but one dataset. The comparison between automatically and semi-automatically obtained net flow volumes in the ascending aorta resulted in very high correlation (r (2)=0.926). Moreover, comparison of the net flow volumes obtained automatically in other vessel locations also produced high correlations where expected: pulmonary trunk vs. proximal ascending aorta (r (2)=0.955), pulmonary

  3. Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease

    NASA Astrophysics Data System (ADS)

    Kim, Youngwoo; Bae, Sonu K.; Cheng, Tianming; Tao, Cheng; Ge, Yinghui; Chapman, Arlene B.; Torres, Vincente E.; Yu, Alan S. L.; Mrug, Michal; Bennett, William M.; Flessner, Michael F.; Landsittel, Doug P.; Bae, Kyongtae T.

    2016-11-01

    Liver and liver cyst volume measurements are important quantitative imaging biomarkers for assessment of disease progression in autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD). To date, no study has presented automated segmentation and volumetric computation of liver and liver cysts in these populations. In this paper, we proposed an automated segmentation framework for liver and liver cysts from bounded abdominal MR images in patients with ADPKD. To model the shape and variations in ADPKD livers, the spatial prior probability map (SPPM) of liver location and the tissue prior probability maps (TPPMs) of liver parenchymal tissue intensity and cyst morphology were generated. Formulated within a three-dimensional level set framework, the TPPMs successfully captured liver parenchymal tissues and cysts, while the SPPM globally constrained the initial surfaces of the liver into the desired boundary. Liver cysts were extracted by combined operations of the TPPMs, thresholding, and false positive reduction based on spatial prior knowledge of kidney cysts and distance map. With cross-validation for the liver segmentation, the agreement between the radiology expert and the proposed method was 84% for shape congruence and 91% for volume measurement assessed by the intra-class correlation coefficient (ICC). For the liver cyst segmentation, the agreement between the reference method and the proposed method was ICC  =  0.91 for cyst volumes and ICC  =  0.94 for % cyst-to-liver volume.

  4. Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease.

    PubMed

    Kim, Youngwoo; Bae, Sonu K; Cheng, Tianming; Tao, Cheng; Ge, Yinghui; Chapman, Arlene B; Torres, Vincente E; Yu, Alan S L; Mrug, Michal; Bennett, William M; Flessner, Michael F; Landsittel, Doug P; Bae, Kyongtae T

    2016-11-21

    Liver and liver cyst volume measurements are important quantitative imaging biomarkers for assessment of disease progression in autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD). To date, no study has presented automated segmentation and volumetric computation of liver and liver cysts in these populations. In this paper, we proposed an automated segmentation framework for liver and liver cysts from bounded abdominal MR images in patients with ADPKD. To model the shape and variations in ADPKD livers, the spatial prior probability map (SPPM) of liver location and the tissue prior probability maps (TPPMs) of liver parenchymal tissue intensity and cyst morphology were generated. Formulated within a three-dimensional level set framework, the TPPMs successfully captured liver parenchymal tissues and cysts, while the SPPM globally constrained the initial surfaces of the liver into the desired boundary. Liver cysts were extracted by combined operations of the TPPMs, thresholding, and false positive reduction based on spatial prior knowledge of kidney cysts and distance map. With cross-validation for the liver segmentation, the agreement between the radiology expert and the proposed method was 84% for shape congruence and 91% for volume measurement assessed by the intra-class correlation coefficient (ICC). For the liver cyst segmentation, the agreement between the reference method and the proposed method was ICC  =  0.91 for cyst volumes and ICC  =  0.94 for % cyst-to-liver volume.

  5. Learning fully-connected CRFs for blood vessel segmentation in retinal images.

    PubMed

    Orlando, José Ignacio; Blaschko, Matthew

    2014-01-01

    In this work, we present a novel method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Retinal image analysis is greatly aided by blood vessel segmentation as the vessel structure may be considered both a key source of signal, e.g. in the diagnosis of diabetic retinopathy, or a nuisance, e.g. in the analysis of pigment epithelium or choroid related abnormalities. Blood vessel segmentation in fundus images has been considered extensively in the literature, but remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors such as a Potts model or total variation. In this work, we overcome this difficulty using a discriminatively trained conditional random field model with more expressive potentials. In particular, we employ recent results enabling extremely fast inference in a fully connected model. We find that this rich but computationally efficient model family, combined with principled discriminative training based on a structured output support vector machine yields a fully automated system that achieves results statistically indistinguishable from an expert human annotator. Implementation details are available at http://pages.saclay.inria.fr/ matthew.blaschko/projects/retina/.

  6. Active double contour for segmentation of vessels in digital subtraction angiography

    NASA Astrophysics Data System (ADS)

    Hinz, Manfred; Toennies, Klaus D.; Grohmann, Markus; Pohle, Regina

    2001-07-01

    Successful extraction of small vessels in DSA images requires inclusion of prior knowledge about vessel characteristics. We developed an active double contour (ADC) that uses a vessel template as a model. The template is fitted to the vessel using an adapted ziplock snake approach based on two user-specified end locations. The external energy terms of the ADC describe an ideal vessel with projections changing slowly their course, width and intensity. A backtracking ability was added that enables overturning local decisions that may cause the ziplock snake to be trapped in a local minimum. This is because the optimization of the ADC is carried out locally. If the total energy indicates such case, vessel boundary points are removed and the ziplock process starts again without this location in its actual configuration. The method was tested on artificial data and DSA data. The former showed good agreement between artificial vessel and segmented structure at an SNR as low as 1.5:1. Results from DSA data showed robustness of the method in the presence of noise and its ability to cope with branchings and crossings. The backtracking was found to overcome local minima of the energy function at artefacts, vessel crossings and in regions of low SNR.

  7. A comparison between two robust techniques for segmentation of blood vessels.

    PubMed

    Rodríguez, Roberto; Castillo, Patricio J; Guerra, Valia; Azuela, Juan Humberto Sossa; Suáreza, Ana G; Izquierdo, Ebroul

    2008-08-01

    Image segmentation plays an important role in image analysis. According to several authors, segmentation terminates when the observer's goal is satisfied. For this reason, a unique method that can be applied to all possible cases does not yet exist. In this paper, we have carried out a comparison between two current segmentation techniques, namely the mean shift method, for which we propose a new algorithm, and the so-called spectral method. In this investigation the important information to be extracted from an image is the number of blood vessels (BV) present in the image. The results obtained by both strategies were compared with the results provided by manual segmentation. We have found that using the mean shift segmentation an error less than 20% for false positives (FP) and 0% for false negatives (FN) was observed, while for the spectral method more than 45% for FP and 0% for FN were obtained. We discuss the advantages and disadvantages of both methods.

  8. Automatic segmentation of lymph vessel wall using optimal surface graph cut and hidden Markov Models.

    PubMed

    Jones, Jonathan-Lee; Essa, Ehab; Xie, Xianghua

    2015-01-01

    We present a novel method to segment the lymph vessel wall in confocal microscopy images using Optimal Surface Segmentation (OSS) and hidden Markov Models (HMM). OSS is used to preform a pre-segmentation on the images, to act as the initial state for the HMM. We utilize a steerable filter to determine edge based filters for both of these segmentations, and use these features to build Gaussian probability distributions for both the vessel walls and the background. From this we infer the emission probability for the HMM, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into one of cost minimization, with each node in the graph corresponding to one state, and the weight for each node being defined using its emission probability. We define the inter-relations between neighboring nodes using the transmission probability. Having constructed the problem, it is solved using the Viterbi algorithm, allowing the vessel to be reconstructed. The optimal solution can be found in polynomial time. We present qualitative and quantitative analysis to show the performance of the proposed method.

  9. Supervised recursive segmentation of volumetric CT images for 3D reconstruction of lung and vessel tree.

    PubMed

    Li, Xuanping; Wang, Xue; Dai, Yixiang; Zhang, Pengbo

    2015-12-01

    Three dimensional reconstruction of lung and vessel tree has great significance to 3D observation and quantitative analysis for lung diseases. This paper presents non-sheltered 3D models of lung and vessel tree based on a supervised semi-3D lung tissues segmentation method. A recursive strategy based on geometric active contour is proposed instead of the "coarse-to-fine" framework in existing literature to extract lung tissues from the volumetric CT slices. In this model, the segmentation of the current slice is supervised by the result of the previous one slice due to the slight changes between adjacent slice of lung tissues. Through this mechanism, lung tissues in all the slices are segmented fast and accurately. The serious problems of left and right lungs fusion, caused by partial volume effects, and segmentation of pleural nodules can be settled meanwhile during the semi-3D process. The proposed scheme is evaluated by fifteen scans, from eight healthy participants and seven participants suffering from early-stage lung tumors. The results validate the good performance of the proposed method compared with the "coarse-to-fine" framework. The segmented datasets are utilized to reconstruct the non-sheltered 3D models of lung and vessel tree.

  10. Segmentation of vessels in retinal images based on directional height statistics.

    PubMed

    Lazar, Istvan; Hajdu, Andras

    2012-01-01

    In this paper we present a fast and simple, yet accurate method for the segmentation of retinal blood vessels. Many diseases of the eye result in the distortions of the vessels. The precise location of the major optic veins may be used for the localization of other anatomical parts, such as the macula and the optic disc. Also, many microaneurysm detection methods consider an additional vessel segmentation step. The proposed method realizes the recognition of vessels through considering cross-sections of the image at different orientations. Peaks on the profiles are localized and their heights are measured. This way, a set of height values are assigned to every pixel of the image. Simple statistics are calculated for every pixel, and combined to construct a vessel score map. We apply a simple thresholding procedure and postprocessing step to obtain a binary vessel mask. The method has been tested on the publicly available DRIVE database, and it proved to be competitive with the state-of-the-art.

  11. Hepatic vessel segmentation using variational level set combined with non-local robust statistics.

    PubMed

    Lu, Siyu; Huang, Hui; Liang, Ping; Chen, Gang; Xiao, Liang

    2017-02-01

    Hepatic vessel segmentation is a challenging step in therapy guided by magnetic resonance imaging (MRI). This paper presents an improved variational level set method, which uses non-local robust statistics to suppress the influence of noise in MR images. The non-local robust statistics, which represent vascular features, are learned adaptively from seeds provided by users. K-means clustering in neighborhoods of seeds is utilized to exclude inappropriate seeds, which are obviously corrupted by noise. The neighborhoods of appropriate seeds are placed in an array to calculate the non-local robust statistics, and the variational level set formulation can be constructed. Bias correction is utilized in the level set formulation to reduce the influence of intensity inhomogeneity of MRI. Experiments were conducted over real MR images, and showed that the proposed method performed better on small hepatic vessel segmentation compared with other segmentation methods.

  12. A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images.

    PubMed

    Li, Qiaoliang; Feng, Bowei; Xie, LinPei; Liang, Ping; Zhang, Huisheng; Wang, Tianfu

    2016-01-01

    This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.

  13. Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information.

    PubMed

    Mapayi, Temitope; Viriri, Serestina; Tapamo, Jules-Raymond

    2015-01-01

    Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal image demonstrates the high performance of the proposed local adaptive thresholding technique. The maximum average accuracy rates of 0.9511 and 0.9510 with maximum average sensitivity rates of 0.7650 and 0.7641 were achieved on DRIVE and STARE databases, respectively. When compared to the widely previously used techniques on the databases, the proposed adaptive thresholding technique is time efficient with a higher average sensitivity and average accuracy rates in the same range of very good specificity.

  14. Segmentation of Blood Vessels and 3D Representation of CMR Image

    NASA Astrophysics Data System (ADS)

    Jiji, G. W.

    2013-06-01

    Current cardiac magnetic resonance imaging (CMR) technology allows the determination of patient-individual coronary tree structure, detection of infarctions, and assessment of myocardial perfusion. The purpose of this work is to segment heart blood vessels and visualize it in 3D. In this work, 3D visualisation of vessel was performed into four phases. The first step is to detect the tubular structures using multiscale medialness function, which distinguishes tube-like structures from and other structures. Second step is to extract the centrelines of the tubes. From the centreline radius the cylindrical tube model is constructed. The third step is segmentation of the tubular structures. The cylindrical tube model is used in segmentation process. Fourth step is to 3D representation of the tubular structure using Volume . The proposed approach is applied to 10 datasets of patients from the clinical routine and tested the results with radiologists.

  15. Hepatic vessel segmentation from computed tomography using three-dimensional hyper-complex edge detection operator

    NASA Astrophysics Data System (ADS)

    Ma, Yang; Li, Xingmin

    2014-01-01

    This paper proposes a three-dimensional(3D) segmentation algorithm using hyper-complex edge detection operator and applies the new algorithm to three-dimensional hepatic vessel segmentation from computed tomography (CT) volumetric data. A 3D hyper-complex edge detection operator is constructed by combining octonion and gradient operator. We replace every voxel of the volumetric data by one octonion which consist of its gray-level and its 6 neighborhoods' gray-level. Via this the original volumetric data is defined as octonion volumetric data. Similar to the Sobel operator, there are three principal directions (coordinate axes) in 3D hyper-complex edge detection operator, and each element in this operator is a octonion. The operator is circularly convoluted with octonion volumetric data to get the value of matching response. If matched, this voxel is the edge of vessel. Experimental results show that the algorithm can effectively segment small vascular tree branches.

  16. Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes

    NASA Astrophysics Data System (ADS)

    Zheng, Yefeng; Loziczonek, Maciej; Georgescu, Bogdan; Zhou, S. Kevin; Vega-Higuera, Fernando; Comaniciu, Dorin

    2011-03-01

    Automatic coronary centerline extraction and lumen segmentation facilitate the diagnosis of coronary artery disease (CAD), which is a leading cause of death in developed countries. Various coronary centerline extraction methods have been proposed and most of them are based on shortest path computation given one or two end points on the artery. The major variation of the shortest path based approaches is in the different vesselness measurements used for the path cost. An empirically designed measurement (e.g., the widely used Hessian vesselness) is by no means optimal in the use of image context information. In this paper, a machine learning based vesselness is proposed by exploiting the rich domain specific knowledge embedded in an expert-annotated dataset. For each voxel, we extract a set of geometric and image features. The probabilistic boosting tree (PBT) is then used to train a classifier, which assigns a high score to voxels inside the artery and a low score to those outside. The detection score can be treated as a vesselness measurement in the computation of the shortest path. Since the detection score measures the probability of a voxel to be inside the vessel lumen, it can also be used for the coronary lumen segmentation. To speed up the computation, we perform classification only for voxels around the heart surface, which is achieved by automatically segmenting the whole heart from the 3D volume in a preprocessing step. An efficient voxel-wise classification strategy is used to further improve the speed. Experiments demonstrate that the proposed learning based vesselness outperforms the conventional Hessian vesselness in both speed and accuracy. On average, it only takes approximately 2.3 seconds to process a large volume with a typical size of 512x512x200 voxels.

  17. A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes.

    PubMed

    Lesage, David; Angelini, Elsa D; Bloch, Isabelle; Funka-Lea, Gareth

    2009-12-01

    Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies. Despite the tremendous amount of past and on-going dedicated research, vascular segmentation remains a challenging task. In this paper, we review state-of-the-art literature on vascular segmentation, with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA). We structure our analysis along three axes: models, features and extraction schemes. We first detail model-based assumptions on the vessel appearance and geometry which can embedded in a segmentation approach. We then review the image features that can be extracted to evaluate these models. Finally, we discuss how existing extraction schemes combine model and feature information to perform the segmentation task. Each component (model, feature and extraction scheme) plays a crucial role toward the efficient, robust and accurate segmentation of vessels of interest. Along each axis of study, we discuss the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones.

  18. Retinal vessel segmentation in colour fundus images using Extreme Learning Machine.

    PubMed

    Zhu, Chengzhang; Zou, Beiji; Zhao, Rongchang; Cui, Jinkai; Duan, Xuanchu; Chen, Zailiang; Liang, Yixiong

    2017-01-01

    Attributes of the retinal vessel play important role in systemic conditions and ophthalmic diagnosis. In this paper, a supervised method based on Extreme Learning Machine (ELM) is proposed to segment retinal vessel. Firstly, a set of 39-D discriminative feature vectors, consisting of local features, morphological features, phase congruency, Hessian and divergence of vector fields, is extracted for each pixel of the fundus image. Then a matrix is constructed for pixel of the training set based on the feature vector and the manual labels, and acts as the input of the ELM classifier. The output of classifier is the binary retinal vascular segmentation. Finally, an optimization processing is implemented to remove the region less than 30 pixels which is isolated from the retinal vascilar. The experimental results testing on the public Digital Retinal Images for Vessel Extraction (DRIVE) database demonstrate that the proposed method is much faster than the other methods in segmenting the retinal vessels. Meanwhile the average accuracy, sensitivity, and specificity are 0.9607, 0.7140 and 0.9868, respectively. Moreover the proposed method exhibits high speed and robustness on a new Retinal Images for Screening (RIS) database. Therefore it has potential applications for real-time computer-aided diagnosis and disease screening.

  19. Computer-aided mesenteric small vessel segmentation on high-resolution 3D contrast-enhanced CT angiography scans

    NASA Astrophysics Data System (ADS)

    Zhang, Weidong; Liu, Jiamin; Yao, Jianhua; Nguyen, Tan; Louie, Adeline; Wank, Stephen; Summers, Ronald M.

    2012-03-01

    Segmentation of the mesenteric vasculature has important applications for evaluation of the small bowel. In particular, it may be useful for small bowel path reconstruction and precise localization of small bowel tumors such as carcinoid. Segmentation of the mesenteric vasculature is very challenging, even for manual labeling, because of the low contrast and tortuosity of the small blood vessels. Many vessel segmentation methods have been proposed. However, most of them are designed for segmenting large vessels. We propose a semi-automated method to extract the mesenteric vasculature on contrast-enhanced abdominal CT scans. First, the internal abdominal region of the body is automatically identified. Second, the major vascular branches are segmented using a multi-linear vessel tracing method. Third, small mesenteric vessels are segmented using multi-view multi-scale vesselness enhancement filters. The method is insensitive to image contrast, variations of vessel shape and small occlusions due to overlapping. The method could automatically detect mesenteric vessels with diameters as small as 1 mm. Compared with the standard-of-reference manually labeled by an expert radiologist, the segmentation accuracy (recall rate) for the whole mesenteric vasculature was 82.3% with a 3.6% false positive rate.

  20. Device for Investigation of Mechanical Tension of Isolated Smooth Muscle Vessels and Airway Segments of Animals

    NASA Astrophysics Data System (ADS)

    Aleinik, A.; Karpovich, N.; Turgunova, N.; Nosarev, A.

    2016-11-01

    For the purpose of testing and the search for new drug compounds, designed to heal many human diseases, it is necessary to investigate the deformation of experimental tissue samples under influence of these drugs. For this task a precision force sensor for measuring the mechanical tension, produced by isolated ring segments of blood vessels and airways was created. The hardware and software systems for the study of changes in contractile responses of the airway smooth muscles and blood vessels of experimental animals was developed.

  1. An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection.

    PubMed

    Saleh, Marwan D; Eswaran, C; Mueen, Ahmed

    2011-08-01

    This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

  2. Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain

    NASA Astrophysics Data System (ADS)

    Phellan, Renzo; Lindner, Thomas; Falcão, Alexandre X.; Forkert, Nils D.

    2017-03-01

    4D arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive and safe modality for cerebrovascular imaging procedures. It uses the patient's magnetically labeled blood as intrinsic contrast agent, so that no external contrast media is required. It provides important 3D structure and blood flow information but a sufficient cerebrovascular segmentation is important since it can help clinicians to analyze and diagnose vascular diseases faster, and with higher confidence as compared to simple visual rating of raw ASL MRA images. This work presents a new method for automatic cerebrovascular segmentation in 4D ASL MRA images of the brain. In this process images are denoised, corresponding image label/control image pairs of the 4D ASL MRA sequences are subtracted, and temporal intensity averaging is used to generate a static representation of the vascular system. After that, sets of vessel and background seeds are extracted and provided as input for the image foresting transform algorithm to segment the vascular system. Four 4D ASL MRA datasets of the brain arteries of healthy subjects and corresponding time-of-flight (TOF) MRA images were available for this preliminary study. For evaluation of the segmentation results of the proposed method, the cerebrovascular system was automatically segmented in the high-resolution TOF MRA images using a validated algorithm and the segmentation results were registered to the 4D ASL datasets. Corresponding segmentation pairs were compared using the Dice similarity coefficient (DSC). On average, a DSC of 0.9025 was achieved, indicating that vessels can be extracted successfully from 4D ASL MRA datasets by the proposed segmentation method.

  3. Automatic liver segmentation method featuring a novel filter for multiphase multidetector-row helical computed tomography.

    PubMed

    Hirose, Tomohiro; Nitta, Norihisa; Tsudagawa, Masaru; Takahashi, Masashi; Murata, Kiyoshi

    2011-01-01

    To introduce an automatic liver segmentation method that includes a novel filter for multiphase multidetector-row helical computed tomography. We acquired 3-phase multidetector-row computed tomographic scans that included unenhanced, arterial, and portal phases. The liver was segmented using our novel adaptive linear prediction filter designed to reduce the difference between filter input and output values in the liver region and to increase these values outside the liver region. The segmentation algorithm produced a mean dice similarity coefficient (DSC) value of 91.4%. The application of our adaptive linear prediction filter was effective in automatically extracting liver regions.

  4. Automated segmentation of 3-D spectral OCT retinal blood vessels by neural canal opening false positive suppression.

    PubMed

    Hu, Zhihong; Niemeijer, Meindert; Abràmoft, Michael D; Lee, Kyungmoo; Garvin, Mona K

    2010-01-01

    We present a method for automatically segmenting the blood vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes, with a focus on the ability to segment the vessels in the region near the neural canal opening (NCO). The algorithm first pre-segments the NCO using a graph-theoretic approach. Oriented Gabor wavelets rotated around the center of the NCO are applied to extract features in a 2-D vessel-aimed projection image. Corresponding oriented NCO-based templates are utilized to help suppress the false positive tendency near the NCO boundary. The vessels are identified in a vessel-aimed projection image using a pixel classification algorithm. Based on the 2-D vessel profiles, 3-D vessel segmentation is performed by a triangular-mesh-based graph search approach in the SD-OCT volume. The segmentation method is trained on 5 and is tested on 10 randomly chosen independent ONH-centered SD-OCT volumes from 15 subjects with glaucoma. Using ROC analysis, for the 2-D vessel segmentation, we demonstrate an improvement over the closest previous work with an area under the curve (AUC) of 0.81 (0.72 for previously reported approach) for the region around the NCO and 0.84 for the region outside the NCO (0.81 for previously reported approach).

  5. Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy.

    PubMed

    Jelinek, Herbert F; Cree, Michael J; Leandro, Jorge J G; Soares, João V B; Cesar, Roberto M; Luckie, A

    2007-05-01

    Proliferative diabetic retinopathy can lead to blindness. However, early recognition allows appropriate, timely intervention. Fluorescein-labeled retinal blood vessels of 27 digital images were automatically segmented using the Gabor wavelet transform and classified using traditional features such as area, perimeter, and an additional five morphological features based on the derivatives-of-Gaussian wavelet-derived data. Discriminant analysis indicated that traditional features do not detect early proliferative retinopathy. The best single feature for discrimination was the wavelet curvature with an area under the curve (AUC) of 0.76. Linear discriminant analysis with a selection of six features achieved an AUC of 0.90 (0.73-0.97, 95% confidence interval). The wavelet method was able to segment retinal blood vessels and classify the images according to the presence or absence of proliferative retinopathy.

  6. Comparison of two algorithms in the automatic segmentation of blood vessels in fundus images

    NASA Astrophysics Data System (ADS)

    LeAnder, Robert; Chowdary, Myneni Sushma; Mokkapati, Swapnasri; Umbaugh, Scott E.

    2008-03-01

    Effective timing and treatment are critical to saving the sight of patients with diabetes. Lack of screening, as well as a shortage of ophthalmologists, help contribute to approximately 8,000 cases per year of people who lose their sight to diabetic retinopathy, the leading cause of new cases of blindness [1] [2]. Timely treatment for diabetic retinopathy prevents severe vision loss in over 50% of eyes tested [1]. Fundus images can provide information for detecting and monitoring eye-related diseases, like diabetic retinopathy, which if detected early, may help prevent vision loss. Damaged blood vessels can indicate the presence of diabetic retinopathy [9]. So, early detection of damaged vessels in retinal images can provide valuable information about the presence of disease, thereby helping to prevent vision loss. Purpose: The purpose of this study was to compare the effectiveness of two blood vessel segmentation algorithms. Methods: Fifteen fundus images from the STARE database were used to develop two algorithms using the CVIPtools software environment. Another set of fifteen images were derived from the first fifteen and contained ophthalmologists' hand-drawn tracings over the retinal vessels. The ophthalmologists' tracings were used as the "gold standard" for perfect segmentation and compared with the segmented images that were output by the two algorithms. Comparisons between the segmented and the hand-drawn images were made using Pratt's Figure of Merit (FOM), Signal-to-Noise Ratio (SNR) and Root Mean Square (RMS) Error. Results: Algorithm 2 has an FOM that is 10% higher than Algorithm 1. Algorithm 2 has a 6%-higher SNR than Algorithm 1. Algorithm 2 has only 1.3% more RMS error than Algorithm 1. Conclusions: Algorithm 1 extracted most of the blood vessels with some missing intersections and bifurcations. Algorithm 2 extracted all the major blood vessels, but eradicated some vessels as well. Algorithm 2 outperformed Algorithm 1 in terms of visual clarity, FOM

  7. Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding

    PubMed Central

    Yousefi, Siavash; Liu, Ting; Wang, Ruikang K.

    2014-01-01

    Optical coherence tomography (OCT) based microangiography is capable of visualizing 3D functional blood vessel networks within microcirculatory tissue beds in vivo. To provide the quantitative information of vasculature from the microangiograms such as vessel diameter and morphology, it is necessary to develop efficient vessel segmentation algorithms. In this paper, we propose to develop a hybrid Hessian/intensity based method to segment and quantify shape and diameter of the blood vessels innervating capillary beds that are imaged by functional OCT in vivo. The proposed method utilizes the multi-scale Hessian filters to segment tubular structures such as blood vessels, but compounded by the intensity-based segmentation method to mitigate the limitations of Hessian filter's sensitivity to the selection of scale parameters. Such compounding segmentation scheme takes the advantage of morphological nature of Hessian filters while correcting for the scale parameter selection by intensity-based segmentation. The proposed algorithm is tested on a wound healing model and its performance of segmentation vessels is quantified by a publicly available manual segmentation dataset. We believe that this method will play an important role in the quantification of micro-angiograms for microcirculation research in ophthalmology and diagnosing retinal eye diseases involved with microcirculation. PMID:25283347

  8. Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding.

    PubMed

    Yousefi, Siavash; Liu, Ting; Wang, Ruikang K

    2015-01-01

    Optical coherence tomography (OCT) based microangiography is capable of visualizing 3D functional blood vessel networks within microcirculatory tissue beds in vivo. To provide the quantitative information of vasculature from the microangiograms such as vessel diameter and morphology, it is necessary to develop efficient vessel segmentation algorithms. In this paper, we propose to develop a hybrid Hessian/intensity based method to segment and quantify shape and diameter of the blood vessels innervating capillary beds that are imaged by functional OCT in vivo. The proposed method utilizes multi-scale Hessian filters to segment tubular structures such as blood vessels, but compounded by the intensity-based segmentation method to mitigate the limitations of Hessian filters' sensitivity to the selection of scale parameters. Such compounding segmentation scheme takes advantage of the morphological nature of Hessian filters while correcting for the scale parameter selection by intensity-based segmentation. The proposed algorithm is tested on a wound healing model and its performance of segmentation vessels is quantified by a publicly available manual segmentation dataset. We believe that this method will play an important role in the quantification of micro-angiograms for microcirculation research in ophthalmology and diagnosing retinal eye diseases involved with microcirculation.

  9. Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy.

    PubMed

    Salem, Sameh A; Salem, Nancy M; Nandi, Asoke K

    2007-03-01

    In this paper, segmentation of blood vessels from colour retinal images using a novel clustering algorithm with a partial supervision strategy is proposed. The proposed clustering algorithm, which is a RAdius based Clustering ALgorithm (RACAL), uses a distance based principle to map the distributions of the data by utilising the premise that clusters are determined by a distance parameter, without having to specify the number of clusters. Additionally, the proposed clustering algorithm is enhanced with a partial supervision strategy and it is demonstrated that it is able to segment blood vessels of small diameters and low contrasts. Results are compared with those from the KNN classifier and show that the proposed RACAL performs better than the KNN in case of abnormal images as it succeeds in segmenting small and low contrast blood vessels, while it achieves comparable results for normal images. For automation process, RACAL can be used as a classifier and results show that it performs better than the KNN classifier in both normal and abnormal images.

  10. Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography.

    PubMed

    Lahiri, A; Roy, Abhijit Guha; Sheet, Debdoot; Biswas, Prabir Kumar

    2016-08-01

    Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member autoencoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708. Comparison with other major algorithms substantiates the high efficacy of our model.

  11. Automatic segmentation of coronary arteries in CT imaging in the presence of kissing vessel artifacts.

    PubMed

    Wang, Yin; Liatsis, Panos

    2012-07-01

    In this paper, we present a novel two-step algorithm for segmentation of coronary arteries in computed tomography images based on the framework of active contours. In the proposed method, both global and local intensity information is utilized in the energy calculation. The global term is defined as a normalized cumulative distribution function, which contributes to the overall active contour energy in an adaptive fashion based on image histograms, to deform the active contour away from local stationary points. Possible outliers, such as kissing vessel artifacts, are removed in the postprocessing stage by a slice-by-slice correction scheme based on multiregion competition, where both arteries and kissing vessels are identified and tracked through the slices. The efficiency and the accuracy of the proposed technique are demonstrated on both synthetic and real datasets. The results on clinical datasets show that the method is able to extract the major branches of arteries with an average distance of 0.73 voxels to the manually delineated ground truth data. In the presence of kissing vessel artifacts, the outer surface of the entire coronary tree, extracted by the proposed algorithm, is smooth and contains fewer erroneous regions, originating in kissing vessel artifacts, as compared to the initial segmentation.

  12. Detection and characterization of flaws in segments of light water reactor pressure vessels

    SciTech Connect

    Cook, K.V.; Cunningham, R.A. Jr.; McClung, R.W.

    1987-01-01

    Studies have been conducted to determine flaw density in segments cut from light water reactor (LWR) pressure vessels as part of the Oak Ridge National Laboratory's Heavy-Section Steel Technology (HSST) Program. Segments from the Hope Creek Unit 2 vessil and the Pilgrim Unit 2 Vessel were purchased from salvage dealers. Hope Creek was a boiling water reactor (BWR) design and Pilgrim was a pressurized water reactor (PWR) design. Neither were ever placed in service. Objectives were to evaluate these LWR segments for flaws with ultrasonic and liquid penetrant techniques. Both objectives were successfully completed. One significant indication was detected in a Hope Creek seam weld by ultrasonic techniques and characterized by further analyses terminating with destructive correlation. This indication (with a through-wall dimension of approx.6 mm (approx.0.24 in.)) was detected in only 3 m (10 ft) of weldment and offers extremely limited data when compared to the extent of welding even in a single pressure vessel. However, the detection and confirmation of the flaw in the arbitrarily selected sections implies the Marshall report estimates (and others) are nonconservative for such small flaws. No significant indications were detected in the Pilgrim material by ultrasonic techniques. Unfortunately, the Pilgrim segments contained relatively little weldment; thus, we limited our ultrasonic examinations to the cladding and subcladding regions. Fluorescent liquid penetrant inspection of the cladding surfaces for both LWR segments detected no significant indications (i.e., for a total of approximately 6.8 m/sup 2/ (72 ft/sup 2/) of cladding surface).

  13. Automatic liver segmentation on Computed Tomography using random walkers for treatment planning

    PubMed Central

    Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M. Iqbal Bin

    2016-01-01

    Segmentation of the liver from Computed Tomography (CT) volumes plays an important role during the choice of treatment strategies for liver diseases. Despite lots of attention, liver segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled with high variability of both intensity patterns and anatomical appearances with all these difficulties becoming more prominent in pathological livers. To achieve a more accurate segmentation, a random walker based framework is proposed that can segment contrast-enhanced livers CT images with great accuracy and speed. Based on the location of the right lung lobe, the liver dome is automatically detected thus eliminating the need for manual initialization. The computational requirements are further minimized utilizing rib-caged area segmentation, the liver is then extracted by utilizing random walker method. The proposed method was able to achieve one of the highest accuracies reported in the literature against a mixed healthy and pathological liver dataset compared to other segmentation methods with an overlap error of 4.47 % and dice similarity coefficient of 0.94 while it showed exceptional accuracy on segmenting the pathological livers with an overlap error of 5.95 % and dice similarity coefficient of 0.91. PMID:28096782

  14. Image-based vessel-by-vessel analysis for red blood cell and plasma dynamics with automatic segmentation.

    PubMed

    Kawaguchi, Hiroshi; Masamoto, Kazuto; Ito, Hiroshi; Kanno, Iwao

    2012-09-01

    The aim of the present study was to test the hypothesis that vascular tones of cortical surface and parenchymal blood flow can be dissociated depending on the perturbation. To this end, a novel image-based analytical method for quantitatively measuring vessel diameters and flow dynamics was developed. The algorithm relies on the spatiotemporal coherence of the pixel intensity changes induced by the transit of the fluorescent signals measured using confocal laser scanning fluorescent microscopy in the rat cerebral cortex. A cocktail of fluorescently labeled red blood cell (RBC) and plasma agents was administered to simultaneously compare RBC and plasma dynamics in the same vascular networks. The time to fluorescent signal appearance and the width of the fluorescent signal were measured in each segment and compared between sodium nitroprusside-induced global and sensory stimulation-induced local perturbation conditions. We observed that infusion of sodium nitroprusside induced significant vasodilation in the surface artery, particularly in the small arteries (1.8-fold increase). Vasodilation induced by sensory stimulation was observed to depend on vessel size, but significant changes were only detected for the small arteries and veins. Measurements of the time to venous appearance revealed that appearance time was extended by sodium nitroprusside, but shortened during forepaw stimulation, relative to the control condition. Both perturbations provoked the largest changes between the small artery and vein segments, indicating that the changes in the appearance time originate from blood passage through parenchymal microcirculation. These findings support the hypothesis that cortical surface vascular tone and parenchymal blood flow are individually coordinated. Copyright © 2012 Elsevier Inc. All rights reserved.

  15. Phantom-based ground-truth generation for cerebral vessel segmentation and pulsatile deformation analysis

    NASA Astrophysics Data System (ADS)

    Schetelig, Daniel; Säring, Dennis; Illies, Till; Sedlacik, Jan; Kording, Fabian; Werner, René

    2016-03-01

    Hemodynamic and mechanical factors of the vascular system are assumed to play a major role in understanding, e.g., initiation, growth and rupture of cerebral aneurysms. Among those factors, cardiac cycle-related pulsatile motion and deformation of cerebral vessels currently attract much interest. However, imaging of those effects requires high spatial and temporal resolution and remains challenging { and similarly does the analysis of the acquired images: Flow velocity changes and contrast media inflow cause vessel intensity variations in related temporally resolved computed tomography and magnetic resonance angiography data over the cardiac cycle and impede application of intensity threshold-based segmentation and subsequent motion analysis. In this work, a flow phantom for generation of ground-truth images for evaluation of appropriate segmentation and motion analysis algorithms is developed. The acquired ground-truth data is used to illustrate the interplay between intensity fluctuations and (erroneous) motion quantification by standard threshold-based segmentation, and an adaptive threshold-based segmentation approach is proposed that alleviates respective issues. The results of the phantom study are further demonstrated to be transferable to patient data.

  16. An enhanced segmentation of blood vessels in retinal images using contourlet.

    PubMed

    Rezatofighi, S H; Roodaki, A; Ahmadi Noubari, H

    2008-01-01

    Retinal images acquired using a fundus camera often contain low grey, low level contrast and are of low dynamic range. This may seriously affect the automatic segmentation stage and subsequent results; hence, it is necessary to carry-out preprocessing to improve image contrast results before segmentation. Here we present a new multi-scale method for retinal image contrast enhancement using Contourlet transform. In this paper, a combination of feature extraction approach which utilizes Local Binary Pattern (LBP), morphological method and spatial image processing is proposed for segmenting the retinal blood vessels in optic fundus images. Furthermore, performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) is investigated in the classification section. The performance of the proposed algorithm is tested on the publicly available DRIVE database. The results are numerically assessed for different proposed algorithms.

  17. Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis.

    PubMed

    Kumar, Rahul Prasanna; Albregtsen, Fritz; Reimers, Martin; Edwin, Bjørn; Langø, Thomas; Elle, Ole Jakob

    2015-05-01

    The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. In this paper we present a novel, semi-automatic method for blood vessel segmentation and centerline extraction, by tracking the blood vessel tree from a user-initiated seed point to the ends of the blood vessel tree. The novelty of our method is in performing only two-dimensional cross-section analysis for segmentation of the connected blood vessels. The cross-section analysis is done by our novel single-scale or multi-scale circle enhancement filter, used at the blood vessel trunk or bifurcation, respectively. The method was validated for both synthetic and medical images. Our validation has shown that the cross-sectional centerline error for our method is below 0.8 pixels and the Dice coefficient for our segmentation is 80% ± 2.7%. On combining our method with an optional active contour post-processing, the Dice coefficient for the resulting segmentation is found to be 94% ± 2.4%. Furthermore, by restricting the image analysis to the regions of interest and converting most of the three-dimensional calculations to two-dimensional calculations, the processing was found to be more than 18 times faster than Frangi vesselness with thinning, 8 times faster than user-initiated active contour segmentation with thinning and 7 times faster than our previous method.

  18. Fast vessel segmentation in retinal images using multi-scale enhancement and second-order local entropy

    NASA Astrophysics Data System (ADS)

    Yu, H.; Barriga, S.; Agurto, C.; Zamora, G.; Bauman, W.; Soliz, P.

    2012-03-01

    Retinal vasculature is one of the most important anatomical structures in digital retinal photographs. Accurate segmentation of retinal blood vessels is an essential task in automated analysis of retinopathy. This paper presents a new and effective vessel segmentation algorithm that features computational simplicity and fast implementation. This method uses morphological pre-processing to decrease the disturbance of bright structures and lesions before vessel extraction. Next, a vessel probability map is generated by computing the eigenvalues of the second derivatives of Gaussian filtered image at multiple scales. Then, the second order local entropy thresholding is applied to segment the vessel map. Lastly, a rule-based decision step, which measures the geometric shape difference between vessels and lesions is applied to reduce false positives. The algorithm is evaluated on the low-resolution DRIVE and STARE databases and the publicly available high-resolution image database from Friedrich-Alexander University Erlangen-Nuremberg, Germany). The proposed method achieved comparable performance to state of the art unsupervised vessel segmentation methods with a competitive faster speed on the DRIVE and STARE databases. For the high resolution fundus image database, the proposed algorithm outperforms an existing approach both on performance and speed. The efficiency and robustness make the blood vessel segmentation method described here suitable for broad application in automated analysis of retinal images.

  19. Retinal hyperaemia-related blood vessel artifacts are relevant to automated OCT layer segmentation.

    PubMed

    Balk, L J; Mayer, M; Uitdehaag, B M J; Petzold, A

    2014-03-01

    A frequently observed local measurement artifact with spectral domain OCT is caused by the void signal of the retinal vasculature. This study investigated the effect of suppression of blood vessel artifacts with and without retinal hyperaemia. Spectral domain OCT scans, centred on the optic nerve head, were performed in 46 healthy subjects (92 eyes). Baseline scans were made during rest, while for the follow-up scan, 23 subjects (50 %) performed strenuous physical exercise. Systemic and retinal hyperaemia were quantified. Quantification of retinal nerve fibre layer (RNFL) thickness was performed with and without suppression of retinal blood vessel artifacts. The potential systematic effect on RNFL thickness measurements was analysed using Bland-Altman plots. At baseline (no retinal hyperaemia), there was a systematic difference in RNFL thickness (3.4 μm, limits of agreement -0.9 to 7.7) with higher values if blood vessel artifacts were not suppressed. There was significant retinal hyperaemia in the exercise group (p < 0.0001). Baseline thickness increased from 93.18 to 93.83 μm (p < 0.05) in the exercise group using the algorithm with blood vessel artifact suppression, but no significant changes were observed using the algorithm without blood vessel artifact suppression. Retinal hyperaemia leads to blood vessel artifacts which are relevant to the precision of OCT layer segmentation algorithms. The two algorithms investigated in this study can not be used interchangeably. The algorithm with blood vessel artifact suppression was more sensitive in detecting small changes in RNFL thickness. This may be relevant for the use of OCT in a range of neurodegenerative diseases were only a small degree of retinal layer atrophy have been found so far.

  20. Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores.

    PubMed

    Zhang, Jiong; Dashtbozorg, Behdad; Bekkers, Erik; Pluim, Josien P W; Duits, Remco; Ter Haar Romeny, Bart M

    2016-12-01

    This paper presents a robust and fully automatic filter-based approach for retinal vessel segmentation. We propose new filters based on 3D rotating frames in so-called orientation scores, which are functions on the Lie-group domain of positions and orientations [Formula: see text]. By means of a wavelet-type transform, a 2D image is lifted to a 3D orientation score, where elongated structures are disentangled into their corresponding orientation planes. In the lifted domain [Formula: see text], vessels are enhanced by means of multi-scale second-order Gaussian derivatives perpendicular to the line structures. More precisely, we use a left-invariant rotating derivative (LID) frame, and a locally adaptive derivative (LAD) frame. The LAD is adaptive to the local line structures and is found by eigensystem analysis of the left-invariant Hessian matrix (computed with the LID). After multi-scale filtering via the LID or LAD in the orientation score domain, the results are projected back to the 2D image plane giving us the enhanced vessels. Then a binary segmentation is obtained through thresholding. The proposed methods are validated on six retinal image datasets with different image types, on which competitive segmentation performances are achieved. In particular, the proposed algorithm of applying the LAD filter on orientation scores (LAD-OS) outperforms most of the state-of-the-art methods. The LAD-OS is capable of dealing with typically difficult cases like crossings, central arterial reflex, closely parallel and tiny vessels. The high computational speed of the proposed methods allows processing of large datasets in a screening setting.

  1. Use of Gabor filters and deep networks in the segmentation of retinal vessel morphology

    NASA Astrophysics Data System (ADS)

    Leopold, Henry A.; Orchard, Jeff; Zelek, John; Lakshminarayanan, Vasudevan

    2017-02-01

    The segmentation of retinal morphology has numerous applications in assessing ophthalmologic and cardiovascular disease pathologies. The early detection of many such conditions is often the most effective method for reducing patient risk. Computer aided segmentation of the vasculature has proven to be a challenge, mainly due to inconsistencies such as noise, variations in hue and brightness that can greatly reduce the quality of fundus images. Accurate fundus and/or retinal vessel maps give rise to longitudinal studies able to utilize multimodal image registration and disease/condition status measurements, as well as applications in surgery preparation and biometrics. This paper further investigates the use of a Convolutional Neural Network as a multi-channel classifier of retinal vessels using the Digital Retinal Images for Vessel Extraction database, a standardized set of fundus images used to gauge the effectiveness of classification algorithms. The CNN has a feed-forward architecture and varies from other published architectures in its combination of: max-pooling, zero-padding, ReLU layers, batch normalization, two dense layers and finally a Softmax activation function. Notably, the use of Adam to optimize training the CNN on retinal fundus images has not been found in prior review. This work builds on prior work of the authors, exploring the use of Gabor filters to boost the accuracy of the system to 0.9478 during post processing. The mean of a series of Gabor filters with varying frequencies and sigma values are applied to the output of the network and used to determine whether a pixel represents a vessel or non-vessel.

  2. Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

    NASA Astrophysics Data System (ADS)

    Scorza, Davide; Moccia, Sara; De Luca, Giuseppe; Plaino, Lisa; Cardinale, Francesco; Mattos, Leonardo S.; Kabongo, Luis; De Momi, Elena

    2017-03-01

    Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is more likely to occur. In this paper, we propose a novel framework to help clinicians in defining a safe trajectory and focus our attention on EP. For each electrode, a Maximum Intensity Projection (MIP) image was obtained from Computer Tomography Angiography (CTA) slices of the brain first centimeter measured along the electrode trajectory. A Gaussian Mixture Model (GMM), modified to include neighborhood prior through Markov Random Fields (GMM-MRF), is used to robustly segment vessels and deal with the noisy nature of MIP images. Results are compared with simple GMM and manual global Thresholding (Th) by computing sensitivity, specificity, accuracy and Dice similarity index against manual segmentation performed under the supervision of an expert surgeon. In this work we present a novel framework which can be easily integrated into manual and automatic planner to help surgeon during the planning phase. GMM-MRF qualitatively showed better performance over GMM in reproducing the connected nature of brain vessels also in presence of noise and image intensity drops typical of MIP images. With respect Th, it is a completely automatic method and it is not influenced by inter-subject variability.

  3. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification.

    PubMed

    Soares, João V B; Leandro, Jorge J G; Cesar Júnior, Roberto M; Jelinek, Herbert F; Cree, Michael J

    2006-09-01

    We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et al., 2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.

  4. Efficient liver segmentation in CT images based on graph cuts and bottleneck detection.

    PubMed

    Liao, Miao; Zhao, Yu-Qian; Wang, Wei; Zeng, Ye-Zhan; Yang, Qing; Shih, Frank Y; Zou, Bei-Ji

    2016-11-01

    Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes. An intensity model is combined with a principal component analysis (PCA) based appearance model to exclude complex background and highlight liver region. They are then integrated with location information from neighboring slices into graph cuts to segment the liver in each slice automatically. Finally, a boundary refinement method based on bottleneck detection is used to increase the segmentation accuracy. Our method does not require heavy training process or statistical model construction, and is capable of dealing with complicated shape and intensity variations. We apply the proposed method on XHCSU14 and SLIVER07 databases, and evaluate it by MICCAI criteria and Dice similarity coefficient. Experimental results show our method outperforms several existing methods on liver segmentation.

  5. UltraFast Doppler ultrasonography for hepatic vessels of liver recipients: preliminary experiences

    PubMed Central

    2015-01-01

    Purpose: The purpose of this study was to investigate the value of UltraFast Doppler ultrasonography (US) for evaluating hepatic vessels in liver recipients. Methods: Thirty-nine liver Doppler US sessions were conducted in 20 liver recipients. Each session consisted of UltraFast and conventional liver Doppler US in a random order. We compared the velocities and phasicities of the hepatic vessels, duration of each Doppler study, occurrence of technical failures, and differences in clinical decisions. Results: The velocities and resistive index values of hepatic vessels showed a strong positive correlation between the two Doppler studies (mean R=0.806; range, 0.710 to 0.924). The phasicities of the hepatic vessels were the same in both Doppler US exams. With respect to the duration of the Doppler US exam, there was no significant difference between the UltraFast (251±99 seconds) and conventional (231±117 seconds) Doppler studies (P=0.306). In five poor breath-holders, in whom the duration of conventional Doppler US was longer, UltraFast Doppler US (272±157 seconds) required a shorter time than conventional Doppler US (381±133 seconds; P=0.005). There was no difference between the two techniques with respect to technical failures and clinical decisions. Conclusion: UltraFast Doppler US is clinically equivalent to conventional Doppler US with advantages for poor breath-holders during the post-liver transplantation work-up. PMID:25409662

  6. An ensemble classification-based approach applied to retinal blood vessel segmentation.

    PubMed

    Fraz, Muhammad Moazam; Remagnino, Paolo; Hoppe, Andreas; Uyyanonvara, Bunyarit; Rudnicka, Alicja R; Owen, Christopher G; Barman, Sarah A

    2012-09-01

    This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.

  7. Automatic 2D and 3D segmentation of liver from Computerised Tomography

    NASA Astrophysics Data System (ADS)

    Evans, Alun

    As part of the diagnosis of liver disease, a Computerised Tomography (CT) scan is taken of the patient, which the clinician then uses for assistance in determining the presence and extent of the disease. This thesis presents the background, methodology, results and future work of a project that employs automated methods to segment liver tissue. The clinical motivation behind this work is the desire to facilitate the diagnosis of liver disease such as cirrhosis or cancer, assist in volume determination for liver transplantation, and possibly assist in measuring the effect of any treatment given to the liver. Previous attempts at automatic segmentation of liver tissue have relied on 2D, low-level segmentation techniques, such as thresholding and mathematical morphology, to obtain the basic liver structure. The derived boundary can then be smoothed or refined using more advanced methods. The 2D results presented in this thesis improve greatly on this previous work by using a topology adaptive active contour model to accurately segment liver tissue from CT images. The use of conventional snakes for liver segmentation is difficult due to the presence of other organs closely surrounding the liver this new technique avoids this problem by adding an inflationary force to the basic snake equation, and initialising the snake inside the liver. The concepts underlying the 2D technique are extended to 3D, and results of full 3D segmentation of the liver are presented. The 3D technique makes use of an inflationary active surface model which is adaptively reparameterised, according to its size and local curvature, in order that it may more accurately segment the organ. Statistical analysis of the accuracy of the segmentation is presented for 18 healthy liver datasets, and results of the segmentation of unhealthy livers are also shown. The novel work developed during the course of this project has possibilities for use in other areas of medical imaging research, for example the

  8. A quantum mechanics-based algorithm for vessel segmentation in retinal images

    NASA Astrophysics Data System (ADS)

    Youssry, Akram; El-Rafei, Ahmed; Elramly, Salwa

    2016-06-01

    Blood vessel segmentation is an important step in retinal image analysis. It is one of the steps required for computer-aided detection of ophthalmic diseases. In this paper, a novel quantum mechanics-based algorithm for retinal vessel segmentation is presented. The algorithm consists of three major steps. The first step is the preprocessing of the images to prepare the images for further processing. The second step is feature extraction where a set of four features is generated at each image pixel. These features are then combined using a nonlinear transformation for dimensionality reduction. The final step is applying a recently proposed quantum mechanics-based framework for image processing. In this step, pixels are mapped to quantum systems that are allowed to evolve from an initial state to a final state governed by Schrödinger's equation. The evolution is controlled by the Hamiltonian operator which is a function of the extracted features at each pixel. A measurement step is consequently performed to determine whether the pixel belongs to vessel or non-vessel classes. Many functional forms of the Hamiltonian are proposed, and the best performing form was selected. The algorithm is tested on the publicly available DRIVE database. The average results for sensitivity, specificity, and accuracy are 80.29, 97.34, and 95.83 %, respectively. These results are compared to some recently published techniques showing the superior performance of the proposed method. Finally, the implementation of the algorithm on a quantum computer and the challenges facing this implementation are introduced.

  9. Computational study of pulsatile blood flow in prototype vessel geometries of coronary segments

    PubMed Central

    Chaniotis, A.K.; Kaiktsis, L.; Katritsis, D.; Efstathopoulos, E.; Pantos, I.; Marmarellis, V.

    2010-01-01

    The spatial and temporal distributions of wall shear stress (WSS) in prototype vessel geometries of coronary segments are investigated via numerical simulation, and the potential association with vascular disease and specifically atherosclerosis and plaque rupture is discussed. In particular, simulation results of WSS spatio-temporal distributions are presented for pulsatile, non-Newtonian blood flow conditions for: (a) curved pipes with different curvatures, and (b) bifurcating pipes with different branching angles and flow division. The effects of non-Newtonian flow on WSS (compared to Newtonian flow) are found to be small at Reynolds numbers representative of blood flow in coronary arteries. Specific preferential sites of average low WSS (and likely atherogenesis) were found at the outer regions of the bifurcating branches just after the bifurcation, and at the outer-entry and inner-exit flow regions of the curved vessel segment. The drop in WSS was more dramatic at the bifurcating vessel sites (less than 5% of the pre-bifurcation value). These sites were also near rapid gradients of WSS changes in space and time – a fact that increases the risk of rupture of plaque likely to develop at these sites. The time variation of the WSS spatial distributions was very rapid around the start and end of the systolic phase of the cardiac cycle, when strong fluctuations of intravascular pressure were also observed. These rapid and strong changes of WSS and pressure coincide temporally with the greatest flexion and mechanical stresses induced in the vessel wall by myocardial motion (ventricular contraction). The combination of these factors may increase the risk of plaque rupture and thrombus formation at these sites. PMID:20400349

  10. Computational study of pulsatile blood flow in prototype vessel geometries of coronary segments.

    PubMed

    Chaniotis, A K; Kaiktsis, L; Katritsis, D; Efstathopoulos, E; Pantos, I; Marmarellis, V

    2010-01-01

    The spatial and temporal distributions of wall shear stress (WSS) in prototype vessel geometries of coronary segments are investigated via numerical simulation, and the potential association with vascular disease and specifically atherosclerosis and plaque rupture is discussed. In particular, simulation results of WSS spatio-temporal distributions are presented for pulsatile, non-Newtonian blood flow conditions for: (a) curved pipes with different curvatures, and (b) bifurcating pipes with different branching angles and flow division. The effects of non-Newtonian flow on WSS (compared to Newtonian flow) are found to be small at Reynolds numbers representative of blood flow in coronary arteries. Specific preferential sites of average low WSS (and likely atherogenesis) were found at the outer regions of the bifurcating branches just after the bifurcation, and at the outer-entry and inner-exit flow regions of the curved vessel segment. The drop in WSS was more dramatic at the bifurcating vessel sites (less than 5% of the pre-bifurcation value). These sites were also near rapid gradients of WSS changes in space and time - a fact that increases the risk of rupture of plaque likely to develop at these sites. The time variation of the WSS spatial distributions was very rapid around the start and end of the systolic phase of the cardiac cycle, when strong fluctuations of intravascular pressure were also observed. These rapid and strong changes of WSS and pressure coincide temporally with the greatest flexion and mechanical stresses induced in the vessel wall by myocardial motion (ventricular contraction). The combination of these factors may increase the risk of plaque rupture and thrombus formation at these sites.

  11. Automatic Vessel Shade-Robust Segmentation of Retinal Layers in OCT Images.

    PubMed

    González-López, Ana; Ortega, Marcos; Penedo, Manuel G; Charlón, Pablo

    2014-01-01

    Optical Coherence Tomography (OCT) is a promising imaging technique used by ophthalmologists to diagnose diseases. Since retinal morphology can be identified on these images, several image processing-based methods are emerging with the purpose of extracting their information. The first step to tackle any automatic method to extract pathological features from these images is delimiting retinal layers automatically. This is the aim of this paper, which presents an active contour-based method to segment layer boundaries in the retina. Results obtained by this method present high accuracy and robustness, even when some of these layers are low defined or vessel shades are present.

  12. Segmentation of arterial vessel wall motion to sub-pixel resolution using M-mode ultrasound.

    PubMed

    Fancourt, Craig; Azer, Karim; Ramcharan, Sharmilee L; Bunzel, Michelle; Cambell, Barry R; Sachs, Jeffrey R; Walker, Matthew

    2008-01-01

    We describe a method for segmenting arterial vessel wall motion to sub-pixel resolution, using the returns from M-mode ultrasound. The technique involves measuring the spatial offset between all pairs of scans from their cross-correlation, converting the spatial offsets to relative wall motion through a global optimization, and finally translating from relative to absolute wall motion by interpolation over the M-mode image. The resulting detailed wall distension waveform has the potential to enhance existing vascular biomarkers, such as strain and compliance, as well as enable new ones.

  13. Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials.

    PubMed

    Yan, Zhennan; Zhang, Shaoting; Tan, Chaowei; Qin, Hongxing; Belaroussi, Boubakeur; Yu, Hui Jing; Miller, Colin; Metaxas, Dimitris N

    2015-04-01

    Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic fat-fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to obtain fine segmentation. Fat-fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with two other atlas-based methods. Experimental results demonstrate the promises of our assessment framework.

  14. A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data

    NASA Astrophysics Data System (ADS)

    Goetz, Michael; Heim, Eric; Maerz, Keno; Norajitra, Tobias; Hafezi, Mohammadreza; Fard, Nassim; Mehrabi, Arianeb; Knoll, Max; Weber, Christian; Maier-Hein, Lena; Maier-Hein, Klaus H.

    2016-03-01

    Current fully automatic liver tumor segmentation systems are designed to work on a single CT-image. This hinders these systems from the detection of more complex types of liver tumor. We therefore present a new algorithm for liver tumor segmentation that allows incorporating different CT scans and requires no manual interaction. We derive a liver segmentation with state-of-the-art shape models which are robust to initialization. The tumor segmentation is then achieved by classifying all voxels into healthy or tumorous tissue using Extremely Randomized Trees with an auto-context learning scheme. Using DALSA enables us to learn from only sparse annotations and allows a fast set-up for new image settings. We validate the quality of our algorithm with exemplary segmentation results.

  15. 3-D segmentation of retinal blood vessels in spectral-domain OCT volumes of the optic nerve head

    NASA Astrophysics Data System (ADS)

    Lee, Kyungmoo; Abràmoff, Michael D.; Niemeijer, Meindert; Garvin, Mona K.; Sonka, Milan

    2010-03-01

    Segmentation of retinal blood vessels can provide important information for detecting and tracking retinal vascular diseases including diabetic retinopathy, arterial hypertension, arteriosclerosis and retinopathy of prematurity (ROP). Many studies on 2-D segmentation of retinal blood vessels from a variety of medical images have been performed. However, 3-D segmentation of retinal blood vessels from spectral-domain optical coherence tomography (OCT) volumes, which is capable of providing geometrically accurate vessel models, to the best of our knowledge, has not been previously studied. The purpose of this study is to develop and evaluate a method that can automatically detect 3-D retinal blood vessels from spectral-domain OCT scans centered on the optic nerve head (ONH). The proposed method utilized a fast multiscale 3-D graph search to segment retinal surfaces as well as a triangular mesh-based 3-D graph search to detect retinal blood vessels. An experiment on 30 ONH-centered OCT scans (15 right eye scans and 15 left eye scans) from 15 subjects was performed, and the mean unsigned error in 3-D of the computer segmentations compared with the independent standard obtained from a retinal specialist was 3.4 +/- 2.5 voxels (0.10 +/- 0.07 mm).

  16. CT liver volumetry using geodesic active contour segmentation with a level-set algorithm

    NASA Astrophysics Data System (ADS)

    Suzuki, Kenji; Epstein, Mark L.; Kohlbrenner, Ryan; Obajuluwa, Ademola; Xu, Jianwu; Hori, Masatoshi; Baron, Richard

    2010-03-01

    Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard" manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference (p(F<=f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes.

  17. Deep residual networks for automatic segmentation of laparoscopic videos of the liver

    NASA Astrophysics Data System (ADS)

    Gibson, Eli; Robu, Maria R.; Thompson, Stephen; Edwards, P. Eddie; Schneider, Crispin; Gurusamy, Kurinchi; Davidson, Brian; Hawkes, David J.; Barratt, Dean C.; Clarkson, Matthew J.

    2017-03-01

    Motivation: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos. Method: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver resections and 7 laparoscopic staging procedures, and evaluated using the Dice score. Results: The CNN yielded segmentations with Dice scores >=0.95 for the majority of images; however, the inter-patient variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations: minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological liver tissue that mimics non-liver tissue appearance. Conclusion: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video, but additional data or computational advances are necessary to address challenges due to the high inter-patient variability in liver appearance.

  18. Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter.

    PubMed

    Singh, Nagendra Pratap; Srivastava, Rajeev

    2016-06-01

    Retinal blood vessel segmentation is a prominent task for the diagnosis of various retinal pathology such as hypertension, diabetes, glaucoma, etc. In this paper, a novel matched filter approach with the Gumbel probability distribution function as its kernel is introduced to improve the performance of retinal blood vessel segmentation. Before applying the proposed matched filter, the input retinal images are pre-processed. During pre-processing stage principal component analysis (PCA) based gray scale conversion followed by contrast limited adaptive histogram equalization (CLAHE) are applied for better enhancement of retinal image. After that an exhaustive experiments have been conducted for selecting the appropriate value of parameters to design a new matched filter. The post-processing steps after applying the proposed matched filter include the entropy based optimal thresholding and length filtering to obtain the segmented image. For evaluating the performance of proposed approach, the quantitative performance measures, an average accuracy, average true positive rate (ATPR), and average false positive rate (AFPR) are calculated. The respective values of the quantitative performance measures are 0.9522, 0.7594, 0.0292 for DRIVE data set and 0.9270, 0.7939, 0.0624 for STARE data set. To justify the effectiveness of proposed approach, receiver operating characteristic (ROC) curve is plotted and the average area under the curve (AUC) is calculated. The average AUC for DRIVE and STARE data sets are 0.9287 and 0.9140 respectively. The obtained experimental results confirm that the proposed approach performance better with respect to other prominent Gaussian distribution function and Cauchy PDF based matched filter approaches. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  19. A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images.

    PubMed

    Christodoulidis, Argyrios; Hurtut, Thomas; Tahar, Houssem Ben; Cheriet, Farida

    2016-09-01

    Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05).

  20. Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring

    PubMed Central

    Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M. Iqbal Bin

    2016-01-01

    Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset. PMID:27540353

  1. Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring.

    PubMed

    Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M Iqbal Bin

    2016-01-01

    Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset.

  2. Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm.

    PubMed

    Zygomalas, Apollon; Karavias, Dionissios; Koutsouris, Dimitrios; Maroulis, Ioannis; Karavias, Dimitrios D; Giokas, Konstantinos; Megalooikonomou, Vasileios

    2016-05-01

    We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are based on thresholding by pixel intensity value from initial seed points. A random sample of 12 patients undergoing elective high-risk hepatectomies at our institution was prospectively selected to undergo computer-assisted surgery using our algorithms (June 2013-July 2014). Quantitative and qualitative evaluation was performed. The average computer analysis time (segmentation, resection planning, volumetry, visualization) was 45 min/dataset. The runtime for the semiautomatic algorithm was <0.2 s/slice. Liver volumetric segmentation using the hybrid method was achieved in 12.9 s/dataset (SD ± 6.14). Mean similarity index was 96.2 % (SD ± 1.6). The future liver remnant volume calculated by the application showed a correlation of 0.99 to that calculated using manual boundary tracing. The 3D liver models and the virtual liver resections had an acceptable coincidence with the real intraoperative findings. The patient-specific 3D models produced using our semiautomatic and hybrid semiautomatic segmentation algorithms proved to be accurate for the preoperative planning in liver tumor surgery and effectively enhanced the intraoperative medical image guidance.

  3. Quantitative evaluation of six graph based semi-automatic liver tumor segmentation techniques using multiple sets of reference segmentation

    NASA Astrophysics Data System (ADS)

    Su, Zihua; Deng, Xiang; Chefd'hotel, Christophe; Grady, Leo; Fei, Jun; Zheng, Dong; Chen, Ning; Xu, Xiaodong

    2011-03-01

    Graph based semi-automatic tumor segmentation techniques have demonstrated great potential in efficiently measuring tumor size from CT images. Comprehensive and quantitative validation is essential to ensure the efficacy of graph based tumor segmentation techniques in clinical applications. In this paper, we present a quantitative validation study of six graph based 3D semi-automatic tumor segmentation techniques using multiple sets of expert segmentation. The six segmentation techniques are Random Walk (RW), Watershed based Random Walk (WRW), LazySnapping (LS), GraphCut (GHC), GrabCut (GBC), and GrowCut (GWC) algorithms. The validation was conducted using clinical CT data of 29 liver tumors and four sets of expert segmentation. The performance of the six algorithms was evaluated using accuracy and reproducibility. The accuracy was quantified using Normalized Probabilistic Rand Index (NPRI), which takes into account of the variation of multiple expert segmentations. The reproducibility was evaluated by the change of the NPRI from 10 different sets of user initializations. Our results from the accuracy test demonstrated that RW (0.63) showed the highest NPRI value, compared to WRW (0.61), GWC (0.60), GHC (0.58), LS (0.57), GBC (0.27). The results from the reproducibility test indicated that GBC is more sensitive to user initialization than the other five algorithms. Compared to previous tumor segmentation validation studies using one set of reference segmentation, our evaluation methods use multiple sets of expert segmentation to address the inter or intra rater variability issue in ground truth annotation, and provide quantitative assessment for comparing different segmentation algorithms.

  4. A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

    PubMed Central

    BahadarKhan, Khan; A Khaliq, Amir; Shahid, Muhammad

    2016-01-01

    Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts. PMID:27441646

  5. A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding.

    PubMed

    BahadarKhan, Khan; A Khaliq, Amir; Shahid, Muhammad

    2016-01-01

    Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts.

  6. Liver segmentation with new supervised method to create initial curve for active contour.

    PubMed

    Zareei, Abouzar; Karimi, Abbas

    2016-08-01

    The liver performs a critical task in the human body; therefore, detecting liver diseases and preparing a robust plan for treating them are both crucial. Liver diseases kill nearly 25,000 Americans every year. A variety of image segmentation methods are available to determine the liver's position and to detect possible liver tumors. Among these is the Active Contour Model (ACM), a framework which has proven very sensitive to initial contour delineation and control parameters. In the proposed method based on image energy, we attempted to obtain an initial segmentation close to the liver's boundary, and then implemented an ACM to improve the initial segmentation. The ACM used in this work incorporates gradient vector flow (GVF) and balloon energy in order to overcome ACM limitations, such as local minima entrapment and initial contour dependency. Additionally, in order to adjust active contour control parameters, we applied a genetic algorithm to produce a proper parameter set close to the optimal solution. The pre-processing method has a better ability to segment the liver tissue during a short time with respect to other mentioned methods in this paper. The proposed method was performed using Sliver CT image datasets. The results show high accuracy, precision, sensitivity, specificity and low overlap error, MSD and runtime with few ACM iterations.

  7. Intracellular pH regulation in resting and contracting segments of rat mesenteric resistance vessels.

    PubMed Central

    Aalkjaer, C; Cragoe, E J

    1988-01-01

    1. The pH-sensitive dye 2',7'-bis-(2-carboxyethyl)-5 (and -6)-carboxyfluorescein (BCECF) was used to measure intracellular pH (pHi) in segments of rat resistance vessels (internal diameter about 200 microns) with the vessels mounted in a myograph for simultaneous measurements of isometric contraction. 2. BCECF loaded slowly into the vessels over 1 h and did not affect the maximal contractility of the vessels. There was a loss of dye with time which, however, was very slow when the segments were only excited for 2 s/min, suggesting that the loss was mainly due to dye bleaching with only a very slow leak. 3. The ratio of the emissions (at 540 nm) with excitation at 495 and 450 nm was calibrated in terms of pH using the K+-H+ ionophore nigericin. This calibration gave a pHi value of 7.15 +/- 0.02 (n = 20), suggesting that hydrogen ions are not in electrochemical equilibrium in these vascular smooth muscles which have a membrane potential of about -60 mV. 4. Addition of 10 mM-NH4Cl caused a transient alkalinization and wash-out of 10 mM-NH4Cl a transient acidification. Increasing CO2 with maintained bicarbonate caused a rapid acidification followed by an incomplete recovery. Removal of CO2 and bicarbonate (HEPES-buffered solution) with constant extracellular pH caused a transient alkalinization but steady-state pHi was not significantly altered. 5. In bicarbonate-free buffer the Na+-H+ exchange blocker 5-(N-ethyl-N-isopropyl) amiloride (EIPA) and sodium-free conditions caused a slow acidification. In bicarbonate buffer (PSS) EIPA had no detectable effect after 10 min but the anion exchange blocker diisothio-cyanatostilbenedisulphonic acid (DIDS) caused a small acidification over that time course. 6. The rate of recovery after an acid load was about 50% lower in HEPES buffer compared to PSS and it was inhibited by EIPA. In PSS amiloride and EIPA each had a small inhibitory effect on the pH recovery after an acid load. DIDS also inhibited the recovery from an acid load

  8. Reconstruction of vessel structures from serial whole slide sections of murine liver samples

    NASA Astrophysics Data System (ADS)

    Schwier, Michael; Hahn, Horst K.; Dahmen, Uta; Dirsch, Olaf

    2013-03-01

    Image-based analysis of the vascular structures of murine liver samples is an important tool for scientists to understand liver physiology and morphology. Typical assessment methods are MicroCT, which allows for acquiring images of the whole organ while lacking resolution for fine details, and confocal laser scanning microscopy, which allows detailed insights into fine structures while lacking the broader context. Imaging of histological serial whole slide sections is a recent technology able to fill this gap, since it provides a fine resolution up to the cellular level, but on a whole organ scale. However, whole slide imaging is a modality providing only 2D images. Therefore the challenge is to use stacks of serial sections from which to reconstruct the 3D vessel structures. In this paper we present a semi-automatic procedure to achieve this goal. We employ an automatic method that detects vessel structures based on continuity and shape characteristics. Furthermore it supports the user to perform manual corrections where required. With our methods we were able to successfully extract and reconstruct vessel structures from a stack of 100 and a stack of 397 serial sections of a mouse liver lobe, thus proving the potential of our approach.

  9. Low level arsenic promotes progressive inflammatory angiogenesis and liver blood vessel remodeling in mice

    SciTech Connect

    Straub, Adam C.; Stolz, Donna B.; Vin, Harina; Ross, Mark A.; Soucy, Nicole V.; Klei, Linda R.; Barchowsky, Aaron

    2007-08-01

    The vascular effects of arsenic in drinking water are global health concerns contributing to human disease worldwide. Arsenic targets the endothelial cells lining blood vessels, and endothelial cell activation or dysfunction may underlie the pathogenesis of both arsenic-induced vascular diseases and arsenic-enhanced tumorigenesis. The purpose of the current studies was to demonstrate that exposing mice to drinking water containing environmentally relevant levels of arsenic promoted endothelial cell dysfunction and pathologic vascular remodeling. Increased angiogenesis, neovascularization, and inflammatory cell infiltration were observed in Matrigel plugs implanted in C57BL/6 mice following 5-week exposures to 5-500 ppb arsenic [Soucy, N.V., Mayka, D., Klei, L.R., Nemec, A.A., Bauer, J.A., Barchowsky, A., 2005. Neovascularization and angiogenic gene expression following chronic arsenic exposure in mice. Cardiovasc.Toxicol 5, 29-42]. Therefore, functional in vivo effects of arsenic on endothelial cell function and vessel remodeling in an endogenous vascular bed were investigated in the liver. Liver sinusoidal endothelial cells (LSEC) became progressively defenestrated and underwent capillarization to decrease vessel porosity following exposure to 250 ppb arsenic for 2 weeks. Sinusoidal expression of PECAM-1 and laminin-1 proteins, a hallmark of capillarization, was also increased by 2 weeks of exposure. LSEC caveolin-1 protein and caveolae expression were induced after 2 weeks of exposure indicating a compensatory change. Likewise, CD45/CD68-positive inflammatory cells did not accumulate in the livers until after LSEC porosity was decreased, indicating that inflammation is a consequence and not a cause of the arsenic-induced LSEC phenotype. The data demonstrate that the liver vasculature is an early target of pathogenic arsenic effects and that the mouse liver vasculature is a sensitive model for investigating vascular health effects of arsenic.

  10. Low level arsenic promotes progressive inflammatory angiogenesis and liver blood vessel remodeling in mice

    PubMed Central

    Straub, Adam C.; Stolz, Donna B.; Vin, Harina; Ross, Mark A.; Soucy, Nicole V.; Klei, Linda R.; Barchowsky, Aaron

    2006-01-01

    The vascular effects of arsenic in drinking water are global health concerns contributing to human disease worldwide. Arsenic targets the endothelial cells lining blood vessels and endothelial cell activation or dysfunction may underlie the pathogenesis of both arsenic-induced vascular diseases and arsenic-enhanced tumorigenesis. The purpose of the current studies was to demonstrate that exposing mice to drinking water containing environmentally relevant levels of arsenic promoted endothelial cell dysfunction and pathologic vascular remodeling. Increased angiogenesis, neovascularization, and inflammatory cell infiltration was observed in Matrigel plugs implanted in C57BL/6 mice following 5 week exposures to 5-500 ppb arsenic (Soucy et al., 2005). Therefore, functional in vivo effects of arsenic on endothelial cell function and vessel remodeling in an endogenous vascular bed were investigated in the liver. Liver sinusoidal endothelial cells (LSEC) became progressively defenestrated and underwent capillarization to decrease vessel porosity following exposure to 250 ppb arsenic for 2 weeks. Sinusoidal expression of PECAM-1 and laminin-1 proteins, a hallmark of capillarization, was also increased by 2 weeks of exposure. LSEC caveolin-1 protein and caveolae expression were induced after 2 weeks of exposure indicating a compensatory change. Likewise, CD45/CD68 positive inflammatory cells did not accumulate in the livers until after LSEC porosity was decreased; indicating that inflammation is a consequence and not a cause of the arsenic-induced LSEC phenotype. The data demonstrate that the liver vasculature is an early target of pathogenic arsenic effects and that the mouse liver vasculature is a sensitive model for investigating vascular health effects of arsenic. PMID:17123562

  11. Liver: segment-specific analysis of B1 field homogeneity at 3.0-T MR imaging with single-source versus dual-source parallel radiofrequency excitation.

    PubMed

    Pazahr, Shila; Fischer, Michael Alexander; Chuck, Natalie; Luechinger, Roger; Schick, Fritz; Nanz, Daniel; Boss, Andreas

    2012-11-01

    To measure B1 field distribution in different liver segments with and without dual transmission and to quantify the contrast-to-noise ratio (CNR) between normal liver tissue and segmental venous vessels on standard clinical 3.0-T liver magnetic resonance (MR) images. This prospective study was approved by the local ethics committee. All subjects gave written informed consent. Six patients with liver lesions and nine healthy volunteers were included. Average hepatic B1 field values in all Couinaud liver segments were assessed by using actual flip-angle imaging (first and second repetition times msec/echo time msec: 72, 192/2.2; transmission angle: 60°) for both single and dual transmission in a 3.0-T MR imaging unit that allowed both transmission modes. Additionally, two-dimensional T1-weighted gradient-echo (repetition time msec/echo time msec, 180/2.3; transmission angle, 55°) and T2-weighted single-shot fast spin-echo images (1501/80) were acquired. Average CNR between liver parenchyma and segmental veins were measured in each segment. Two-sided paired Student t tests were used for statistical evaluation. Two blinded radiologists independently identified lesions in images from acquisitions in both transmission modes. Mean flip angles achieved with conventional single transmission were 44%-53% of the nominal value in segments II-IV and 67% and 63% of the nominal value in segments VI and VII, respectively, and were less than 77% in all segments. Mean actual flip angles measured for dual transmission were between 82% and 100% of the nominal value in all segments. T1-weighted single-transmission images exhibited areas of low B1 field strength with reduced image contrast. T2-weighted single-transmission images displayed significantly reduced signal intensity but nearly unchanged contrast weighting in these areas. On T1-weighted dual-transmission images, the two readers detected 22 and 14 additional lesions that they did not identify on the single-transmission images

  12. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution

    NASA Astrophysics Data System (ADS)

    Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing

    2016-12-01

    The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of 80.3+/- 4.5 , yielding a mean Dice similarity coefficient of 97.25+/- 0.65 % , and an average symmetric surface distance of 0.84+/- 0.25 mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

  13. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution.

    PubMed

    Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing

    2016-12-21

    The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of [Formula: see text], yielding a mean Dice similarity coefficient of [Formula: see text], and an average symmetric surface distance of [Formula: see text] mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

  14. Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes

    PubMed Central

    Eapen, Maya; Korah, Reeba; Geetha, G.

    2015-01-01

    The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method. PMID:26689833

  15. Liver segmentation in MRI: A fully automatic method based on stochastic partitions.

    PubMed

    López-Mir, F; Naranjo, V; Angulo, J; Alcañiz, M; Luna, L

    2014-04-01

    There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 ± 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.

  16. Retinal image analysis aimed at blood vessel tree segmentation and early detection of neural-layer deterioration.

    PubMed

    Jan, J; Odstrcilik, J; Gazarek, J; Kolar, R

    2012-09-01

    An automatic method of segmenting the retinal vessel tree and estimating status of retinal neural fibre layer (NFL) from high resolution fundus camera images is presented. First, reliable blood vessel segmentation, using 2D directional matched filtering, enables to remove areas occluded by blood vessels thus leaving remaining retinal area available to the following NFL detection. The local existence of rather faint and hardly visible NFL is detected by combining several newly designed local textural features, sensitive to subtle NFL characteristics, into feature vectors submitted to a trained neural-network classifier. Obtained binary retinal maps of NFL distribution show a good agreement with both medical expert evaluations and quantitative results obtained by optical coherence tomography.

  17. Iterative mesh transformation for 3D segmentation of livers with cancers in CT images.

    PubMed

    Lu, Difei; Wu, Yin; Harris, Gordon; Cai, Wenli

    2015-07-01

    Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semi-automated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases.

  18. Radiographic Response to Yttrium-90 Radioembolization in Anterior Versus Posterior Liver Segments

    SciTech Connect

    Ibrahim, Saad M.; Lewandowski, Robert J.; Ryu, Robert K.; Sato, Kent T.; Gates, Vanessa L.; Mulcahy, Mary F.; Kulik, Laura; Larson, Andrew C.; Omary, Reed A.; Salem, Riad

    2008-11-15

    The purpose of our study was to determine if preferential radiographic tumor response occurs in tumors located in posterior versus anterior liver segments following radioembolization with yttrium-90 glass microspheres. One hundred thirty-seven patients with chemorefractory liver metastases of various primaries were treated with yttrium-90 glass microspheres. Of these, a subset analysis was performed on 89 patients who underwent 101 whole-right-lobe infusions to liver segments V, VI, VII, and VIII. Pre- and posttreatment imaging included either triphasic contrast material-enhanced CT or gadolinium-enhanced MRI. Responses to treatment were compared in anterior versus posterior right lobe lesions using both RECIST and WHO criteria. Statistical comparative studies were conducted in 42 patients with both anterior and posterior segment lesions using the paired-sample t-test. Pearson correlation was used to determine the relationship between pretreatment tumor size and posttreatment tumor response. Median administered activity, delivered radiation dose, and treatment volume were 2.3 GBq, 118.2 Gy, and 1,072 cm{sup 3}, respectively. Differences between the pretreatment tumor size of anterior and posterior liver segments were not statistically significant (p = 0.7981). Differences in tumor response between anterior and posterior liver segments were not statistically significant using WHO criteria (p = 0.8557). A statistically significant correlation did not exist between pretreatment tumor size and posttreatment tumor response (r = 0.0554, p = 0.4434). On imaging follow-up using WHO criteria, for anterior and posterior regions of the liver, (1) response rates were 50% (PR = 50%) and 45% (CR = 9%, PR = 36%), and (2) mean changes in tumor size were -41% and -40%. In conclusion, this study did not find evidence of preferential radiographic tumor response in posterior versus anterior liver segments treated with yttrium-90 glass microspheres.

  19. Iterative Mesh Transformation for 3D Segmentation of Livers with Cancers in CT Images

    PubMed Central

    Lu, Difei; Wu, Yin; Harris, Gordon; Cai, Wenli

    2015-01-01

    Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semiautomated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases. PMID:25728595

  20. A 3-D liver segmentation method with parallel computing for selective internal radiation therapy.

    PubMed

    Goryawala, Mohammed; Guillen, Magno R; Cabrerizo, Mercedes; Barreto, Armando; Gulec, Seza; Barot, Tushar C; Suthar, Rekha R; Bhatt, Ruchir N; Mcgoron, Anthony; Adjouadi, Malek

    2012-01-01

    This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a special localized contouring algorithm. In the segmentation process, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction. Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to 442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.

  1. Segmentation of the heart and great vessels in CT images using a model-based adaptation framework.

    PubMed

    Ecabert, Olivier; Peters, Jochen; Walker, Matthew J; Ivanc, Thomas; Lorenz, Cristian; von Berg, Jens; Lessick, Jonathan; Vembar, Mani; Weese, Jürgen

    2011-12-01

    Recently, model-based methods for the automatic segmentation of the heart chambers have been proposed. An important application of these methods is the characterization of the heart function. Heart models are, however, increasingly used for interventional guidance making it necessary to also extract the attached great vessels. It is, for instance, important to extract the left atrium and the proximal part of the pulmonary veins to support guidance of ablation procedures for atrial fibrillation treatment. For cardiac resynchronization therapy, a heart model including the coronary sinus is needed. We present a heart model comprising the four heart chambers and the attached great vessels. By assigning individual linear transformations to the heart chambers and to short tubular segments building the great vessels, variable sizes of the heart chambers and bending of the vessels can be described in a consistent way. A configurable algorithmic framework that we call adaptation engine matches the heart model automatically to cardiac CT angiography images in a multi-stage process. First, the heart is detected using a Generalized Hough Transformation. Subsequently, the heart chambers are adapted. This stage uses parametric as well as deformable mesh adaptation techniques. In the final stage, segments of the large vascular structures are successively activated and adapted. To optimize the computational performance, the adaptation engine can vary the mesh resolution and freeze already adapted mesh parts. The data used for validation were independent from the data used for model-building. Ground truth segmentations were generated for 37 CT data sets reconstructed at several cardiac phases from 17 patients. Segmentation errors were assessed for anatomical sub-structures resulting in a mean surface-to-surface error ranging 0.50-0.82mm for the heart chambers and 0.60-1.32mm for the parts of the great vessels visible in the images.

  2. Automatic seed selection for segmentation of liver cirrhosis in laparoscopic sequences

    NASA Astrophysics Data System (ADS)

    Sinha, Rahul; Marcinczak, Jan Marek; Grigat, Rolf-Rainer

    2014-03-01

    For computer aided diagnosis based on laparoscopic sequences, image segmentation is one of the basic steps which define the success of all further processing. However, many image segmentation algorithms require prior knowledge which is given by interaction with the clinician. We propose an automatic seed selection algorithm for segmentation of liver cirrhosis in laparoscopic sequences which assigns each pixel a probability of being cirrhotic liver tissue or background tissue. Our approach is based on a trained classifier using SIFT and RGB features with PCA. Due to the unique illumination conditions in laparoscopic sequences of the liver, a very low dimensional feature space can be used for classification via logistic regression. The methodology is evaluated on 718 cirrhotic liver and background patches that are taken from laparoscopic sequences of 7 patients. Using a linear classifier we achieve a precision of 91% in a leave-one-patient-out cross-validation. Furthermore, we demonstrate that with logistic probability estimates, seeds with high certainty of being cirrhotic liver tissue can be obtained. For example, our precision of liver seeds increases to 98.5% if only seeds with more than 95% probability of being liver are used. Finally, these automatically selected seeds can be used as priors in Graph Cuts which is demonstrated in this paper.

  3. SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

    SciTech Connect

    Li, Dengwang; Wang, Jie; Kapp, Daniel S.; Xing, Lei

    2015-06-15

    Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is

  4. A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography.

    PubMed

    Boegel, Marco; Hoelter, Philip; Redel, Thomas; Maier, Andreas; Hornegger, Joachim; Doerfler, Arnd

    2015-01-01

    Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.

  5. Computerized Liver Volumetry on MRI by Using 3D Geodesic Active Contour Segmentation

    PubMed Central

    Huynh, Hieu Trung; Karademir, Ibrahim; Oto, Aytekin; Suzuki, Kenji

    2014-01-01

    OBJECTIVE Our purpose was to develop an accurate automated 3D liver segmentation scheme for measuring liver volumes on MRI. SUBJECTS AND METHODS Our scheme for MRI liver volumetry consisted of three main stages. First, the preprocessing stage was applied to T1-weighted MRI of the liver in the portal venous phase to reduce noise and produce the boundary-enhanced image. This boundary-enhanced image was used as a speed function for a 3D fast-marching algorithm to generate an initial surface that roughly approximated the shape of the liver. A 3D geodesic-active-contour segmentation algorithm refined the initial surface to precisely determine the liver boundaries. The liver volumes determined by our scheme were compared with those manually traced by a radiologist, used as the reference standard. RESULTS The two volumetric methods reached excellent agreement (intraclass correlation coefficient, 0.98) without statistical significance (p = 0.42). The average (± SD) accuracy was 99.4% ± 0.14%, and the average Dice overlap coefficient was 93.6% ± 1.7%. The mean processing time for our automated scheme was 1.03 ± 0.13 minutes, whereas that for manual volumetry was 24.0 ± 4.4 minutes (p < 0.001). CONCLUSION The MRI liver volumetry based on our automated scheme agreed excellently with reference-standard volumetry, and it required substantially less completion time. PMID:24370139

  6. [Robotic-assisted and laparoscopic hepatic resections for nonparasitic cysts of posterior liver segments].

    PubMed

    Berelavichus, S V; Kriger, A G; Starkov, Iu G; Shishin, K V; Gorin, D S; Poliakov, I S

    2013-01-01

    Results of 36 robotic-assisted and laparoscopic hepatic resections for nonparasitic cysts of posterior liver segments were demonstrated. Technical aspects of the procedure, advantages and drawbacks of each method were discussed. Important intra- and postoperative indexes were compared. The study allows to state, that the use of the da Vinci robotic surgical system has certain technical advantages over the standard laparoscopic technique in case of the posterior location of liver cysts.

  7. Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT

    SciTech Connect

    Yuan, Yading Chao, Ming; Sheu, Ren-Dih; Rosenzweig, Kenneth; Lo, Yeh-Chi

    2015-07-15

    Purpose: This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images. Methods: In order to track the fuzzy liver–chestwall and liver–heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border. The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors’ automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist. Results: The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm{sup 3}, whereas it was 1719 cm{sup 3} for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98. Conclusions: The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors’ results also clearly

  8. 3D liver segmentation using multiple region appearances and graph cuts

    SciTech Connect

    Peng, Jialin Zhang, Hongbo; Hu, Peijun; Lu, Fang; Kong, Dexing; Peng, Zhiyi

    2015-12-15

    Purpose: Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. Methods: The authors propose a novel multiregion-appearance based approach with graph cuts to delineate the liver surface. For livers with multiple subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. Results: The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4 ± 3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7% ± 0.5% and 97.5% ± 0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets

  9. Surgical Implications of Portal Vein Variations and Liver Segmentations: A Recent Update

    PubMed Central

    Iqbal, Raiz; Iqbal, Faiz

    2017-01-01

    The Couinaud’s liver segmentation is based on the identification of portal vein bifurcation and origin of hepatic veins. It is widely used clinically, because it is better suited for surgery and is more accurate in localizing and monitoring various intra parenchymal lesions. According to standard anatomy, the portal vein bifurcates into right and left branches; the left vein drains segment II, III and IV and the right vein divides into two secondary branches - the anterior portal vein drains segments V and VIII, and the posterior drains segments VI and VII. The portal vein variants such as portal trifurcation, with division of the main portal vein into the left, right anterior, and posterior branches, and the early origin of the right posterior branch directly from the main portal vein were found to be more frequent and was seen in about 20 - 35% of the population. Accurate knowledge of the portal variants and consequent variations in vascular segments are essential for intervention radiologists and transplant surgeons in the proper diagnosis during radiological investigations and in therapeutic applications such as preparation for biopsy, Portal Vein Embolization (PVE), Transjugular Intrahepatic Porto-Systemic Shunt (TIPS), tumour resection and partial hepatectomy for split or living donor transplantations. The advances in the knowledge will reduce intra and postoperative complications and avoid major catastrophic events. The purpose of the present review is to update the normal and variant portal venous anatomy and their implications in the liver segmentations, complex liver surgeries and various radiological intervention procedures. PMID:28384848

  10. Surgical Implications of Portal Vein Variations and Liver Segmentations: A Recent Update.

    PubMed

    Iqbal, Showkathali; Iqbal, Raiz; Iqbal, Faiz

    2017-02-01

    The Couinaud's liver segmentation is based on the identification of portal vein bifurcation and origin of hepatic veins. It is widely used clinically, because it is better suited for surgery and is more accurate in localizing and monitoring various intra parenchymal lesions. According to standard anatomy, the portal vein bifurcates into right and left branches; the left vein drains segment II, III and IV and the right vein divides into two secondary branches - the anterior portal vein drains segments V and VIII, and the posterior drains segments VI and VII. The portal vein variants such as portal trifurcation, with division of the main portal vein into the left, right anterior, and posterior branches, and the early origin of the right posterior branch directly from the main portal vein were found to be more frequent and was seen in about 20 - 35% of the population. Accurate knowledge of the portal variants and consequent variations in vascular segments are essential for intervention radiologists and transplant surgeons in the proper diagnosis during radiological investigations and in therapeutic applications such as preparation for biopsy, Portal Vein Embolization (PVE), Transjugular Intrahepatic Porto-Systemic Shunt (TIPS), tumour resection and partial hepatectomy for split or living donor transplantations. The advances in the knowledge will reduce intra and postoperative complications and avoid major catastrophic events. The purpose of the present review is to update the normal and variant portal venous anatomy and their implications in the liver segmentations, complex liver surgeries and various radiological intervention procedures.

  11. A Clustering Algorithm for Liver Lesion Segmentation of Diffusion-Weighted MR Images

    PubMed Central

    Jha, Abhinav K.; Rodríguez, Jeffrey J.; Stephen, Renu M.; Stopeck, Alison T.

    2010-01-01

    In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms. PMID:21151837

  12. US-Cut: interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions

    NASA Astrophysics Data System (ADS)

    Egger, Jan; Voglreiter, Philip; Dokter, Mark; Hofmann, Michael; Chen, Xiaojun; Zoller, Wolfram G.; Schmalstieg, Dieter; Hann, Alexander

    2016-04-01

    Ultrasound (US) is the most commonly used liver imaging modality worldwide. It plays an important role in follow-up of cancer patients with liver metastases. We present an interactive segmentation approach for liver tumors in US acquisitions. Due to the low image quality and the low contrast between the tumors and the surrounding tissue in US images, the segmentation is very challenging. Thus, the clinical practice still relies on manual measurement and outlining of the tumors in the US images. We target this problem by applying an interactive segmentation algorithm to the US data, allowing the user to get real-time feedback of the segmentation results. The algorithm has been developed and tested hand-in-hand by physicians and computer scientists to make sure a future practical usage in a clinical setting is feasible. To cover typical acquisitions from the clinical routine, the approach has been evaluated with dozens of datasets where the tumors are hyperechoic (brighter), hypoechoic (darker) or isoechoic (similar) in comparison to the surrounding liver tissue. Due to the interactive real-time behavior of the approach, it was possible even in difficult cases to find satisfying segmentations of the tumors within seconds and without parameter settings, and the average tumor deviation was only 1.4mm compared with manual measurements. However, the long term goal is to ease the volumetric acquisition of liver tumors in order to evaluate for treatment response. Additional aim is the registration of intraoperative US images via the interactive segmentations to the patient's pre-interventional CT acquisitions.

  13. A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry.

    PubMed

    Tsou, Chi-Hsuan; Lu, Yi-Chien; Yuan, Ang; Chang, Yeun-Chung; Chen, Chung-Ming

    2015-01-01

    The blood vessel density in a cancerous tissue sample may represent increased levels of tumor growth. However, identifying blood vessels in the histological (tissue) image is difficult and time-consuming and depends heavily on the observer's experience. To overcome this drawback, computer-aided image analysis frameworks have been investigated in order to boost object identification in histological images. We present a novel algorithm to automatically abstract the salient regions in blood vessel images. Experimental results show that the proposed framework is capable of deriving vessel boundaries that are comparable to those demarcated manually, even for vessel regions with weak contrast between the object boundaries and background clutter.

  14. Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation.

    PubMed

    Huang, Weimin; Yang, Yongzhong; Lin, Zhiping; Huang, Guang-Bin; Zhou, Jiayin; Duan, Yuping; Xiong, Wei

    2014-01-01

    This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results.

  15. Automated liver segmentation for whole-body low-contrast CT images from PET-CT scanners.

    PubMed

    Wang, Xiuying; Li, Changyang; Eberl, Stefan; Fulham, Michael; Feng, Dagan

    2009-01-01

    Accurate objective automated liver segmentation in PET-CT studies is important to improve the identification and localization of hepatic tumor. However, this segmentation is an extremely challenging task from the low-contrast CT images captured from PET-CT scanners because of the intensity similarity between liver and adjacent loops of bowel, stomach and muscle. In this paper, we propose a novel automated three-stage liver segmentation technique for PET-CT whole body studies, where: 1) the starting liver slice is automatically localized based on the liver - lung relations; 2) the "masking" slice containing the biggest liver section is localized using the ratio of liver ROI size to the right half of abdomen ROI size; 3) the liver segmented from the "masking" slice forms the initial estimation or mask for the automated liver segmentation. Our experimental results from clinical PET-CT studies show that this method can automatically segment the liver for a range of different patients, with consistent objective selection criteria and reproducible accurate results.

  16. Evaluation of an improved technique for lumen path definition and lumen segmentation of atherosclerotic vessels in CT angiography.

    PubMed

    van Velsen, Evert F S; Niessen, Wiro J; de Weert, Thomas T; de Monyé, Cécile; van der Lugt, Aad; Meijering, Erik; Stokking, Rik

    2007-07-01

    Vessel image analysis is crucial when considering therapeutical options for (cardio-) vascular diseases. Our method, VAMPIRE (Vascular Analysis using Multiscale Paths Inferred from Ridges and Edges), involves two parts: a user defines a start- and endpoint upon which a lumen path is automatically defined, and which is used for initialization; the automatic segmentation of the vessel lumen on computed tomographic angiography (CTA) images. Both parts are based on the detection of vessel-like structures by analyzing intensity, edge, and ridge information. A multi-observer evaluation study was performed to compare VAMPIRE with a conventional method on the CTA data of 15 patients with carotid artery stenosis. In addition to the start- and endpoint, the two radiologists required on average 2.5 (SD: 1.9) additional points to define a lumen path when using the conventional method, and 0.1 (SD: 0.3) when using VAMPIRE. The segmentation results were quantitatively evaluated using Similarity Indices, which were slightly lower between VAMPIRE and the two radiologists (respectively 0.90 and 0.88) compared with the Similarity Index between the radiologists (0.92). The evaluation shows that the improved definition of a lumen path requires minimal user interaction, and that using this path as initialization leads to good automatic lumen segmentation results.

  17. Influence of large peritumoral vessels on outcome of radiofrequency ablation of liver tumors.

    PubMed

    Lu, David S K; Raman, Steven S; Limanond, Piyaporn; Aziz, Donya; Economou, James; Busuttil, Ronald; Sayre, James

    2003-10-01

    The effect of large vessels (>/=3 mm) contiguous to hepatic tumors was evaluated with respect to clinical tumor recurrence rates after radiofrequency (RF) ablation. The first 105 malignant liver tumors treated by RF ablation therapy at our institution with pathologic analysis or a minimum of 6 months of clinical follow-up were reviewed. The original pretreatment imaging studies were reviewed by a radiologist who was blinded to the cases, and, based on lesion contiguity to vessels of at least 3 mm, the lesions were categorized as perivascular or nonperivascular. Treatment outcomes with respect to local tumor recurrence between these two groups were then compared. Logistic regression analysis was performed to take into account other variables and to determine whether this categorization was an independent predictor of treatment outcome. There were 74 nonperivascular tumors and 31 perivascular tumors. Mean tumor size was 2.4 cm and mean follow-up was 11.3 months. Residual or locally recurrent tumors were documented in 20 of 105 cases (19%). In the nonperivascular group, five of 74 (7%) had either incompletely treated tumor (manifested within 6 months) or local recurrence beyond 6 months. In the perivascular group, 15 of 31 (48%) had incompletely treated or locally recurrent tumor (P <.001). Subanalysis of lesion size (61 tumors 4 cm), tumor type (40 hepatocellular carcinomas, 48 colorectal metastases, and 17 other metastases), access (53 intraoperative, 52 percutaneous), and RF device (45 Radiotherapeutics electrodes, 18 Rita electrodes, and 42 Radionics electrodes) showed similar results. Multivariate logistic regression analysis showed that presence or absence of a large peritumoral vessel is an independent, and the dominant, predictor of treatment outcome. The presence of vessels at least 3 mm in size contiguous to hepatic tumors is a strong independent predictor of incomplete tumor destruction by RF ablation. Modified

  18. Fuzzy rule-based image segmentation in dynamic MR images of the liver

    NASA Astrophysics Data System (ADS)

    Kobashi, Syoji; Hata, Yutaka; Tokimoto, Yasuhiro; Ishikawa, Makato

    2000-06-01

    This paper presents a fuzzy rule-based region growing method for segmenting two-dimensional (2-D) and three-dimensional (3- D) magnetic resonance (MR) images. The method is an extension of the conventional region growing method. The proposed method evaluates the growing criteria by using fuzzy inference techniques. The use of the fuzzy if-then rules is appropriate for describing the knowledge of the legions on the MR images. To evaluate the performance of the proposed method, it was applied to artificially generated images. In comparison with the conventional method, the proposed method shows high robustness for noisy images. The method then applied for segmenting the dynamic MR images of the liver. The dynamic MR imaging has been used for diagnosis of hepatocellular carcinoma (HCC), portal hypertension, and so on. Segmenting the liver, portal vein (PV), and inferior vena cava (IVC) can give useful description for the diagnosis, and is a basis work of a pres-surgery planning system and a virtual endoscope. To apply the proposed method, fuzzy if-then rules are derived from the time-density curve of ROIs. In the experimental results, the 2-D reconstructed and 3-D rendered images of the segmented liver, PV, and IVC are shown. The evaluation by a physician shows that the generated images are comparable to the hepatic anatomy, and they would be useful to understanding, diagnosis, and pre-surgery planning.

  19. A hybrid 3D region growing and 4D curvature analysis-based automatic abdominal blood vessel segmentation through contrast enhanced CT

    NASA Astrophysics Data System (ADS)

    Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Shimada, Mitsuo; Iinuma, Gen

    2017-03-01

    In abdominal disease diagnosis and various abdominal surgeries planning, segmentation of abdominal blood vessel (ABVs) is a very imperative task. Automatic segmentation enables fast and accurate processing of ABVs. We proposed a fully automatic approach for segmenting ABVs through contrast enhanced CT images by a hybrid of 3D region growing and 4D curvature analysis. The proposed method comprises three stages. First, candidates of bone, kidneys, ABVs and heart are segmented by an auto-adapted threshold. Second, bone is auto-segmented and classified into spine, ribs and pelvis. Third, ABVs are automatically segmented in two sub-steps: (1) kidneys and abdominal part of the heart are segmented, (2) ABVs are segmented by a hybrid approach that integrates a 3D region growing and 4D curvature analysis. Results are compared with two conventional methods. Results show that the proposed method is very promising in segmenting and classifying bone, segmenting whole ABVs and may have potential utility in clinical use.

  20. Metastatic liver tumour segmentation with a neural network-guided 3D deformable model.

    PubMed

    Vorontsov, Eugene; Tang, An; Roy, David; Pal, Christopher J; Kadoury, Samuel

    2017-01-01

    The segmentation of liver tumours in CT images is useful for the diagnosis and treatment of liver cancer. Furthermore, an accurate assessment of tumour volume aids in the diagnosis and evaluation of treatment response. Currently, segmentation is performed manually by an expert, and because of the time required, a rough estimate of tumour volume is often done instead. We propose a semi-automatic segmentation method that makes use of machine learning within a deformable surface model. Specifically, we propose a deformable model that uses a voxel classifier based on a multilayer perceptron (MLP) to interpret the CT image. The new deformable model considers vertex displacement towards apparent tumour boundaries and regularization that promotes surface smoothness. During operation, a user identifies the target tumour and the mesh then automatically delineates the tumour from the MLP processed image. The method was tested on a dataset of 40 abdominal CT scans with a total of 95 colorectal metastases collected from a variety of scanners with variable spatial resolution. The segmentation results are encouraging with a Dice similarity metric of [Formula: see text] and demonstrates that the proposed method can deal with highly variable data. This work motivates further research into tumour segmentation using machine learning with more data and deeper neural networks.

  1. Automatic segmentation of hepatocellular structure from HE-stained liver tissue

    NASA Astrophysics Data System (ADS)

    Ishikawa, Masahiro; Ahi, Sercan Taha; Murakami, Yuri; Kimura, Fumikazu; Yamaguchi, Masahiro; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

    2013-03-01

    The analysis of hepatic tissue structure is required for quantitative assessment of liver histology. Especially, a cord-like structure of liver cells, called trabecura, has important information in the diagnosis of hepatocellular carcinoma (HCC). However, the extraction of trabeculae is thought to be difficult because liver cells take on various colors and appearances depending on tissue conditions. In this paper, we propose an approach to extract trabeculae from images of hematoxyline and eosin stained liver tissue slide by extracting the rest of trabeculae: sinusoids and stromal area. The sinusoids are simply extracted based on the color information, where the image is corrected by an orientation selective filtering before segmentaion. The stromal area mainly consists of fiber, and often includes lymphocytes densely. Therefore, in the proposed method, fiber region and lymphocytes are extracted separately, then, stromal region is determined based on the extracted results. The determination of stroma is performed based on superpixels, to obtain precise boundaries. Once the regions of sinusoids and stroma are obtained, trabeculae can be segmented as the remaining region. The proposed method was applied to 10 test images of normal and HCC liver tissues, and the results were evaluated based on the manual segmentation. As a result, we confirmed that both sensitivity and specificity of the extraction of trabeculae reach around 90%.

  2. ANGY: A Rule-Based Expert System for Automatic Segmentation of Coronary Vessels From Digital Subtracted Angiograms.

    PubMed

    Stansfield, S A

    1986-02-01

    This paper details the design and implementation of ANGY, a rule-based expert system in the domain of medical image processing. Given a subtracted digital angiogram of the chest, ANGY identifies and isolates the coronary vessels, while ignoring any nonvessel structures which may have arisen from noise, variations in background contrast, imperfect subtraction, and irrelevent anatomical detail. The overall system is modularized into three stages: the preprocessing stage and the two stages embodied in the expert itself. In the preprocessing stage, low-level image processing routines written in C are used to create a segmented representation of the input image. These routines are applied sequentially. The expert system is rule-based and is written in OPS5 and LISP. It is separated into two stages: The low-level image processing stage embodies a domain-independent knowledge of segmentation, grouping, and shape analysis. Working with both edges and regions, it determines such relations as parallel and adjacent and attempts to refine the segmentation begun by the preprocessing. The high-level medical stage embodies a domain-dependent knowledge of cardiac anatomy and physiology. Applying this knowledge to the objects and relations determined in the preceding two stages, it identifies those objects which are vessels and eliminates all others.

  3. Immunohistochemical Heterogeneity of the Endothelium of Blood and Lymphatic Vessels in the Developing Human Liver and in Adulthood.

    PubMed

    Nikolić, Ivan; Todorović, Vera; Petrović, Aleksandar; Petrović, Vladimir; Jović, Marko; Vladičić, Jelena; Puškaš, Nela

    2017-01-01

    The endothelium of liver sinusoids in relation to the endothelium of other blood vessels has specific antigen expression similar to the endothelium of lymphatic vessels. Bearing in mind that there is no consensus as to the period or intensity of the expression of certain antigens in the endothelium of blood and lymphatic vessels in the liver, the aim of our study was to immunohistochemically investigate the dynamic patterns of the expression of CD31, CD34, D2-40, and LYVE-1 antigens during liver development and in adulthood on paraffin tissue sections of human livers of 4 embryos, 38 fetuses, 6 neonates, and 6 adults. The results show that, in a histologically immature liver at the end of the embryonic period, CD34 molecules are expressed only on vein endothelium localized in developing portal areas, whereby the difference between portal venous branches and CD34-negative central veins belongs to the collecting venous system. In the fetal period, with aging, expression of CD34 and CD31 molecules on the endothelium of central veins and blood vessels of the portal areas increases. Sinusoidal endothelium shows light and sporadic CD34 immunoreactivity in the late embryonic and fetal periods, and is lost in the neonatal and adult periods, unlike CD31 immunoreactivity, which is poorly expressed in the fetal and neonatal periods but is present in adults. The endothelium of sinusoids and lymphatic vessels express LYVE-1, and the endothelium of lymphatic vessels express LYVE-1 and D2-40 but not CD34. Similarity between the sinusoidal and lymphatic endothelium includes the fact that both types are LYVE-1 positive and CD34 negative. © 2016 S. Karger AG, Basel.

  4. Flaw density examinations of a clad boiling water reactor pressure vessel segment

    SciTech Connect

    Cook, K.V.; McClung, R.W.

    1986-01-01

    Flaw density is the greatest uncertainty involved in probabilistic analyses of reactor pressure vessel failure. As part of the Heavy-Section Steel Technology (HSST) Program, studies have been conducted to determine flaw density in a section of reactor pressure vessel cut from the Hope Creek Unit 2 vessel (nominally 0.7 by 3 m (2 by 10 ft)). This section (removed from the scrapped vessel that was never in service) was evaluated nondestructively to determine the as-fabricated status. We had four primary objectives: (1) evaluate longitudinal and girth welds for flaws with manual ultrasonics, (2) evaluate the zone under the nominal 6.3-mm (0.25-in.) clad for cracking (again with manual ultrasonics), (3) evaluate the cladding for cracks with a high-sensitivity fluorescent penetrant method, and (4) determine the source of indications detected.

  5. TMA Vessel Segmentation Based on Color and Morphological Features: Application to Angiogenesis Research

    PubMed Central

    Fernández-Carrobles, M. Milagro; Tadeo, Irene; Bueno, Gloria; Noguera, Rosa; Déniz, Oscar; Salido, Jesús; García-Rojo, Marcial

    2013-01-01

    Given that angiogenesis and lymphangiogenesis are strongly related to prognosis in neoplastic and other pathologies and that many methods exist that provide different results, we aim to construct a morphometric tool allowing us to measure different aspects of the shape and size of vascular vessels in a complete and accurate way. The developed tool presented is based on vessel closing which is an essential property to properly characterize the size and the shape of vascular and lymphatic vessels. The method is fast and accurate improving existing tools for angiogenesis analysis. The tool also improves the accuracy of vascular density measurements, since the set of endothelial cells forming a vessel is considered as a single object. PMID:24489494

  6. TMA vessel segmentation based on color and morphological features: application to angiogenesis research.

    PubMed

    Fernández-Carrobles, M Milagro; Tadeo, Irene; Bueno, Gloria; Noguera, Rosa; Déniz, Oscar; Salido, Jesús; García-Rojo, Marcial

    2013-01-01

    Given that angiogenesis and lymphangiogenesis are strongly related to prognosis in neoplastic and other pathologies and that many methods exist that provide different results, we aim to construct a morphometric tool allowing us to measure different aspects of the shape and size of vascular vessels in a complete and accurate way. The developed tool presented is based on vessel closing which is an essential property to properly characterize the size and the shape of vascular and lymphatic vessels. The method is fast and accurate improving existing tools for angiogenesis analysis. The tool also improves the accuracy of vascular density measurements, since the set of endothelial cells forming a vessel is considered as a single object.

  7. Automatic detection and segmentation of liver metastatic lesions on serial CT examinations

    NASA Astrophysics Data System (ADS)

    Ben Cohen, Avi; Diamant, Idit; Klang, Eyal; Amitai, Michal; Greenspan, Hayit

    2014-03-01

    In this paper we present a fully automated method for detection and segmentation of liver metastases on serial CT examinations (portal phase) given a 2D baseline segmentation mask. Our database contains 27 CT scans, baselines and follow-ups, of 12 patients and includes 22 test cases. Our method is based on the information given in the baseline CT scan which contains the lesion's segmentation mask marked manually by a radiologist. We use the 2D baseline segmentation mask to identify the lesion location in the follow-up CT scan using non-rigid image registration. The baseline CT scan is also used to locate regions of tissues surrounding the lesion and to map them onto the follow-up CT scan, in order to reduce the search area on the follow-up CT scan. Adaptive region-growing and mean-shift segmentation are used to obtain the final lesion segmentation. The segmentation results are compared to those obtained by a human radiologist. Compared to the reference standard our method made a correct RECIST 1.1 assessment for 21 out of 22 test cases. The average Dice index was 0.83 +/- 0.07, average Hausdorff distance was 7.85+/- 4.84 mm, average sensitivity was 0.87 +/- 0.11 and positive predictive value was 0.81 +/- 0.10. The segmentation performance and the RECIST assessment results look promising. We are pursuing the methodology further with expansion to 3D segmentation while increasing the dataset we are collecting from the CT abdomen unit at Sheba medical center.

  8. 3D-SIFT-Flow for atlas-based CT liver image segmentation

    SciTech Connect

    Xu, Yan; Xu, Chenchao Kuang, Xiao; Wang, Hongkai; Chang, Eric I-Chao; Huang, Weimin; Fan, Yubo

    2016-05-15

    Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

  9. 3D-SIFT-Flow for atlas-based CT liver image segmentation.

    PubMed

    Xu, Yan; Xu, Chenchao; Kuang, Xiao; Wang, Hongkai; Chang, Eric I-Chao; Huang, Weimin; Fan, Yubo

    2016-05-01

    In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

  10. The reduced left lateral segment in pediatric liver transplantation: an alternative to the monosegment graft.

    PubMed

    Attia, M S; Stringer, M D; McClean, P; Prasad, K R

    2008-09-01

    Tailoring graft size to small paediatric recipients is a challenge. We have developed a reduced left lateral segment as an alternative to monosegment transplantation for small size recipients. Since November 2000, 89 children have been transplanted with 100 deceased donor liver grafts in our unit. Our median patient and graft survival is 89% and 88% respectively. Four of these cases were performed using a new technique of creating a small donor graft by reducing the left lateral segment. The median weight of the reduced liver graft was 264 g (range: 165-390 g). The median blood transfusion requirement was 101 mL/kg body weight (range 69-167 mL/kg). The median values of peak ALT were 1473 IU/L, INR 2.2 and bilirubin 293 micromol/L in the first two wk following surgery. One neonatal recipient died five days after transplantation from a massive intracranial haemorrhage despite satisfactory graft function. Another recipient with excellent graft function died 10 months later from primary pulmonary hypertension and secondary cardiac failure. Hepatic artery thrombosis occurred in one patient with successful revascularization but he was retransplanted three months later for chronic rejection. No biliary or venous outflow complications occurred in this group. This technique of reduced left lateral segment liver transplantation is an alternative to the monosegment graft and allows small recipients to be successfully transplanted with few technical complications related to graft preparation.

  11. Abnormal blood vessels formed by human liver cavernous hemangioma endothelial cells in nude mice are suitable for drug evaluation.

    PubMed

    Zhang, Wen-jian; Wu, Lian-qiu; Liu, Hong-lin; Ye, Li-ya; Xin, Yu-ling; Grau, Georges E; Lou, Jin-ning

    2009-12-01

    Cavernous hemangioma is vascular malformation with developmental aberrations. It was assumed that the abnormality of endothelial cells contributed greatly to the occurrence of cavernous hemangioma. In our previous study, we have found distinct characteristics of endothelial cells derived from human liver cavernous hemangioma (HCHEC). Here, we reported the abnormal vascular vessels formed by primary HCHEC in nude mice and that the drug podophyllotoxin can destroy HCHEC in vitro and in vivo. HCHEC was isolated from a human liver cavernous hemangioma specimen, and the HCHEC generated a red hemangioma-like mass 7 days after subcutaneously co-inoculating HCHEC and human liver cancer cells (Bel-7402) in nude mice. Lentiviral expression of GFP and immunohistochemistry for human CD31 was used to confirm that the HCHEC formed the blood vessels in nude mice. And the pathological features of vascular vessels formed by HCHEC were very similar to clinical cavernous hemangioma. In addition, by MTT assay, the drug podophyllotoxin was found inhibiting HCHEC viability, and by TUNEL and DNA ladder assays, podophyllotoxin was found inducing apoptosis of HCHEC. Moreover, podophyllotoxin was also effective for destroying the abnormal vascular vessels in the hemangioma-like mass in nude mice. In summary, the HCHEC can form abnormal blood vessels in nude mice, and we can evaluate drugs for cavernous hemangioma by using HCHEC in vitro and in vivo.

  12. Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

    PubMed Central

    He, Baochun; Ma, Zhiyuan; Zong, Mao; Zhou, Xiangrong; Fujita, Hiroshi

    2013-01-01

    A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method. PMID:24066017

  13. Automated identification of best-quality coronary artery segments from multiple-phase coronary CT angiography (cCTA) for vessel analysis

    NASA Astrophysics Data System (ADS)

    Zhou, Chuan; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Chughtai, Aamer; Wei, Jun; Kazerooni, Ella A.

    2016-03-01

    We are developing an automated method to identify the best quality segment among the corresponding segments in multiple-phase cCTA. The coronary artery trees are automatically extracted from different cCTA phases using our multi-scale vessel segmentation and tracking method. An automated registration method is then used to align the multiple-phase artery trees. The corresponding coronary artery segments are identified in the registered vessel trees and are straightened by curved planar reformation (CPR). Four features are extracted from each segment in each phase as quality indicators in the original CT volume and the straightened CPR volume. Each quality indicator is used as a voting classifier to vote the corresponding segments. A newly designed weighted voting ensemble (WVE) classifier is finally used to determine the best-quality coronary segment. An observer preference study is conducted with three readers to visually rate the quality of the vessels in 1 to 6 rankings. Six and 10 cCTA cases are used as training and test set in this preliminary study. For the 10 test cases, the agreement between automatically identified best-quality (AI-BQ) segments and radiologist's top 2 rankings is 79.7%, and between AI-BQ and the other two readers are 74.8% and 83.7%, respectively. The results demonstrated that the performance of our automated method was comparable to those of experienced readers for identification of the best-quality coronary segments.

  14. An accurate multimodal 3-D vessel segmentation method based on brightness variations on OCT layers and curvelet domain fundus image analysis.

    PubMed

    Kafieh, Raheleh; Rabbani, Hossein; Hajizadeh, Fedra; Ommani, Mohammadreza

    2013-10-01

    This paper proposes a multimodal approach for vessel segmentation of macular optical coherence tomography (OCT) slices along with the fundus image. The method is comprised of two separate stages; the first step is 2-D segmentation of blood vessels in curvelet domain, enhanced by taking advantage of vessel information in crossing OCT slices (named feedback procedure), and improved by suppressing the false positives around the optic nerve head. The proposed method for vessel localization of OCT slices is also enhanced utilizing the fact that retinal nerve fiber layer becomes thicker in the presence of the blood vessels. The second stage of this method is axial localization of the vessels in OCT slices and 3-D reconstruction of the blood vessels. Twenty-four macular spectral 3-D OCT scans of 16 normal subjects were acquired using a Heidelberg HRA OCT scanner. Each dataset consisted of a scanning laser ophthalmoscopy (SLO) image and limited number of OCT scans with size of 496 × 512 (namely, for a data with 19 selected OCT slices, the whole data size was 496 × 512 × 19). The method is developed with least complicated algorithms and the results show considerable improvement in accuracy of vessel segmentation over similar methods to produce a local accuracy of 0.9632 in area of SLO, covered with OCT slices, and the overall accuracy of 0.9467 in the whole SLO image. The results are also demonstrative of a direct relation between the overall accuracy and percentage of SLO coverage by OCT slices.

  15. Improved automated optic cup segmentation based on detection of blood vessel bends in retinal fundus images.

    PubMed

    Hatanaka, Yuji; Nagahata, Yuuki; Muramatsu, Chisako; Okumura, Susumu; Ogohara, Kazunori; Sawada, Akira; Ishida, Kyoko; Yamamoto, Tetsuya; Fujita, Hiroshi

    2014-01-01

    Glaucoma is a leading cause of permanent blindness. Retinal imaging is useful for early detection of glaucoma. In order to evaluate the presence of glaucoma, ophthalmologists may determine the cup and disc areas and diagnose glaucoma using a vertical optic cup-to-disc (C/D) ratio and a rim-to-disc (R/D) ratio. Previously we proposed a method to determine cup edge by analyzing a vertical profile of pixel values, but this method provided a cup edge smaller than that of an ophthalmologist. This paper describes an improved method using the locations of the blood vessel bends. The blood vessels were detected by a concentration feature determined from the density gradient. The blood vessel bends were detected by tracking the blood vessels from the disc edge to the primary cup edge, which was determined by our previous method. Lastly, the vertical C/D ratio and the R/D ratio were calculated. Using forty-four images, including 32 glaucoma images, the AUCs of both the vertical C/D ratio and R/D ratio by this proposed method were 0.966 and 0.936, respectively.

  16. Improved registration of DCE-MR images of the liver using a prior segmentation of the region of interest

    NASA Astrophysics Data System (ADS)

    Zhang, Tian; Li, Zhang; Runge, Jurgen H.; Lavini, Cristina; Stoker, Jaap; van Gulik, Thomas; van Vliet, Lucas J.; Vos, Frans M.

    2016-03-01

    In Dynamic Contrast-Enhanced MRI (DCE-MRI) of the liver, a series of images is acquired over a period of 20 minutes. Due to the patient's breathing, the liver is subject to a substantial displacement between acquisitions. Furthermore, due to its location in the abdomen, the liver also undergoes marked deformation. The large deformations combined with variation in image contrast make accurate liver registration challenging. We present a registration framework that incorporates a liver segmentation to improve the registration accuracy. The segmented liver serves as region-of-interest to our in-house developed registration method called ALOST (autocorrelation of local image structure). ALOST is a continuous optimization method that uses local phase features to overcome space-variant intensity distortions. The proposed framework can confine the solution field to the liver and allow for ALOST to obtain a more accurate solution. For the segmentation part, we use a level-set method to delineate the liver in a so-called contrast enhancement map. This map is obtained by computing the difference between the last and registered first volume from the DCE series. Subsequently, we slightly dilate the segmentation, and apply it as the mask to the other DCE-MRI volumes during registration. It is shown that the registration result becomes more accurate compared with the original ALOST approach.

  17. Totally laparoscopic liver resection for colorectal metastasis located in Segment 7 in a patient with situs inversus totalis.

    PubMed

    Giuliani, Antonio; Bianco, Paolo; Guerra, Germano; Rocca, Aldo; Calise, Fulvio

    2017-01-17

    Situs inversus totalis (SIT) is a congenital condition consisting of a mirror image of transposition of the abdominal and thoracic organs occurring in about 1:5000 to 1:10 000 adults. We report on a 60-year-old male with a single colorectal liver metastasis in the Segment 7. The patients underwent a totally laparoscopic sub-segmentectomy. Intraoperative approach on a reverse posterior segment was difficult because of left-sided position of the liver. Postoperative course was uneventful and the patient was discharged after 5 days. To our knowledge, only a few cases of open liver resections in patients with SIT have been published. This is, therefore, the first case of laparoscopic liver resection for colorectal liver metastasis in a patient with SIT. We provide the readers with useful tips to perform minimally invasive liver surgery in such patients. Published by Oxford University Press and JSCR Publishing Ltd. All rights reserved. © The Author 2017.

  18. Safety and Efficacy Assessment of Flow Redistribution by Occlusion of Intrahepatic Vessels Prior to Radioembolization in the Treatment of Liver Tumors

    SciTech Connect

    Bilbao, Jose I.; Garrastachu, Puy; Herraiz, Maria J.; Rodriguez, Macarena; Inarrairaegui, Mercedes; Rodriguez, Javier; Hernandez, Carmen; Cuesta, Antonio Martinez de la; Arbizu, Javier; Sangro, Bruno

    2010-06-15

    We evaluated the feasibility, safety, and efficacy of radioembolization (administered from one or two vascular points) after the redistribution of arterial blood flow in the liver in patients with hepatic neoplasms and arterial anatomic peculiarities (AAP). Twenty-four patients with liver neoplasms and AAP (graded according to Michel's classification) were included in the study. During pretreatment angiographic planning, all extrahepatic vessels that could feed the tumor were embolized and the intrahepatic vessels occluded in order to redistribute blood flow. The distribution of microspheres was initially assessed by using technetium-99m-labeled macroaggregated albumin ({sup 99m}Tc-MAA) from one of two vascular points before the administration of yttrium-90 ({sup 90}Y)-radiolabeled resin microspheres. Perfusion of lesions situated in the redistributed segments (L-RS) and nonredistributed segments (L-NRS) were compared by assessing the distribution of {sup 99m}Tc-MAA by SPECT/CT. Perfusion was graded as normal, reduced, or absent. {sup 90}Y resin microspheres were then injected from the same arterial sites as {sup 99m}Tc-MAA and the tumor response recorded 3 months later. The tumor response in L-RS was compared with that in L-NRS and graded as better, similar, or worse. Among 11 patients with type I AAP in whom mainly vessels in segments I-III or IV were occluded, perfusion of L-RS was graded as similar (n = 7) or reduced (n = 4). Among the remaining 13 patients with AAP types III (n = 3), V (n = 4), VIII (n = 3), and others (n = 3) in which aberrant arteries were occluded, perfusion of L-RS was graded as similar (n = 9), reduced (n = 3), or absent (n = 1). Overall, {sup 99m}Tc-MAA was present in the L-RS of 95.8% patients and the distribution of {sup 99m}Tc-MAA in L-RS and L-NRS were graded as similar in 66.6% of patients. Compared with lesions in the L-NRS, tumor response in L-RS was similar in 23 cases and worse in 1 case. No complications were recorded after the

  19. Adaptive Mesh Expansion Model (AMEM) for Liver Segmentation from CT Image

    PubMed Central

    Wang, Xuehu; Yang, Jian; Ai, Danni; Zheng, Yongchang; Tang, Songyuan; Wang, Yongtian

    2015-01-01

    This study proposes a novel adaptive mesh expansion model (AMEM) for liver segmentation from computed tomography images. The virtual deformable simplex model (DSM) is introduced to represent the mesh, in which the motion of each vertex can be easily manipulated. The balloon, edge, and gradient forces are combined with the binary image to construct the external force of the deformable model, which can rapidly drive the DSM to approach the target liver boundaries. Moreover, tangential and normal forces are combined with the gradient image to control the internal force, such that the DSM degree of smoothness can be precisely controlled. The triangular facet of the DSM is adaptively decomposed into smaller triangular components, which can significantly improve the segmentation accuracy of the irregularly sharp corners of the liver. The proposed method is evaluated on the basis of different criteria applied to 10 clinical data sets. Experiments demonstrate that the proposed AMEM algorithm is effective and robust and thus outperforms six other up-to-date algorithms. Moreover, AMEM can achieve a mean overlap error of 6.8% and a mean volume difference of 2.7%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 1.3 mm and 2.7 mm, respectively. PMID:25769030

  20. A strategy for blood vessels segmentation based on the threshold which combines statistical and scale space filter. Application to the study of angiogenesis.

    PubMed

    Rodríguez, Roberto

    2006-04-01

    This paper presents a strategy for segmenting blood vessels based on the threshold, which combines statistics and scale space filter. By incorporating statistical information, the strategy is capable of reducing over-segmentation. We propose a two-stage strategy which involves: (1) optimal selection of window size and (2) optimal selection of scale. We compared our strategy to two commonly used thresholding techniques. Experimental results showed that our method is much more robust and accurate. Our strategy suggested a modification to Otsu's method. In this application the important information to be extracted from images is only the number of blood vessels present in the images. The proposed segmentation technique is tested on manual segmentation, where segmentation errors less than 3% were observed. The work presented in this paper is a part of a global image analysis process. Therefore, these images will be subject to a further morphometrical analysis in order to diagnose and predict automatically malign tumors.

  1. The removal and segmentation of the Yankee Rowe reactor vessel internals

    SciTech Connect

    Child, C.; McGough, M.; Smith, G.

    1995-12-31

    A major element of the reactor decommissioning of the Rowe Yankee reactor was the segmentation and packaging of the reactor internals. PCI Energy Services, specializing in remote cutting, machining, and welding, performed this work under contract to Yankee Atomic Electric Company. Removal techniques are described.

  2. Automatic vessel segmentation in wide-field retina images of infants with retinopathy of prematurity.

    PubMed

    Poletti, Enea; Fiorin, Diego; Grisan, Enrico; Ruggeri, Alfredo

    2011-01-01

    The earliest signs of Retinopathy of Prematurity (ROP) are tortuosity and dilation of retinal vessels. Such vascular changes are considered of primary importance for the diagnosis and the follow-up of the disease. However, a widely accepted computerized system for their quantitative measurement is still missing. Images taken from a preterm baby's eye are often low-contrast, noisy, and blurred. Algorithms that have been successfully applied to analyze adult retinal images do not work well in ROP images. We propose here a novel method for the automatic extraction of vessel centerline in wide-field ROP retinal images, based on a sparse tracking scheme. After a set of seed points is identified all over the image, vessels are traced by connecting those seeds by means of minimum cost paths, whose weights depend on similarity features and alignment evaluated by a custom line operator. The performance of the method was assessed on a dataset of 20 images acquired with the RetCam fundus camera. A sensitivity of 0.78 and a false detection rate of 0.15 were obtained with respect to manual ground truth reference.

  3. Three-Dimensional Quantitative Evaluation of the Segmental Functional Reserve in the Cirrhotic Liver Using Multi-Modality Imaging

    PubMed Central

    Xiang, Canhong; Chen, Yingmao; Shao, Mingzhe; Li, Can; Huang, Xin; Gong, Lei; Li, Ang; Duan, Weidong; Zhang, Aiqun; Dong, Jiahong

    2016-01-01

    Abstract To quantitatively evaluate the regional functional reserve in the cirrhotic liver and to seek related index that reflects diminished segmental liver function. A 3D system for quantitative evaluation of the liver was used to fuse technetium-99m galactosyl human serum albumin single-photon emission computed tomography and computed tomography images from 20 patients with cirrhotic liver and hepatocellular carcinoma. A set of parameters reflecting liver function including morphological liver volume, functional liver volume, functional liver density (FLD), and the drug absorption rate constant for hepatic cells (GSA-K) was calculated. Differences in FLD and GSA-K in intrahepatic segments were compared in patients with a tumor embolus (Group Y) and those without such an embolus (Group N) in the right portal vein. Differences in FLD and GSA-K in tumor-bearing (T+ group) and tumor-free (T− group) segments in patients with no tumor embolus (Group N) were also compared. Eleven living donor liver transplantation donor served as the control group. The FLD of the liver as a whole was significantly lower in patients with cirrhosis than in the control group (0.53 ± 0.13 vs 0.68 ± 0.10, P = 0.010). The FLD in segments of the right hemiliver was significantly lower than that in segments of the left hemiliver in Group Y (0.31 ± 0.21 vs 0.58 ± 0.12, P = 0.002) but not in Group N (0.60 ± 0.19 vs 0.55 ± 0.13, P = 0.294). FLD was 0.45 ± 0.17 in the T+ group and 0.60 ± 0.08 in the T− group (P = 0.008). Differences in GSA-K in intrahepatic segments were not significant. In the control group, differences in FLD and GSA-K in intrahepatic segments were not significant. The segmental liver functional reserve can be quantitatively calculated. FLD, but not GSA-K, is an index that reflects diminished regional liver function caused by portal flow obstruction or tumor compression. PMID:26945357

  4. Computerized segmentation of liver in hepatic CT and MRI by means of level-set geodesic active contouring.

    PubMed

    Suzuki, Kenji; Huynh, Hieu Trung; Liu, Yipeng; Calabrese, Dominic; Zhou, Karen; Oto, Aytekin; Hori, Masatoshi

    2013-01-01

    Computerized liver volumetry has been studied, because the current "gold-standard" manual volumetry is subjective and very time-consuming. Liver volumetry is done in either CT or MRI. A number of researchers have developed computerized liver segmentation in CT, but there are fewer studies on ones for MRI. Our purpose in this study was to develop a general framework for liver segmentation in both CT and MRI. Our scheme consisted of 1) an anisotropic diffusion filter to reduce noise while preserving liver structures, 2) a scale-specific gradient magnitude filter to enhance liver boundaries, 3) a fast-marching algorithm to roughly determine liver boundaries, and 4) a geodesic-active-contour model coupled with a level-set algorithm to refine the initial boundaries. Our CT database contained hepatic CT scans of 18 liver donors obtained under a liver transplant protocol. Our MRI database contains 23 patients with 1.5T MRI scanners. To establish "gold-standard" liver volumes, radiologists manually traced the contour of the liver on each CT or MR slice. We compared our computer volumetry with "gold-standard" manual volumetry. Computer volumetry in CT and MRI reached excellent agreement with manual volumetry (intra-class correlation coefficient = 0.94 and 0.98, respectively). Average user time for computer volumetry in CT and MRI was 0.57 ± 0.06 and 1.0 ± 0.13 min. per case, respectively, whereas those for manual volumetry were 39.4 ± 5.5 and 24.0 ± 4.4 min. per case, respectively, with statistically significant difference (p < .05). Our computerized liver segmentation framework provides an efficient and accurate way of measuring liver volumes in both CT and MRI.

  5. Label-free optical lymphangiography: development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters.

    PubMed

    Yousefi, Siavash; Qin, Jia; Zhi, Zhongwei; Wang, Ruikang K

    2013-08-01

    Lymphatic vessels are a part of the circulatory system that collect plasma and other substances that have leaked from the capillaries into interstitial fluid (lymph) and transport lymph back to the circulatory system. Since lymph is transparent, lymphatic vessels appear as dark hallow vessel-like regions in optical coherence tomography (OCT) cross sectional images. We propose an automatic method to segment lymphatic vessel lumen from OCT structural cross sections using eigenvalues of Hessian filters. Compared to the existing method based on intensity threshold, Hessian filters are more selective on vessel shape and less sensitive to intensity variations and noise. Using this segmentation technique along with optical micro-angiography allows label-free noninvasive simultaneous visualization of blood and lymphatic vessels in vivo. Lymphatic vessels play an important role in cancer, immune system response, inflammatory disease, wound healing and tissue regeneration. Development of imaging techniques and visualization tools for lymphatic vessels is valuable in understanding the mechanisms and studying therapeutic methods in related disease and tissue response.

  6. Label-free optical lymphangiography: development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters

    NASA Astrophysics Data System (ADS)

    Yousefi, Siavash; Qin, Jia; Zhi, Zhongwei; Wang, Ruikang K.

    2013-08-01

    Lymphatic vessels are a part of the circulatory system that collect plasma and other substances that have leaked from the capillaries into interstitial fluid (lymph) and transport lymph back to the circulatory system. Since lymph is transparent, lymphatic vessels appear as dark hallow vessel-like regions in optical coherence tomography (OCT) cross sectional images. We propose an automatic method to segment lymphatic vessel lumen from OCT structural cross sections using eigenvalues of Hessian filters. Compared to the existing method based on intensity threshold, Hessian filters are more selective on vessel shape and less sensitive to intensity variations and noise. Using this segmentation technique along with optical micro-angiography allows label-free noninvasive simultaneous visualization of blood and lymphatic vessels in vivo. Lymphatic vessels play an important role in cancer, immune system response, inflammatory disease, wound healing and tissue regeneration. Development of imaging techniques and visualization tools for lymphatic vessels is valuable in understanding the mechanisms and studying therapeutic methods in related disease and tissue response.

  7. Label-free optical lymphangiography: development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters

    PubMed Central

    Yousefi, Siavash; Qin, Jia; Zhi, Zhongwei

    2013-01-01

    Abstract. Lymphatic vessels are a part of the circulatory system that collect plasma and other substances that have leaked from the capillaries into interstitial fluid (lymph) and transport lymph back to the circulatory system. Since lymph is transparent, lymphatic vessels appear as dark hallow vessel-like regions in optical coherence tomography (OCT) cross sectional images. We propose an automatic method to segment lymphatic vessel lumen from OCT structural cross sections using eigenvalues of Hessian filters. Compared to the existing method based on intensity threshold, Hessian filters are more selective on vessel shape and less sensitive to intensity variations and noise. Using this segmentation technique along with optical micro-angiography allows label-free noninvasive simultaneous visualization of blood and lymphatic vessels in vivo. Lymphatic vessels play an important role in cancer, immune system response, inflammatory disease, wound healing and tissue regeneration. Development of imaging techniques and visualization tools for lymphatic vessels is valuable in understanding the mechanisms and studying therapeutic methods in related disease and tissue response. PMID:23922124

  8. Volume change of segments II and III of the liver after gastrectomy in patients with gastric cancer

    PubMed Central

    Ozutemiz, Can; Obuz, Funda; Taylan, Abdullah; Atila, Koray; Bora, Seymen; Ellidokuz, Hulya

    2016-01-01

    PURPOSE We aimed to evaluate the relationship between gastrectomy and the volume of liver segments II and III in patients with gastric cancer. METHODS Computed tomography images of 54 patients who underwent curative gastrectomy for gastric adenocarcinoma were retrospectively evaluated by two blinded observers. Volumes of the total liver and segments II and III were measured. The difference between preoperative and postoperative volume measurements was compared. RESULTS Total liver volumes measured by both observers in the preoperative and postoperative scans were similar (P > 0.05). High correlation was found between both observers (preoperative r=0.99; postoperative r=0.98). Total liver volumes showed a mean reduction of 13.4% after gastrectomy (P = 0.977). The mean volume of segments II and III showed similar decrease in measurements of both observers (38.4% vs. 36.4%, P = 0.363); the correlation between the observers were high (preoperative r=0.97, P < 0.001; postoperative r=0.99, P < 0.001). Volume decrease in the rest of the liver was not different between the observers (8.2% vs. 9.1%, P = 0.388). Time had poor correlation with volume change of segments II and III and the total liver for each observer (observer 1, rseg2/3=0.32, rtotal=0.13; observer 2, rseg2/3=0.37, rtotal=0.16). CONCLUSION Segments II and III of the liver showed significant atrophy compared with the rest of the liver and the total liver after gastrectomy. Volume reduction had poor correlation with time. PMID:26899148

  9. Investigation of source-detector separation optimization for an implantable perfusion and oxygenation sensor for liver blood vessels

    SciTech Connect

    Baba, Justin S; Akl, Tony; Cote, Gerard L.; Wilson, Mark A.; Ericson, Milton Nance

    2011-01-01

    An implanted system is being developed to monitor transplanted liver health during the critical 7-10 day period posttransplantation. The unit will monitor organ perfusion and oxygen consumption using optically-based probes placed on both the inflow and outflow blood vessels, and on the liver parenchymal surface. Sensing probes are based on a 3- wavelength LED source and a photodiode detector. Sample diffuse reflectance is measured at 735, 805, and 940 nm. To ascertain optimal source-to-photodetector spacing for perfusion measurement in blood vessels, an ex vivo study was conducted. In this work, a dye mixture simulating 80% blood oxygen saturation was developed and perfused through excised porcine arteries while collecting data for various preset probe source-to-photodetector spacings. The results from this study demonstrate a decrease in the optical signal with decreasing LED drive current and a reduction in perfusion index signal with increasing probe spacing. They also reveal a 2- to 4-mm optimal range for blood vessel perfusion probe source-to-photodetector spacing that allows for sufficient perfusion signal modulation depth with maximized signal to noise ratio (SNR). These findings are currently being applied to guide electronic configuration and probe placement for in vivo liver perfusion porcine model studies.

  10. Laparoscopic Liver Resection Using the Lateral Approach from Intercostal Ports in Segments VI, VII, and VIII.

    PubMed

    Inoue, Yoshihiro; Suzuki, Yusuke; Fujii, Kensuke; Kawaguchi, Nao; Ishii, Masatsugu; Masubuchi, Shinsuke; Yamamoto, Masashi; Hirokawa, Fumitoshi; Hayashi, Michihiro; Uchiyama, Kazuhisa

    2017-07-31

    Laparoscopic liver resection (LLR) has been developed as a minimally invasive surgery. However, challenges such as difficulty securing visibility and limited control of forceps make it difficult to complete LLR in hepatic segments VI, VII, and VIII. To overcome these challenges, we devised a surgical technique using intercostal ports. We termed this approach the lateral approach. This work describes our experience performing LLR using this approach and discusses the safety and effectiveness of this approach. Between April 2011 and December 2016, data from 91 patients who underwent LLR with or without the intercostal port at a single institution were retrospectively analyzed regarding surgical outcomes, safety, and utility. LLR was performed for 32 patients with the intercostal port and for 59 patients without the intercostal port. The conversion rates to open surgery with and without intercostal ports were 3.1 and 25.4% (P = 0.008). In hepatic segments VII and VIII, the rates of conversion to open surgery were significantly lower for cases involving intercostal ports (6.7 vs. 42.9 and 0 vs. 38.9%; P = 0.035 and 0026, respectively); however, there were no differences in hepatic segment VI (0 vs. 7.4%; P = 0.563). There were no differences in operative time, blood loss volume, surgical margin, curative resection rate, or postoperative complication rate for LLR in all segments (VI, VII, and VIII). No adverse events due to placement of the intercostal port were observed in this set of patients. LLR using the lateral approach and intercostal ports for hepatic segments VII and VIII resulted in a significant decrease in conversion rates to open surgery.

  11. A modified Seeded Region Growing algorithm for vessel segmentation in breast MRI images for investigating the nature of potential lesions

    NASA Astrophysics Data System (ADS)

    Glotsos, D.; Vassiou, K.; Kostopoulos, S.; Lavdas, El; Kalatzis, I.; Asvestas, P.; Arvanitis, D. L.; Fezoulidis, I. V.; Cavouras, D.

    2014-03-01

    The role of Magnetic Resonance Imaging (MRI) as an alternative protocol for screening of breast cancer has been intensively investigated during the past decade. Preliminary research results have indicated that gadolinium-agent administrative MRI scans may reveal the nature of breast lesions by analyzing the contrast-agent's uptake time. In this study, we attempt to deduce the same conclusion, however, from a different perspective by investigating, using image processing, the vascular network of the breast at two different time intervals following the administration of gadolinium. Twenty cases obtained from a 3.0-T MRI system (SIGNA HDx; GE Healthcare) were included in the study. A new modification of the Seeded Region Growing (SRG) algorithm was used to segment vessels from surrounding background. Delineated vessels were investigated by means of their topology, morphology and texture. Results have shown that it is possible to estimate the nature of the lesions with approximately 94.4% accuracy, thus, it may be claimed that the breast vascular network does encodes useful, patterned, information, which can be used for characterizing breast lesions.

  12. Intercostal Trocars Enable Easier Laparoscopic Resection of Liver Tumors in Segments 7 and 8.

    PubMed

    Hirokawa, Fumitoshi; Hayashi, Michihiro; Asakuma, Mitsuhiro; Shimizu, Tetsunosuke; Inoue, Yoshihiro; Uchiyama, Kazuhisa

    2017-05-01

    Laparoscopic resection of posterosuperior (PS) tumors of the liver is more difficult than that of anterolateral (AL) tumors, owing to the narrow surgical field in the PS location. In this retrospective cohort study, our aim was to determine if port insertion through the intercostal space would lead to improved outcomes for laparoscopic resection of tumors in PS liver segments 7 and 8. Between January 2006 and December 2015, 153 patients underwent laparoscopic resection of solitary liver tumors at Osaka Medical College Hospital. Of these, 107 patients had AL lesions, and 46 had PS lesions. Of the 46 patients with a PS lesion, 23 underwent an abdominal-only approach, and 23 underwent the intercostal trocar approach. Multivariate analyses were performed to investigate outcomes. Conventional abdominal-only laparoscopic resection of PS liver tumors resulted in prolonged surgical time (P = 0.031), increased bleeding (P = 0.012), and a higher open conversion rate (P = 0.022) compared with AL tumors. Among patients with PS tumors, the open conversion rate was significantly higher for those treated with the abdominal-only approach than with the intercostal trocar approach (P = 0.047). Appropriate surgical margins were obtained equally using the intercostal trocar approach (P = 0.648). There was no significant difference in occurrence of complications between the abdominal-only group and the intercostal trocar group. Using the intercostal trocar approach for PS liver lesions is a safe and effective method, which significantly reduced the open conversion rate compared with the conventional abdominal-only approach.

  13. Time-Dependent Impact of Irreversible Electroporation on Pancreas, Liver, Blood Vessels and Nerves: A Systematic Review of Experimental Studies

    PubMed Central

    Agnass, P.; Crezee, J.; Dijk, F.; Verheij, J.; van Gulik, T. M.; Meijerink, M. R.; Vroomen, L. G.; van Lienden, K. P.; Besselink, M. G.

    2016-01-01

    Introduction Irreversible electroporation (IRE) is a novel ablation technique in the treatment of unresectable cancer. The non-thermal mechanism is thought to cause mostly apoptosis compared to necrosis in thermal techniques. Both in experimental and clinical studies, a waiting time between ablation and tissue or imaging analysis to allow for cell death through apoptosis, is often reported. However, the dynamics of the IRE effect over time remain unknown. Therefore, this study aims to summarize these effects in relation to the time between treatment and evaluation. Methods A systematic search was performed in Pubmed, Embase and the Cochrane Library for original articles using IRE on pancreas, liver or surrounding structures in animal or human studies. Data on pathology and time between IRE and evaluation were extracted. Results Of 2602 screened studies, 36 could be included, regarding IRE in liver (n = 24), pancreas (n = 4), blood vessels (n = 4) and nerves (n = 4) in over 440 animals (pig, rat, goat and rabbit). No eligible human studies were found. In liver and pancreas, the first signs of apoptosis and haemorrhage were observed 1–2 hours after treatment, and remained visible until 24 hours in liver and 7 days in pancreas after which the damaged tissue was replaced by fibrosis. In solitary blood vessels, the tunica media, intima and lumen remained unchanged for 24 hours. After 7 days, inflammation, fibrosis and loss of smooth muscle cells were demonstrated, which persisted until 35 days. In nerves, the median time until demonstrable histological changes was 7 days. Conclusions Tissue damage after IRE is a dynamic process with remarkable time differences between tissues in animals. Whereas pancreas and liver showed the first damages after 1–2 hours, this took 24 hours in blood vessels and 7 days in nerves. PMID:27870918

  14. Time-Dependent Impact of Irreversible Electroporation on Pancreas, Liver, Blood Vessels and Nerves: A Systematic Review of Experimental Studies.

    PubMed

    Vogel, J A; van Veldhuisen, E; Agnass, P; Crezee, J; Dijk, F; Verheij, J; van Gulik, T M; Meijerink, M R; Vroomen, L G; van Lienden, K P; Besselink, M G

    2016-01-01

    Irreversible electroporation (IRE) is a novel ablation technique in the treatment of unresectable cancer. The non-thermal mechanism is thought to cause mostly apoptosis compared to necrosis in thermal techniques. Both in experimental and clinical studies, a waiting time between ablation and tissue or imaging analysis to allow for cell death through apoptosis, is often reported. However, the dynamics of the IRE effect over time remain unknown. Therefore, this study aims to summarize these effects in relation to the time between treatment and evaluation. A systematic search was performed in Pubmed, Embase and the Cochrane Library for original articles using IRE on pancreas, liver or surrounding structures in animal or human studies. Data on pathology and time between IRE and evaluation were extracted. Of 2602 screened studies, 36 could be included, regarding IRE in liver (n = 24), pancreas (n = 4), blood vessels (n = 4) and nerves (n = 4) in over 440 animals (pig, rat, goat and rabbit). No eligible human studies were found. In liver and pancreas, the first signs of apoptosis and haemorrhage were observed 1-2 hours after treatment, and remained visible until 24 hours in liver and 7 days in pancreas after which the damaged tissue was replaced by fibrosis. In solitary blood vessels, the tunica media, intima and lumen remained unchanged for 24 hours. After 7 days, inflammation, fibrosis and loss of smooth muscle cells were demonstrated, which persisted until 35 days. In nerves, the median time until demonstrable histological changes was 7 days. Tissue damage after IRE is a dynamic process with remarkable time differences between tissues in animals. Whereas pancreas and liver showed the first damages after 1-2 hours, this took 24 hours in blood vessels and 7 days in nerves.

  15. A region-appearance-based adaptive variational model for 3D liver segmentation

    SciTech Connect

    Peng, Jialin; Dong, Fangfang; Chen, Yunmei; Kong, Dexing

    2014-04-15

    Purpose: Liver segmentation from computed tomography images is a challenging task owing to pixel intensity overlapping, ambiguous edges, and complex backgrounds. The authors address this problem with a novel active surface scheme, which minimizes an energy functional combining both edge- and region-based information. Methods: In this semiautomatic method, the evolving surface is principally attracted to strong edges but is facilitated by the region-based information where edge information is missing. As avoiding oversegmentation is the primary challenge, the authors take into account multiple features and appearance context information. Discriminative cues, such as multilayer consecutiveness and local organ deformation are also implicitly incorporated. Case-specific intensity and appearance constraints are included to cope with the typically large appearance variations over multiple images. Spatially adaptive balancing weights are employed to handle the nonuniformity of image features. Results: Comparisons and validations on difficult cases showed that the authors’ model can effectively discriminate the liver from adhering background tissues. Boundaries weak in gradient or with no local evidence (e.g., small edge gaps or parts with similar intensity to the background) were delineated without additional user constraint. With an average surface distance of 0.9 mm and an average volume overlap of 93.9% on the MICCAI data set, the authors’ model outperformed most state-of-the-art methods. Validations on eight volumes with different initial conditions had segmentation score variances mostly less than unity. Conclusions: The proposed model can efficiently delineate ambiguous liver edges from complex tissue backgrounds with reproducibility. Quantitative validations and comparative results demonstrate the accuracy and efficacy of the model.

  16. Diploic vessels and computed tomography: Segmentation and comparison in modern humans and fossil hominids.

    PubMed

    Rangel de Lázaro, Gizéh; de la Cuétara, José Manuel; Píšová, Hana; Lorenzo, Carlos; Bruner, Emiliano

    2016-02-01

    The diploic channels appear to be more developed in humans than in nonhuman primates, suggesting they may be relevant in evolutionary biology. This study is aimed at providing a segmentation procedure for diploic channels and CT analysis, a quantitative description of their variation in modern humans, and paleoanthropological case-studies. CT data were used for the 2D and 3D visualization, rendering, and measure, of diploic channels in modern and fossil hominids. We analyzed 20 modern human skulls and three Neanderthals. The effect of different resolution factors was evaluated. A specific protocol was designed to segment the vascular network and localize the main branches, reducing the noise of the cancellous bone. We provide a quantitative description of the frontal, parietal, and occipital diploic networks in modern humans and in three Neanderthals. There is a correlation in the degree of vascularization among the different vault areas. No side differences can be detected. The diploic network is commonly connected with the meningeal artery at the temporal fossa, with the emissary veins at the occipital bone, and with the venous sinuses at the parieto-occipital areas. The channels are more developed in the parietal areas. The three Neanderthals show a vascular development, which is in the lower range of the modern human variation. Modern humans display a large variation in their morphological patterns, being the parietal area the most vascularized. The pattern of the diploic channels may be relevant in anthropology, medicine, and paleontology, taking into account their possible involvement in thermoregulation. © 2015 Wiley Periodicals, Inc.

  17. Computer-aided liver volumetry: performance of a fully-automated, prototype post-processing solution for whole-organ and lobar segmentation based on MDCT imaging.

    PubMed

    Fananapazir, Ghaneh; Bashir, Mustafa R; Marin, Daniele; Boll, Daniel T

    2015-06-01

    To evaluate the performance of a prototype, fully-automated post-processing solution for whole-liver and lobar segmentation based on MDCT datasets. A polymer liver phantom was used to assess accuracy of post-processing applications comparing phantom volumes determined via Archimedes' principle with MDCT segmented datasets. For the IRB-approved, HIPAA-compliant study, 25 patients were enrolled. Volumetry performance compared the manual approach with the automated prototype, assessing intraobserver variability, and interclass correlation for whole-organ and lobar segmentation using ANOVA comparison. Fidelity of segmentation was evaluated qualitatively. Phantom volume was 1581.0 ± 44.7 mL, manually segmented datasets estimated 1628.0 ± 47.8 mL, representing a mean overestimation of 3.0%, automatically segmented datasets estimated 1601.9 ± 0 mL, representing a mean overestimation of 1.3%. Whole-liver and segmental volumetry demonstrated no significant intraobserver variability for neither manual nor automated measurements. For whole-liver volumetry, automated measurement repetitions resulted in identical values; reproducible whole-organ volumetry was also achieved with manual segmentation, p(ANOVA) 0.98. For lobar volumetry, automated segmentation improved reproducibility over manual approach, without significant measurement differences for either methodology, p(ANOVA) 0.95-0.99. Whole-organ and lobar segmentation results from manual and automated segmentation showed no significant differences, p(ANOVA) 0.96-1.00. Assessment of segmentation fidelity found that segments I-IV/VI showed greater segmentation inaccuracies compared to the remaining right hepatic lobe segments. Automated whole-liver segmentation showed non-inferiority of fully-automated whole-liver segmentation compared to manual approaches with improved reproducibility and post-processing duration; automated dual-seed lobar segmentation showed slight tendencies for underestimating the right hepatic lobe

  18. Automatic detection and segmentation of vascular structures in dermoscopy images using a novel vesselness measure based on pixel redness and tubularness

    NASA Astrophysics Data System (ADS)

    Kharazmi, Pegah; Lui, Harvey; Stoecker, William V.; Lee, Tim

    2015-03-01

    Vascular structures are one of the most important features in the diagnosis and assessment of skin disorders. The presence and clinical appearance of vascular structures in skin lesions is a discriminating factor among different skin diseases. In this paper, we address the problem of segmentation of vascular patterns in dermoscopy images. Our proposed method is composed of three parts. First, based on biological properties of human skin, we decompose the skin to melanin and hemoglobin component using independent component analysis of skin color images. The relative quantities and pure color densities of each component were then estimated. Subsequently, we obtain three reference vectors of the mean RGB values for normal skin, pigmented skin and blood vessels from the hemoglobin component by averaging over 100000 pixels of each group outlined by an expert. Based on the Euclidean distance thresholding, we generate a mask image that extracts the red regions of the skin. Finally, Frangi measure was applied to the extracted red areas to segment the tubular structures. Finally, Otsu's thresholding was applied to segment the vascular structures and get a binary vessel mask image. The algorithm was implemented on a set of 50 dermoscopy images. In order to evaluate the performance of our method, we have artificially extended some of the existing vessels in our dermoscopy data set and evaluated the performance of the algorithm to segment the newly added vessel pixels. A sensitivity of 95% and specificity of 87% were achieved.

  19. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

    SciTech Connect

    He, Baochun; Huang, Cheng; Zhou, Shoujun; Hu, Qingmao; Jia, Fucang; Sharp, Gregory; Fang, Chihua; Fan, Yingfang

    2016-05-15

    Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods—3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration—are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach

  20. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.

    PubMed

    He, Baochun; Huang, Cheng; Sharp, Gregory; Zhou, Shoujun; Hu, Qingmao; Fang, Chihua; Fan, Yingfang; Jia, Fucang

    2016-05-01

    A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence. The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. The proposed automatic approach achieves robust, accurate, and fast liver

  1. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features.

    PubMed

    Marin, Diego; Aquino, Arturo; Gegundez-Arias, Manuel Emilio; Bravo, José Manuel

    2011-01-01

    This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.

  2. Robust Adaptive 3-D Segmentation of Vessel Laminae From Fluorescence Confocal Microscope Images and Parallel GPU Implementation

    PubMed Central

    Narayanaswamy, Arunachalam; Dwarakapuram, Saritha; Bjornsson, Christopher S.; Cutler, Barbara M.; Shain, William

    2010-01-01

    This paper presents robust 3-D algorithms to segment vasculature that is imaged by labeling laminae, rather than the lumenal volume. The signal is weak, sparse, noisy, nonuniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding and Hessian filtering based methods are not effective. The structure deviates from a tubular geometry, so tracing algorithms are not effective. We propose a four step approach. The first step detects candidate voxels using a robust hypothesis test based on a model that assumes Poisson noise and locally planar geometry. The second step performs an adaptive region growth to extract weakly labeled and fine vessels while rejecting spectral artifacts. To enable interactive visualization and estimation of features such as statistical confidence, local curvature, local thickness, and local normal, we perform the third step. In the third step, we construct an accurate mesh representation using marching tetrahedra, volume-preserving smoothing, and adaptive decimation algorithms. To enable topological analysis and efficient validation, we describe a method to estimate vessel centerlines using a ray casting and vote accumulation algorithm which forms the final step of our algorithm. Our algorithm lends itself to parallel processing, and yielded an 8× speedup on a graphics processor (GPU). On synthetic data, our meshes had average error per face (EPF) values of (0.1–1.6) voxels per mesh face for peak signal-to-noise ratios from (110–28 dB). Separately, the error from decimating the mesh to less than 1% of its original size, the EPF was less than 1 voxel/face. When validated on real datasets, the average recall and precision values were found to be 94.66% and 94.84%, respectively. PMID:20199906

  3. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours

    NASA Astrophysics Data System (ADS)

    Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

    2017-01-01

    Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to

  4. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours.

    PubMed

    Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

    2017-01-07

    Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to

  5. A robust and accurate approach to automatic blood vessel detection and segmentation from angiography x-ray images using multistage random forests

    NASA Astrophysics Data System (ADS)

    Gupta, Vipin; Kale, Amit; Sundar, Hari

    2012-03-01

    In this paper we propose a novel approach based on multi-stage random forests to address problems faced by traditional vessel segmentation algorithms on account of image artifacts such as stitches organ shadows etc.. Our approach consists of collecting a very large number of training data consisting of positive and negative examples of valid seed points. The method makes use of a 14x14 window around a putative seed point. For this window three types of feature vectors are computed viz. vesselness, eigenvalue and a novel effective margin feature. A random forest RF is trained for each of the feature vectors. At run time the three RFs are applied in succession to a putative seed point generated by a naiive vessel detection algorithm based on vesselness. Our approach will prune this set of putative seed points to correctly identify true seed points thereby avoiding false positives. We demonstrate the effectiveness of our algorithm on a large dataset of angio images.

  6. Pre-Operative Image-based Segmentation of the Cranial Nerves and Blood Vessels in Microvascular Decompression: Can we Prevent Unnecessary Explorations?

    PubMed Central

    Dolati, P; Golby, A; Eichberg, D; Abolfotoh, M; Dunn, IF; Mukundan, S; Hulou, MM; Al-Mefty, O

    2016-01-01

    Objectives This study was conducted to validate the accuracy of image-based pre-operative segmentation using the gold standard endoscopic and microscopic findings for localization and pre-operative diagnosis of the offensive vessel. Patients and Methods Fourteen TN and 6 HS cases were randomly selected. All patients had 3T MRI, which included thin-sectioned 3D space T2, 3D Time of Flight and MPRAGE Sequences. Imaging sequences were loaded in BrainLab iPlanNet and fused. Individual segmentation of the affected cranial nerves and the compressing vascular structure was performed by a neurosurgeon, and the results were compared with the microscopic and endoscopic findings by two blinded neurosurgeons. For each case, at least three neurovascular landmarks were targeted. Each segmented neurovascular element was validated by manual placement of the navigation probe over each target, and errors of localization were measured in mm. Results All patients underwent retro-sigmoid craniotomy and MVD using both microscope and endoscope. Based on image segmentation, the compressing vessel was identified in all cases except one, which was also negative intraoperatively. Perfect correspondence was found between image-based segmentation and endoscopic and microscopic images and videos (Dice coefficient of 1). Measurement accuracy was 0.45+/-0.21 mm (mean +/-SD). Conclusion Image-based segmentation is a promising method for pre-operative identification and localization of offending blood vessels causing HFS and TN. Using this method may prevent some unnecessary explorations on especially atypical cases with no vascular contacts. However, negative pre-operative image segmentation may not preclude one from exploration in classic cases of TN or HFS. A multicenter study with larger number of cases is recommended. PMID:26476700

  7. A South Indian Cadaveric Study About the Relationship of Hepatic Segment of Inferior Vena Cava with the Liver

    PubMed Central

    Surendran, Sudarshan; Nelluri, Venu Madhav; Kumar, Naveen; Aithal, Ashwini P

    2016-01-01

    Introduction Inferior Vena Cava (IVC) is the largest vein of the body. It runs vertically upwards in the abdomen, behind the liver. Its course is very constant in relation to liver. However, the amount of liver parenchyma related to it can vary from person to person. The data regarding its course and relations may be very useful to radiologists and surgeons during surgical treatment procedures for Budd-Chiari syndrome, liver carcinoma, liver transplant, venous cannulations and many other clinical procedures. Aim Aim of this study was to document the incidence of straight and curved course of IVC in relation to liver and also to note the pattern in which the liver tissue was related to the IVC. Materials and Methods In the current study, 95 adult cadaveric livers were observed; specifically to study the course/direction of the hepatic segment of IVC in relation to the liver. The extent of liver tissue related to various aspects of IVC was also studied. The course of the IVC was classified as straight and curved; and the relationship of liver parenchyma to the IVC was classified into 6 categories. The data was expressed as percentage incidence. Results In 78.94% cases, the IVC had a straight course in relation to the liver; whereas in 21.06% cases, it had a left sided curve (concavity of the curve towards the caudate lobe) in its course. In 6.31% cases, IVC travelled in a tunnel, being encircled by the liver parenchyma all around; in 36.84% cases, it was covered by liver parenchyma on front and sides so that only posterior surface of IVC was visible; in 3.15% cases it was covered by liver tissue on front, sides and also partly on posterior aspect; in 50.52% of cases, its anterior surface, sides and left edge of the posterior surface was covered by liver tissue; and in 3.15% cases it was covered only from the front by the liver tissue. Conclusion The data being reported here might be useful for surgeons while planning and executing various hepatic surgeries and also

  8. Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.

    PubMed

    Pavliha, Denis; Mušič, Maja M; Serša, Gregor; Miklavčič, Damijan

    2013-01-01

    Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient's medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required.

  9. Inner and outer coronary vessel wall segmentation from CCTA using an active contour model with machine learning-based 3D voxel context-aware image force

    NASA Astrophysics Data System (ADS)

    Sivalingam, Udhayaraj; Wels, Michael; Rempfler, Markus; Grosskopf, Stefan; Suehling, Michael; Menze, Bjoern H.

    2016-03-01

    In this paper, we present a fully automated approach to coronary vessel segmentation, which involves calcification or soft plaque delineation in addition to accurate lumen delineation, from 3D Cardiac Computed Tomography Angiography data. Adequately virtualizing the coronary lumen plays a crucial role for simulating blood ow by means of fluid dynamics while additionally identifying the outer vessel wall in the case of arteriosclerosis is a prerequisite for further plaque compartment analysis. Our method is a hybrid approach complementing Active Contour Model-based segmentation with an external image force that relies on a Random Forest Regression model generated off-line. The regression model provides a strong estimate of the distance to the true vessel surface for every surface candidate point taking into account 3D wavelet-encoded contextual image features, which are aligned with the current surface hypothesis. The associated external image force is integrated in the objective function of the active contour model, such that the overall segmentation approach benefits from the advantages associated with snakes and from the ones associated with machine learning-based regression alike. This yields an integrated approach achieving competitive results on a publicly available benchmark data collection (Rotterdam segmentation challenge).

  10. Segments.

    ERIC Educational Resources Information Center

    Zemsky, Robert; Shaman, Susan; Shapiro, Daniel B.

    2001-01-01

    Presents a market taxonomy for higher education, including what it reveals about the structure of the market, the model's technical attributes, and its capacity to explain pricing behavior. Details the identification of the principle seams separating one market segment from another and how student aspirations help to organize the market, making…

  11. Stereotactic body radiation therapy in hepatocellular carcinoma: Optimal treatment strategies based on liver segmentation and functional hepatic reserve

    PubMed Central

    Wang, Po-Ming; Chung, Na-Na; Hsu, Wei-Chung; Chang, Feng-Ling; Jang, Chin-Jyh; Scorsetti, Marta

    2015-01-01

    Aim To discuss current dosage for stereotactic body radiation therapy (SBRT) in hepatocellular carcinoma (HCC) patients and suggest alternative treatment strategies according to liver segmentation as defined by the Couinaud classification. Background SBRT is a safe and effective alternative treatment for HCC patients who are unable to undergo liver ablation/resection. However, the SBRT fractionation schemes and treatment planning strategies are not well established. Materials and methods In this article, the latest developments and key findings from research studies exploring the efficacy of SBRT fractionation schemes for treatment of HCC are reviewed. Patients’ characteristics, fractionation schemes, treatment outcomes and toxicities were compiled. Special attention was focused on SBRT fractionation approaches that take into consideration liver segmentation according to the Couinaud classification and functional hepatic reserve based on Child–Pugh (CP) liver cirrhosis classification. Results The most common SBRT fractionation schemes for HCC were 3 × 10–20 Gy, 4–6 × 8–10 Gy, and 10 × 5–5.5 Gy. Based on previous SBRT studies, and in consideration of tumor size and CP classification, we proposed 3 × 15–25 Gy for patients with tumor size <3 cm and adequate liver reserve (CP-A score 5), 5 × 10–12 Gy for patients with tumor sizes between 3 and 5 cm or inadequate liver reserve (CP-A score 6), and 10 × 5–5.5 Gy for patients with tumor size >5 cm or CP-B score. Conclusions Treatment schemes in SBRT for HCC vary according to liver segmentation and functional hepatic reserve. Further prospective studies may be necessary to identify the optimal dose of SBRT for HCC. PMID:26696781

  12. A novel three-dimensional print of liver vessels and tumors in hepatectomy.

    PubMed

    Oshiro, Yukio; Mitani, Jun; Okada, Toshiyuki; Ohkohchi, Nobuhiro

    2017-04-01

    Creating a three-dimensional (3D)-printed liver model is costly, and the visibility of the inner structures is slightly hindered. We developed a novel structure that simultaneously solves both of these problems. The outer frames were set up along the liver surface. Our structure did not use the transparent loading material because this material increases the printing cost. Therefore, we were able to directly observe the inside of the structure. We performed hepatectomy using this novel 3D-printed liver model. Using this model, we were able to clearly simulate the resection line and safely perform the surgery. Our process was more cost effective, had a shorter production time, and improved the visibility than other processes. We developed a novel 3D-printed liver for hepatectomy, which made the procedure easier, reduced the production cost, and improved the visibility; this approach may be useful for future surgeries.

  13. Framework for quantitative evaluation of 3D vessel segmentation approaches using vascular phantoms in conjunction with 3D landmark localization and registration

    NASA Astrophysics Data System (ADS)

    Wörz, Stefan; Hoegen, Philipp; Liao, Wei; Müller-Eschner, Matthias; Kauczor, Hans-Ulrich; von Tengg-Kobligk, Hendrik; Rohr, Karl

    2016-03-01

    We introduce a framework for quantitative evaluation of 3D vessel segmentation approaches using vascular phantoms. Phantoms are designed using a CAD system and created with a 3D printer, and comprise realistic shapes including branches and pathologies such as abdominal aortic aneurysms (AAA). To transfer ground truth information to the 3D image coordinate system, we use a landmark-based registration scheme utilizing fiducial markers integrated in the phantom design. For accurate 3D localization of the markers we developed a novel 3D parametric intensity model that is directly fitted to the markers in the images. We also performed a quantitative evaluation of different vessel segmentation approaches for a phantom of an AAA.

  14. Automated detection of pulmonary embolism (PE) in computed tomographic pulmonary angiographic (CTPA) images: multiscale hierachical expectation-maximization segmentation of vessels and PEs

    NASA Astrophysics Data System (ADS)

    Zhou, Chuan; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Chughtai, Aamer; Patel, Smita; Cascade, Philip N.; Sahiner, Berkman; Wei, Jun; Ge, Jun; Kazerooni, Ella A.

    2007-03-01

    CT pulmonary angiography (CTPA) has been reported to be an effective means for clinical diagnosis of pulmonary embolism (PE). We are developing a computer-aided detection (CAD) system to assist radiologist in PE detection in CTPA images. 3D multiscale filters in combination with a newly designed response function derived from the eigenvalues of Hessian matrices is used to enhance vascular structures including the vessel bifurcations and suppress non-vessel structures such as the lymphoid tissues surrounding the vessels. A hierarchical EM estimation is then used to segment the vessels by extracting the high response voxels at each scale. The segmented vessels are pre-screened for suspicious PE areas using a second adaptive multiscale EM estimation. A rule-based false positive (FP) reduction method was designed to identify the true PEs based on the features of PE and vessels. 43 CTPA scans were used as an independent test set to evaluate the performance of PE detection. Experienced chest radiologists identified the PE locations which were used as "gold standard". 435 PEs were identified in the artery branches, of which 172 and 263 were subsegmental and proximal to the subsegmental, respectively. The computer-detected volume was considered true positive (TP) when it overlapped with 10% or more of the gold standard PE volume. Our preliminary test results show that, at an average of 33 and 24 FPs/case, the sensitivities of our PE detection method were 81% and 78%, respectively, for proximal PEs, and 79% and 73%, respectively, for subsegmental PEs. The study demonstrates the feasibility that the automated method can identify PE accurately on CTPA images. Further study is underway to improve the sensitivity and reduce the FPs.

  15. Recruitment of Cancer-Associated Fibroblasts and Blood Vessels by Orthotopic Liver Tumors Imaged in Red Fluorescent Protein (RFP) Transgenic Nude Mice.

    PubMed

    Suetsugu, Atsushi; Hiroshima, Yukihiko; Matsumoto, Takuro; Hasagawa, Kosuke; Nakamura, Miki; Shimizu, Masahito; Saji, Shigetoyo; Moriwaki, Hisataka; Bouvet, Michael; Hoffman, Robert M

    2015-11-01

    The tumor microenvironment (TME) is critical for tumor growth and progression. We report here an imageable model of the TME of orthotopic liver cancer. The transgenic red fluorescent protein (RFP)-expressing nude mouse was used as the host. The RFP nude mouse expresses RFP in all organs. Non-colored Huh-7 human hepatoma cells were injected in the spleen of RFP nude mice to establish an orthotopic liver cancer model. TME formation resulting from the orthotopic liver tumor was observed using the Olympus OV100 small animal fluorescence imaging system. Non-colored liver cancer cells formed tumor colonies in the liver 28 days after cell transplantation to the spleen. RFP-expressing host cells and blood vessels were recruited by the liver tumors as visualized by fluorescence imaging. A desmin- and sirus-red-positive area increased around and within the liver tumor over time. These results indicate cancer-associated fibroblasts (CAFs) were recruited by the liver tumors suggesting that CAFs, along with the angiogenic tumor blood vessels, were necessary for liver-tumor growth and could serve as visible therapeutic targets. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

  16. Predictive Value of ST-Segment Elevation in Lead aVR for Left Main and/or Three-Vessel Disease in Non-ST-Segment Elevation Myocardial Infarction.

    PubMed

    Misumida, Naoki; Kobayashi, Akihiro; Fox, John T; Hanon, Sam; Schweitzer, Paul; Kanei, Yumiko

    2016-01-01

    ST-segment elevation in lead aVR predicts left main and/or three-vessel disease (LM/3VD) in patients with acute coronary syndromes. ST-segment elevation in lead aVR is generally reciprocal to and accompanied by ST-segment depression in precordial leads. Previous studies have assessed the independent predictive value of ST-segment elevation in lead aVR for LM/3VD in non-ST-segment elevation acute coronary syndrome and have reported conflicting results. We performed a retrospective analysis of 379 patients with non-ST-segment elevation myocardial infarction (NSTEMI). Electrocardiograms on presentation were reviewed especially for ST-segment elevation ≥0.05 mV in lead aVR and ST-segment depression ≥0.05 mV in more than two contiguous leads in any other leads. Among 379 patients, 97 (26%) patients had ST-segment elevation in lead aVR and 88 (23%) patients had LM/3VD. Patients with ST-segment elevation in lead aVR had a higher rate of LM/3VD (39% vs. 18%; P < 0.001) and in-hospital revascularization (73% vs. 60%; P = 0.02) driven by a higher rate of in-hospital coronary artery bypass grafting (19% vs. 7%; P < 0.001) than those without ST-segment elevation in lead aVR. On multivariate analysis, ST-segment elevation in lead aVR (odds ratio [OR] 2.05; 95% confidence interval [CI] 1.10-3.77; P = 0.02) and ST-segment depression in leads V1 -V4 (OR 2.99; 95% CI 1.46-6.15; P = 0.003) were independent predictors of LM/3VD. This study demonstrates that ST-segment elevation in lead aVR is an independent predictor of LM/3VD in patients with NSTEMI. © 2015 Wiley Periodicals, Inc.

  17. ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques.

    PubMed

    Ji, Hongwei; He, Jiangping; Yang, Xin; Deklerck, Rudi; Cornelis, Jan

    2013-05-01

    In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

  18. Assessment of myocardial segmental function with coronary artery stenosis in multi-vessel coronary disease patients with normal wall motion.

    PubMed

    Xie, M-Y; Lv, Q; Wang, J; Yin, J-B

    2016-04-01

    To discover the impact of the various degrees of coronary artery stenosis (CAD) on the left ventricular systolic dysfunction in steady state with quantitative analysis of the regional systolic myocardium in longitudinal, radial and circumferential direction in patients with coronary artery disease by two-dimensional speckle tracking imaging (STI). Forty-three normal wall motion-multi vessel coronary artery disease (NWM-MVD) patients labeled as the experimental groups and forty-two subjects with little risk of CAD marked as the control group were enrolled in this study. The two-dimensional STI was obtained in the apical long axis and three levels of the short axis of the left ventricle. The left ventricular wall was divided into 18 segments. The affected myocardia were divided into three groups: group B (coronary stenosis degree ≤50%), group C (coronary stenosis degree 50%-99%)and group D (coronary stenosis degree ≥99%). Using the Q-analysis software, the longitudinal, radial and circumferential systolic strain (SL, SR, SC) and strain ratio (SrL, SrR, SrC) of the myocardium were analyzed. The bradycardia in the NWM-MVD group is greater than that in the control group (16/43 vs. 7/42, p <0.05). Compared with the control group, the SL and SR of group B, group C and group D decreased significantly (p <0.05). Compared with group C, the SL of group D also decreased significantly (p <0.05). However, there was no SC difference among the four groups. Meanwhile, compared with group A, the SrL, SrR and SrC of group B, group C and group D decreased significantly (p <0.05). Compared with group A, group B and group C, the SrL and SrC of group D also decreased (p <0.05). Compared with group A and group C, the SrR of group D decreased. The SrL was equal to 1.085 for the cut-off value, and the sum (1.348) of sensitivity (0.673) and specificity (0.675) were the greatest. Bland-Altman analysis showed that there was myocardium conformity of in both the multi-vessel CAD patients and

  19. Real-time segmentation of multiple implanted cylindrical liver markers in kilovoltage and megavoltage x-ray images

    NASA Astrophysics Data System (ADS)

    Fledelius, W.; Worm, E.; Høyer, M.; Grau, C.; Poulsen, P. R.

    2014-06-01

    Gold markers implanted in or near a tumor can be used as x-ray visible landmarks for image based tumor localization. The aim of this study was to develop and demonstrate fast and reliable real-time segmentation of multiple liver tumor markers in intra-treatment kV and MV images and in cone-beam CT (CBCT) projections, for real-time motion management. Thirteen patients treated with conformal stereotactic body radiation therapy in three fractions had 2-3 cylindrical gold markers implanted in the liver prior to treatment. At each fraction, the projection images of a pre-treatment CBCT scan were used for automatic generation of a 3D marker model that consisted of the size, orientation, and estimated 3D trajectory of each marker during the CBCT scan. The 3D marker model was used for real-time template based segmentation in subsequent x-ray images by projecting each marker's 3D shape and likely 3D motion range onto the imager plane. The segmentation was performed in intra-treatment kV images (526 marker traces, 92 097 marker projections) and MV images (88 marker traces, 22 382 marker projections), and in post-treatment CBCT projections (42 CBCT scans, 71 381 marker projections). 227 kV marker traces with low mean contrast-to-noise ratio were excluded as markers were not visible due to MV scatter. Online segmentation times measured for a limited dataset were used for estimating real-time segmentation times for all images. The percentage of detected markers was 94.8% (kV), 96.1% (MV), and 98.6% (CBCT). For the detected markers, the real-time segmentation was erroneous in 0.2-0.31% of the cases. The mean segmentation time per marker was 5.6 ms [2.1-12 ms] (kV), 5.5 ms [1.6-13 ms] (MV), and 6.5 ms [1.8-15 ms] (CBCT). Fast and reliable real-time segmentation of multiple liver tumor markers in intra-treatment kV and MV images and in CBCT projections was demonstrated for a large dataset.

  20. Automatic segmentation of the liver using multi-planar anatomy and deformable surface model in abdominal contrast-enhanced CT images

    NASA Astrophysics Data System (ADS)

    Jang, Yujin; Hong, Helen; Chung, Jin Wook; Yoon, Young Ho

    2012-02-01

    We propose an effective technique for the extraction of liver boundary based on multi-planar anatomy and deformable surface model in abdominal contrast-enhanced CT images. Our method is composed of four main steps. First, for extracting an optimal volume circumscribing a liver, lower and side boundaries are defined by positional information of pelvis and rib. An upper boundary is defined by separating the lungs and heart from CT images. Second, for extracting an initial liver volume, optimal liver volume is smoothed by anisotropic diffusion filtering and is segmented using adaptively selected threshold value. Third, for removing neighbor organs from initial liver volume, morphological opening and connected component labeling are applied to multiple planes. Finally, for refining the liver boundaries, deformable surface model is applied to a posterior liver surface and missing left robe in previous step. Then, probability summation map is generated by calculating regional information of the segmented liver in coronal plane, which is used for restoring the inaccurate liver boundaries. Experimental results show that our segmentation method can accurately extract liver boundaries without leakage to neighbor organs in spite of various liver shape and ambiguous boundary.

  1. Arsenic Stimulates Sinusoidal Endothelial Cell Capillarization and Vessel Remodeling in Mouse Liver

    PubMed Central

    Straub, Adam C.; Stolz, Donna B.; Ross, Mark A.; Hernández-Zavala, Araceli; Soucy, Nicole V.; Klei, Linda R.; Barchowsky, Aaron

    2006-01-01

    Trivalent arsenic [As(III)] is a well-known environmental toxicant that causes a wide range of organ-specific diseases and cancers. In the human liver, As(III) promotes vascular remodeling, portal fibrosis, and hypertension, but the pathogenesis of these As(III)-induced vascular changes is unknown. To investigate the hypothesis that As(III) targets the hepatic endothelium to initiate pathogenic change, mice were exposed to 0 or 250 parts per billion (ppb) of As(III) in their drinking water for 5 weeks. Arsenic(III) exposure did not affect the overall health of the animals, the general structure of the liver, or hepatocyte morphology. There was no change in the total tissue arsenic levels, indicating that arsenic does not accumulate in the liver at this level of exposure. However, there was significant vascular remodeling with increased sinusoidal endothelial cell (SEC) capillarization, vascularization of the peribiliary vascular plexus (PBVP), and constriction of hepatic arterioles in As(III)-exposed mice. In addition to ultrastructural demonstration of SEC defenestration and capillarization, quantitative immunofluorescence analysis revealed increased sinusoidal PECAM-1 and laminin-1 protein expression, suggesting gain of adherens junctions and a basement membrane. Conversion of SECs to a capillarized, dedifferentiated endothelium was confirmed at the cellular level with demonstration of increased caveolin-1 expression and SEC caveolae, as well as increased membrane-bound Rac1-GTPase. Conclusion These data demonstrate that exposure to As(III) causes functional changes in SEC signaling for sinusoidal capillarization that may be initial events in pathogenic changes in the liver. PMID:17187425

  2. A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points.

    PubMed

    Yang, Xiaopeng; Yu, Hee Chul; Choi, Younggeun; Lee, Wonsup; Wang, Baojian; Yang, Jaedo; Hwang, Hongpil; Kim, Ji Hyun; Song, Jisoo; Cho, Baik Hwan; You, Heecheon

    2014-01-01

    The present study developed a hybrid semi-automatic method to extract the liver from abdominal computerized tomography (CT) images. The proposed hybrid method consists of a customized fast-marching level-set method for detection of an optimal initial liver region from multiple seed points selected by the user and a threshold-based level-set method for extraction of the actual liver region based on the initial liver region. The performance of the hybrid method was compared with those of the 2D region growing method implemented in OsiriX using abdominal CT datasets of 15 patients. The hybrid method showed a significantly higher accuracy in liver extraction (similarity index, SI=97.6 ± 0.5%; false positive error, FPE = 2.2 ± 0.7%; false negative error, FNE=2.5 ± 0.8%; average symmetric surface distance, ASD=1.4 ± 0.5mm) than the 2D (SI=94.0 ± 1.9%; FPE = 5.3 ± 1.1%; FNE=6.5 ± 3.7%; ASD=6.7 ± 3.8mm) region growing method. The total liver extraction time per CT dataset of the hybrid method (77 ± 10 s) is significantly less than the 2D region growing method (575 ± 136 s). The interaction time per CT dataset between the user and a computer of the hybrid method (28 ± 4 s) is significantly shorter than the 2D region growing method (484 ± 126 s). The proposed hybrid method was found preferred for liver segmentation in preoperative virtual liver surgery planning.

  3. Comparison of Safety and Efficacy of Different Models of Target Vessel Regional Chemotherapy for Gastric Cancer with Liver Metastases.

    PubMed

    Chen, Hui; Gao, Song; Yang, Xiao-Zhen; Chen, Li-Jun; Liu, Peng; Xu, Hai-Feng

    2016-01-01

    We previously demonstrated the safety and efficacy of low-dose, short-interval target vessel regional chemotherapy (TVRC(LDSI)) delivered through the hepatic artery with transarterial embolization (TAE) in patients with advanced gastric cancer (AGC). The present study aimed to compare the efficacy of TAE + TVRC(LDSI) with that of standard TAE + TVRC in AGC patients with liver metastases who failed to respond to first- or second-line systemic chemotherapy. This study recruited a total of 58 GC patients with liver metastases after failure of first- or second-line systemic chemotherapy. Twenty-eight patients were assigned to the TAE + TVRC(LDSI) group and 30 patients to the TAE + TVRC group. The primary end point was overall survival (OS(TVRC)), which was defined as the time from the initiation of TVRC until the last follow-up or death. OS(TVRC), time to progression (TTP) until appearance of intra- and extrahepatic metastases, and overall TTP and treatment periods in the TAE + TVRC(LDSI) group were all significantly longer than in the TAE + TVRC group (all p < 0.001). TAE + TVRC(LDSI) had a higher efficacy and safety, which was reflected by OS rates, progression-free survival rates, longer duration of treatment and milder side effects compared to standard TAE + TVRC. © 2015 S. Karger AG, Basel.

  4. A frequent misinterpretation in current research on liver fibrosis: the vessel in the center of CCl4-induced pseudolobules is a portal vein.

    PubMed

    Hammad, Seddik; Braeuning, Albert; Meyer, Christoph; Mohamed, Fatma El Zahraa Ammar; Hengstler, Jan G; Dooley, Steven

    2017-08-19

    Carbon tetrachloride-induced liver injury is a thoroughly studied model for regeneration and fibrosis in rodents. Nevertheless, its pattern of liver fibrosis is frequently misinterpreted as portal type. To clarify this, we show that collagen type IV(+) "streets" and α-SMA(+) cells accumulate pericentrally and extend to neighbouring central areas of the liver lobule, forming a 'pseudolobule'. Blood vessels in the center of such pseudolobules are portal veins as indicated by the presence of bile duct cells (CK19(+)) and the absence of pericentral hepatocytes (glutamine synthetase(+)). It is critical to correctly describe this pattern of fibrosis, particulary for metabolic zonation studies.

  5. 3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

    NASA Astrophysics Data System (ADS)

    Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

    2015-03-01

    During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

  6. Impact of segmentation errors and retinal blood vessels on retinal nerve fibre layer measurements using spectral-domain optical coherence tomography.

    PubMed

    Ye, Cong; Yu, Marco; Leung, Christopher Kai-Shun

    2016-05-01

    To investigate the impact of retinal blood vessels and retinal nerve fibre layer (RNFL) segmentation errors on RNFL measurement. One eye of 180 subjects (60 normal, 66 mild-to-moderate and 54 advanced glaucoma subjects) was randomly selected for RNFL imaging with a spectral-domain OCT. The boundaries of the RNFL detected by the instrument software were checked, and the segmentation errors were corrected by a customized computer program. The differences in average and regional RNFL thicknesses (RNFLT) before and after the correction were analysed to determine the frequency of segmentation error (defined as an absolute difference in average RNFLT >5.0 μm). The ratio of retinal blood vessel cross-sectional area to RNFL cross-sectional area was calculated. The difference in average RNFLT (postsegmentation minus presegmentation refinement) ranged from -3.0 to 2.5 μm (mean ± standard deviation: 0.83 ± 0.86 μm) in the normal, -2.5 to 5.0 μm (0.56 ± 1.08 μm) in the mild-to-moderate glaucoma and -11.0 to 9.5 μm (0.05 ± 3.49 μm) in the advanced glaucoma groups (p = 0.003). A total of 15% of eyes had average RNFLT measurement error >5.0 μm in the advanced glaucoma group. The proportion of retinal blood vessels in the RNFL also increased with the severity of glaucoma (p < 0.001) with 4.2 ± 1.0% in the normal group, 4.9 ± 1.5% in the mild-to-moderate and 8.5 ± 3.5% in the advanced glaucoma groups. Inclusion of retinal blood vessels and RNFL segmentation error could adversely affect RNFL measurement, particularly in advanced glaucoma when the RNFL was thin. © 2015 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

  7. Semi-automated segmentation of solid and GGO nodules in lung CT images using vessel-likelihood derived from local foreground structure

    NASA Astrophysics Data System (ADS)

    Yaguchi, Atsushi; Okazaki, Tomoya; Takeguchi, Tomoyuki; Matsumoto, Sumiaki; Ohno, Yoshiharu; Aoyagi, Kota; Yamagata, Hitoshi

    2015-03-01

    Reflecting global interest in lung cancer screening, considerable attention has been paid to automatic segmentation and volumetric measurement of lung nodules on CT. Ground glass opacity (GGO) nodules deserve special consideration in this context, since it has been reported that they are more likely to be malignant than solid nodules. However, due to relatively low contrast and indistinct boundaries of GGO nodules, segmentation is more difficult for GGO nodules compared with solid nodules. To overcome this difficulty, we propose a method for accurately segmenting not only solid nodules but also GGO nodules without prior information about nodule types. First, the histogram of CT values in pre-extracted lung regions is modeled by a Gaussian mixture model and a threshold value for including high-attenuation regions is computed. Second, after setting up a region of interest around the nodule seed point, foreground regions are extracted by using the threshold and quick-shift-based mode seeking. Finally, for separating vessels from the nodule, a vessel-likelihood map derived from elongatedness of foreground regions is computed, and a region growing scheme starting from the seed point is applied to the map with the aid of fast marching method. Experimental results using an anthropomorphic chest phantom showed that our method yielded generally lower volumetric measurement errors for both solid and GGO nodules compared with other methods reported in preceding studies conducted using similar technical settings. Also, our method allowed reasonable segmentation of GGO nodules in low-dose images and could be applied to clinical CT images including part-solid nodules.

  8. Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network

    PubMed Central

    2016-01-01

    Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI) image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN), which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the “ground truth.” Results. The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively. PMID:27597960

  9. Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network.

    PubMed

    Le, Trong-Ngoc; Bao, Pham The; Huynh, Hieu Trung

    2016-01-01

    Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI) image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN), which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the "ground truth." Results. The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively.

  10. Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features.

    PubMed

    Rahebi, Javad; Hardalaç, Fırat

    2014-08-01

    This paper focuses on the issue of extracting retina vessels with supervised approach. Since the green channel in the retina image has the best contrast between vessel and non-vessel, this channel is used to separate vessels. In our approach we are proposing a technique of using gray-level co-occurrence matrix method for composition of the retinal images. It is based on fact that the co-occurrence matrix of retina image describes the transition of intensities between neighbour pixels, indicating spatial structural information of retina image. So, we first extract the features vector based on specified characteristics of the gray-level co-occurrence matrix and then we use these features vector to train a neural network approach for the classification method which makes our proposed approach more effective. Obtained results from the experiments in DRIVE and STARE database shows the advantage of the proposed method in contrast to current methods. This advantage is evaluated by the criteria of sensitivity, specificity, area under ROC and accuracy. The result of such a conversion as the input vector of a multilayer perceptron neural network will be trained and tested. Although in recent years different methods have been presented in this respect, but results of simulation shows that the proposed algorithm has a very high efficiency than the other researches.

  11. A simple technique for hemostasis control after enucleation of deep located liver tumors or after liver trauma

    PubMed Central

    Machado, Marcel A.; Surjan, Rodrigo C.; Basseres, Tiago; Makdissi, Fábio F.

    2016-01-01

    Modern liver techniques allowed the development of segment-based anatomical liver resections. Nevertheless, there is still a place for nonanatomical liver resections. However, in some cases, there is a need for enucleation of deep located liver tumors. The main problem with enucleation of a liver tumor deeply located in the middle of the liver is the control of bleeding resulting from the rupture of small or medium vessels. The authors describe a simple way to control the bleeding without the use of any special instrument or material. This technique can also be used to control bleeding from penetrating liver injury. PMID:26846270

  12. Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms

    SciTech Connect

    Suzuki, Kenji; Kohlbrenner, Ryan; Epstein, Mark L.; Obajuluwa, Ademola M.; Xu Jianwu; Hori, Masatoshi

    2010-05-15

    Purpose: Computerized liver extraction from hepatic CT images is challenging because the liver often abuts other organs of a similar density. The purpose of this study was to develop a computer-aided measurement of liver volumes in hepatic CT. Methods: The authors developed a computerized liver extraction scheme based on geodesic active contour segmentation coupled with level-set contour evolution. First, an anisotropic diffusion filter was applied to portal-venous-phase CT images for noise reduction while preserving the liver structure, followed by a scale-specific gradient magnitude filter to enhance the liver boundaries. Then, a nonlinear grayscale converter enhanced the contrast of the liver parenchyma. By using the liver-parenchyma-enhanced image as a speed function, a fast-marching level-set algorithm generated an initial contour that roughly estimated the liver shape. A geodesic active contour segmentation algorithm coupled with level-set contour evolution refined the initial contour to define the liver boundaries more precisely. The liver volume was then calculated using these refined boundaries. Hepatic CT scans of 15 prospective liver donors were obtained under a liver transplant protocol with a multidetector CT system. The liver volumes extracted by the computerized scheme were compared to those traced manually by a radiologist, used as ''gold standard.''Results: The mean liver volume obtained with our scheme was 1504 cc, whereas the mean gold standard manual volume was 1457 cc, resulting in a mean absolute difference of 105 cc (7.2%). The computer-estimated liver volumetrics agreed excellently with the gold-standard manual volumetrics (intraclass correlation coefficient was 0.95) with no statistically significant difference (F=0.77; p(F{<=}f)=0.32). The average accuracy, sensitivity, specificity, and percent volume error were 98.4%, 91.1%, 99.1%, and 7.2%, respectively. Computerized CT liver volumetry would require substantially less completion time

  13. Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms

    PubMed Central

    Suzuki, Kenji; Kohlbrenner, Ryan; Epstein, Mark L.; Obajuluwa, Ademola M.; Xu, Jianwu; Hori, Masatoshi

    2010-01-01

    Purpose: Computerized liver extraction from hepatic CT images is challenging because the liver often abuts other organs of a similar density. The purpose of this study was to develop a computer-aided measurement of liver volumes in hepatic CT. Methods: The authors developed a computerized liver extraction scheme based on geodesic active contour segmentation coupled with level-set contour evolution. First, an anisotropic diffusion filter was applied to portal-venous-phase CT images for noise reduction while preserving the liver structure, followed by a scale-specific gradient magnitude filter to enhance the liver boundaries. Then, a nonlinear grayscale converter enhanced the contrast of the liver parenchyma. By using the liver-parenchyma-enhanced image as a speed function, a fast-marching level-set algorithm generated an initial contour that roughly estimated the liver shape. A geodesic active contour segmentation algorithm coupled with level-set contour evolution refined the initial contour to define the liver boundaries more precisely. The liver volume was then calculated using these refined boundaries. Hepatic CT scans of 15 prospective liver donors were obtained under a liver transplant protocol with a multidetector CT system. The liver volumes extracted by the computerized scheme were compared to those traced manually by a radiologist, used as “gold standard.” Results: The mean liver volume obtained with our scheme was 1504 cc, whereas the mean gold standard manual volume was 1457 cc, resulting in a mean absolute difference of 105 cc (7.2%). The computer-estimated liver volumetrics agreed excellently with the gold-standard manual volumetrics (intraclass correlation coefficient was 0.95) with no statistically significant difference (F=0.77; p(F≤f)=0.32). The average accuracy, sensitivity, specificity, and percent volume error were 98.4%, 91.1%, 99.1%, and 7.2%, respectively. Computerized CT liver volumetry would require substantially less completion time

  14. Modulation of nitric oxide synthase activity in brain, liver, and blood vessels of spontaneously hypertensive rats by ascorbic acid: protection from free radical injury.

    PubMed

    Newaz, M A; Yousefipour, Z; Nawal, N N A

    2005-08-01

    End organ damage in essential hypertension has been linked to increased oxygen free radical generation, reduced antioxidant defense, and/or attenuation of nitric oxide synthase (NOS) activity. Ascorbic acid (AA), a water-soluble antioxidant, has been reported as a strong defense against free radicals in both aqueous and nonaqueous environment. In this study we examined the hypothesis that antioxidant ascorbic acid may confer protection from increased free radical activity in brain, liver, and blood vessels of spontaneously hypertensive rats (SHR). Male SHRs were divided into groups: SHR + AA (treated with AA, 1 mg/rat/day; for 12 weeks) or SHR (untreated). Wister-Kyoto rats (WKY) served as the control. Mean systolic blood pressure (SBP) in treated and untreated SHR was 145 +/- 7 mmHg and 142 +/- 8 mmHg, respectively. AA treatment prevented the increase in systolic blood pressure in SHR by 37 +/- 1% (p < 0.05). NOS activity in the brain, liver, and blood vessels of WKY rat was 1.82 +/- 0.02, 0.14 +/- 0.003, and 1.54 +/- 0.06 pmol citruline/mg protein, respectively. In SHR, total NOS activity was significantly reduced by 52 +/- 1%, 21 +/- 3%, and 44 +/- 4%, respectively. AA increased NOS activity in brain, liver, and blood vessels of SHR from 0.87 +/-.03, 0.11 +/-.01, and 0.87 +/-.08 pmol citruline/mg protein to 0.93 +/- 0.01, 0.13 +/- 0.001, and 1.11 +/- 0.03 pmol citruline/mg protein (p < 0.05), respectively. Lipid peroxides in the brain, liver, and blood vessels from WKY rats were 0.87 +/- 0.06, 0.11 +/- 0.005, and 0.47 +/- 0.04 nmol MDA equiv/mg protein, respectively. In SHR, lipid peroxides in brain, liver, and blood vessels were significantly increased by 40 +/- 3%, 64 +/- 3%, and 104 +/- 13%, respectively. AA reduced lipid peroxidation in liver and blood vessels by 17 +/- 1% and 34 +/- 3% but not in brain. Plasma lipid peroxides were almost doubled in SHR (p < 0.01) together with a reduction in total antioxidant status (6 +/- 0.1%; p < 0.05), nitrite (53 +/- 2

  15. Anatomical Variations in the Pattern of the Right Hepatic Veins Draining the Posterior Segment of the Right Lobe of the Liver

    PubMed Central

    Tuli, Anita

    2015-01-01

    Background: The pattern of drainage in the right posterior lobe of liver varies considerably. The knowledge of this variation is very important while performing various surgeries on the right posterior lobe. Aim: A study was conducted to see the variations in the pattern of drainage of posterior segment of the right lobe of liver. The aim was to see the variations of right hepatic vein and small accessory hepatic veins draining the posterior segment, the presence of which led to modifications in drainage of posterior segment. Material and Methods: Sixty formalin fixed adult human liver specimens were dissected manually. Results: According to the pattern of drainage of tributaries of right hepatic vein, the right hepatic vein was classified into type I, type II, type III and type IV. According to presence of inferior right hepatic vein, three types of drainage of posterior lobe were seen: Type I, (76.36%) right hepatic vein was large, draining wide area of posterior segment. A small inferior right hepatic vein drained the small area of posterior segment. In Type II, (19.92%) both right hepatic and inferior right hepatic veins were medium sized draining the posteroinferior segment of the right lobe concomitantly. In Type III, (32%) accessory veins, the middle right hepatic vein drained the posterosuperior (VII) as well as the posteroinferior (VI) segment. In one specimen, there were numerous middle right hepatic veins draining the right posterior segment. The knowledge of anatomic relationship of veins draining right lobe, is important in performing right posterior segmentectomy. Conclusion: For safe resection of the liver, the complex anatomy of the distribution of the tributaries of the right hepatic vein and the accessory veins have to be studied prior to any surgery done on liver. PMID:25954610

  16. Laparoscopic Left Liver Sectoriectomy of Caroli's Disease Limited to Segment II and III

    PubMed Central

    Boni, Luigi; Dionigi, Gianlorenzo; Rovera, Francesca; Di Giuseppe, Matteo

    2009-01-01

    Caroli's disease is defined as a abnormal dilatation of the intra-hepatica bile ducts: Its incidence is extremely low (1 in 1,000,000 population) and in most of the cases the whole liver is interested and liver transplantation is the treatment of choice. In case of dilatation limited to the left or right lobe, liver resection can be performed. For many year the standard approach for liver resection has been a formal laparotomy by means of a large incision of abdomen that is characterized by significant post-operatie morbidity. More recently, minimally invasive, laparoscopic approach has been proposed as possible surgical technique for liver resection both for benign and malignant diseases. The main benefits of the minimally invasive approach is represented by a significant reduction of the surgical trauma that allows a faster recovery a less post-operative complications. This video shows a case of Caroli s disease occured in a 58 years old male admitted at the gastroenterology department for sudden onset of abdominal pain associated with fever (>38C° ), nausea and shivering. Abdominal ultrasound demonstrated a significant dilatation of intra-hepatic left sited bile ducts with no evidences of gallbladder or common bile duct stones. Such findings were confirmed abdominal high resolution computer tomography. Laparoscopic left sectoriectomy was planned. Five trocars and 30° optic was used, exploration of the abdominal cavity showed no adhesions or evidences of other diseases. In order to control blood inflow to the liver, vascular clamp was placed on the hepatic pedicle (Pringle s manouvre), Parenchymal division is carried out with a combined use of 5 mm bipolar forceps and 5 mm ultrasonic dissector. A severely dilated left hepatic duct was isolated and divided using a 45mm endoscopic vascular stapler. Liver dissection was continued up to isolation of the main left portal branch that was then divided with a further cartridge of 45 mm vascular stapler. At his point

  17. Hepatic Artery Reconstruction Using 3-in-1 Segmental Resection in Pediatric Living Donor Liver Transplantation: A Case Report and Literature Review.

    PubMed

    Luo, Y; Zhao, D; Zhang, M; Zhou, T; Qiu, B-J; Zhang, J-J; Xia, Q

    2017-09-01

    We report a transplant of the left lateral liver segments with 3 arteries for a pediatric recipient from a living donor. A 6-month-old female infant was diagnosed with liver cirrhosis secondary to biliary atresia and scheduled for living donor liver transplantation (LDLT; mother as donor). Left lateral hepatectomy was performed at the donor site. The dissection of the left hepatic artery (HA), which was divided immediately after its origin, showed 3 branches for segments II, III, and IV. The arteries for segment II, segment III, and segment IV were anastomosed to the recipient HA. No postoperative complications were observed. The outcome of this case demonstrates that left lateral segments with 3 arteries can be successfully used if proper surgical techniques are applied. From this experience we can recommend "3-in-1 segmental resection" in the donor can be safely done by skilled microvascular surgeons and this technique should be considered for selected cases where multiple tiny arteries supply the graft. Copyright © 2017. Published by Elsevier Inc.

  18. WE-AB-303-05: Breathing Motion of Liver Segments From Fiducial Tracking During Robotic Radiosurgery and Comparison with 4D-CT-Derived Fiducial Motion

    SciTech Connect

    Sutherland, J; Pantarotto, J; Nair, V; Cook, G; Plourde, M; Vandervoort, E

    2015-06-15

    Purpose: To quantify respiratory-induced motion of liver segments using the positions of implanted fiducials during robotic radiosurgery. This study also compared fiducial motion derived from four-dimensional computed tomography (4D-CT) maximum intensity projections (MIP) with motion derived from imaging during treatment. Methods: Forty-two consecutive liver patients treated with liver ablative radiotherapy were accrued to an ethics approved retrospective study. The liver segment in which each fiducial resided was identified. Fiducial positions throughout each treatment fraction were determined using orthogonal kilovoltage images. Any data due to patient repositioning or motion was removed. Mean fiducial positions were calculated. Fiducial positions beyond two standard deviations of the mean were discarded and remaining positions were fit to a line segment using least squares minimization (LSM). For eight patients, fiducial motion was derived from 4D-CT MIPs by calculating the CT number weighted mean position of the fiducial on each slice and fitting a line segment to these points using LSM. Treatment derived fiducial trajectories were corrected for patient rotation and compared to MIP derived trajectories. Results: The mean total magnitude of fiducial motion across all liver segments in left-right, anteroposterior, and superoinferior (SI) directions were 3.0 ± 0.2 mm, 9.3 ± 0.4 mm, and 20.5 ± 0.5 mm, respectively. Differences in per-segment mean fiducial motion were found with SI motion ranging from 12.6 ± 0.8 mm to 22.6 ± 0.9 mm for segments 3 and 8, respectively. Large, varied differences between treatment and MIP derived motion at simulation were found with the mean difference for SI motion being 2.6 mm (10.8 mm standard deviation). Conclusion: The magnitude of liver fiducial motion was found to differ by liver segment. MIP derived liver fiducial motion differed from motion observed during treatment, implying that 4D-CTs may not accurately capture the

  19. Automatic Registration between Real-Time Ultrasonography and Pre-Procedural Magnetic Resonance Images: A Prospective Comparison between Two Registration Methods by Liver Surface and Vessel and by Liver Surface Only.

    PubMed

    Kim, Ah Yeong; Lee, Min Woo; Cha, Dong Ik; Lim, Hyo Keun; Oh, Young-Taek; Jeong, Ja-Yeon; Chang, Jung-Woo; Ryu, Jiwon; Lee, Kyong Joon; Kim, Jaeil; Bang, Won-Chul; Shin, Dong Kuk; Choi, Sung Jin; Koh, Dalkwon; Seo, Bong Koo; Kim, Kyunga

    2016-07-01

    The aim of this study was to compare the accuracy of and the time required for image fusion between real-time ultrasonography (US) and pre-procedural magnetic resonance (MR) images using automatic registration by a liver surface only method and automatic registration by a liver surface and vessel method. This study consisted of 20 patients referred for planning US to assess the feasibility of percutaneous radiofrequency ablation or biopsy for focal hepatic lesions. The first 10 consecutive patients were evaluated by an experienced radiologist using the automatic registration by liver surface and vessel method, whereas the remaining 10 patients were evaluated using the automatic registration by liver surface only method. For all 20 patients, image fusion was automatically executed after following the protocols and fused real-time US and MR images moved synchronously. The accuracy of each method was evaluated by measuring the registration error, and the time required for image fusion was assessed by evaluating the recorded data using in-house software. The results obtained using the two automatic registration methods were compared using the Mann-Whitney U-test. Image fusion was successful in all 20 patients, and the time required for image fusion was significantly shorter with the automatic registration by liver surface only method than with the automatic registration by liver surface and vessel method (median: 43.0 s, range: 29-74 s vs. median: 83.0 s, range: 46-101 s; p = 0.002). The registration error did not significantly differ between the two methods (median: 4.0 mm, range: 2.1-9.9 mm vs. median: 3.7 mm, range: 1.8-5.2 mm; p = 0.496). The automatic registration by liver surface only method offers faster image fusion between real-time US and pre-procedural MR images than does the automatic registration by liver surface and vessel method. However, the degree of accuracy was similar for the two methods. Copyright © 2016 World Federation for Ultrasound

  20. Feasibility of laparoscopic liver resection for caudate lobe: technical strategy and comparative analysis with anteroinferior and posterosuperior segments.

    PubMed

    Araki, Kenichiro; Fuks, David; Nomi, Takeo; Ogiso, Satoshi; Lozano, Ruben R; Kuwano, Hiroyuki; Gayet, Brice

    2016-10-01

    Although laparoscopic liver resection (LLR) is now considered a standard procedure in peripheral segments, there are few reports on laparoscopic segment 1 (Sg1) resection. The aim of this study was to assess both safety and feasibility of Sg1 LLR. From 2000 to 2014, all patients who underwent LLR were identified from a prospective database. Patients with resection of Sg1 (Sg1 group) were compared with those with resection of anteroinferior segments (AI group: segments 3, 4b, 5, 6) or posterosuperior segments (PS group: segments 4a, 7, 8), in terms of tumor characteristics, surgical treatment, and short-term outcomes. There were 15, 151, and 67 patients in Sg1, AI, and PS groups. Tumor size and tumor number were similar between the three groups (p = 0.139, p = 0.102). Operative time was significantly shorter in Sg1 (150 min) and AI group (135 min) compared with PS group (180 min) (p = 0.021). Median blood loss was notably higher in PS group (140 ml) compared with Sg1 group (75 ml) and AI group (10 ml) (p = 0.001). No mortality was observed in all groups. Postoperative complication rate was 20.0 % with Sg1 group, 14.6 % with AI group, and 20.9 % with PS group (p = 0.060). The rate of major complication was significantly higher in Sg1 group (13.3 %) and PS group (11.9 %) compared with AI group (4.0 %) (p = 0.042). Resection margins were clear in all Sg1 and PS group patients, whereas two (1.3 %) patients in AI group had R1 margins (p = 0.586). The laparoscopic approach of isolated resection located in the caudate lobe is a feasible and curative surgical option in selected patients.

  1. Vasculature segmentation for radio frequency ablation of non-resectable hepatic tumors

    NASA Astrophysics Data System (ADS)

    Hemler, Paul F.; McCreedy, Evan S.; Cheng, Ruida; Wood, Brad; McAuliffe, Matthew J.

    2006-03-01

    In Radio Frequency Ablation (RFA) procedures, hepatic tumor tissue is heated to a temperature where necrosis is insured. Unfortunately, recent results suggest that heating tumor tissue to necrosis is complicated because nearby major blood vessels provide a cooling effect. Therefore, it is fundamentally important for physicians to perform a careful analysis of the spatial relationship of diseased tissue to larger liver blood vessels. The liver contains many of these large vessels, which affect the RFA ablation shape and size. There are many sophisticated vasculature detection and segmentation techniques reported in the literature that identify continuous vessels as the diameter changes size and it transgresses through many bifurcation levels. However, the larger blood vessels near the treatment area are the only vessels required for proper RFA treatment plan formulation and analysis. With physician guidance and interaction, our system can segment those vessels which are most likely to affect the RFA ablations. We have found that our system provides the physician with therapeutic, geometric and spatial information necessary to accurately plan treatment of tumors near large blood vessels. The segmented liver vessels near the treatment region are also necessary for computing isolevel heating profiles used to evaluate different proposed treatment configurations.

  2. Automated Method for Identification and Artery-Venous Classification of Vessel Trees in Retinal Vessel Networks

    PubMed Central

    Joshi, Vinayak S.; Reinhardt, Joseph M.; Garvin, Mona K.; Abramoff, Michael D.

    2014-01-01

    The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV) classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44 correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42. PMID:24533066

  3. Fatty Liver

    MedlinePlus

    ... and Throat Disorders Eye Disorders Fundamentals Heart and Blood Vessel Disorders Hormonal and Metabolic Disorders Immune Disorders Infections Injuries and Poisoning Kidney and Urinary Tract Disorders Liver ...

  4. Reconstitution of hepatic tissue architectures from fetal liver cells obtained from a three-dimensional culture with a rotating wall vessel bioreactor.

    PubMed

    Ishikawa, Momotaro; Sekine, Keisuke; Okamura, Ai; Zheng, Yun-wen; Ueno, Yasuharu; Koike, Naoto; Tanaka, Junzo; Taniguchi, Hideki

    2011-06-01

    Reconstitution of tissue architecture in vitro is important because it enables researchers to investigate the interactions and mutual relationships between cells and cellular signals involved in the three-dimensional (3D) construction of tissues. To date, in vitro methods for producing tissues with highly ordered structure and high levels of function have met with limited success although a variety of 3D culture systems have been investigated. In this study, we reconstituted functional hepatic tissue including mature hepatocyte and blood vessel-like structures accompanied with bile duct-like structures from E15.5 fetal liver cells, which contained more hepatic stem/progenitor cells comparing with neonatal liver cells. The culture was performed in a simulated microgravity environment produced by a rotating wall vessel (RWV) bioreactor. The hepatocytes in the reconstituted 3D tissue were found to be capable of producing albumin and storing glycogen. Additionally, bile canaliculi between hepatocytes, characteristics of adult hepatocyte in vivo were also formed. Apart from this, bile duct structure secreting mucin was shown to form complicated tubular branches. Furthermore, gene expression analysis by semi-quantitative RT-PCR revealed the elevated levels of mature hepatocyte markers as well as genes with the hepatic function. With RWV culture system, we could produce functionally reconstituted liver tissue and this might be useful in pharmaceutical industry including drug screening and testing and other applications such as an alternative approach to experimental animals.

  5. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images.

    PubMed

    Wang, Jinke; Cheng, Yuanzhi; Guo, Changyong; Wang, Yadong; Tamura, Shinichi

    2016-05-01

    Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images. First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape-intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms. Using the 25 test CT datasets, average symmetric surface distance is [Formula: see text] mm (range 0.62-2.12 mm), root mean square symmetric surface distance error is [Formula: see text] mm (range 0.97-3.01 mm), and maximum symmetric surface distance error is [Formula: see text] mm (range 12.73-26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques. The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual

  6. Systematic review: comparative effectiveness of adjunctive devices in patients with ST-segment elevation myocardial infarction undergoing percutaneous coronary intervention of native vessels

    PubMed Central

    2011-01-01

    Background During percutaneous coronary intervention (PCI), dislodgement of atherothrombotic material from coronary lesions can result in distal embolization, and may lead to increased major adverse cardiovascular events (MACE) and mortality. We sought to systematically review the comparative effectiveness of adjunctive devices to remove thrombi or protect against distal embolization in patients with ST-segment elevation myocardial infarction (STEMI) undergoing PCI of native vessels. Methods We conducted a systematic literature search of Medline, the Cochrane Database, and Web of Science (January 1996-March 2011), http://www.clinicaltrials.gov, abstracts from major cardiology meetings, TCTMD, and CardioSource Plus. Two investigators independently screened citations and extracted data from randomized controlled trials (RCTs) that compared the use of adjunctive devices plus PCI to PCI alone, evaluated patients with STEMI, enrolled a population with 95% of target lesion(s) in native vessels, and reported data on at least one pre-specified outcome. Quality was graded as good, fair or poor and the strength of evidence was rated as high, moderate, low or insufficient. Disagreement was resolved through consensus. Results 37 trials met inclusion criteria. At the maximal duration of follow-up, catheter aspiration devices plus PCI significantly decreased the risk of MACE by 27% compared to PCI alone. Catheter aspiration devices also significantly increased the achievement of ST-segment resolution by 49%, myocardial blush grade of 3 (MBG-3) by 39%, and thrombolysis in myocardial infarction (TIMI) 3 flow by 8%, while reducing the risk of distal embolization by 44%, no reflow by 48% and coronary dissection by 70% versus standard PCI alone. In a majority of trials, the use of catheter aspiration devices increased procedural time upon qualitative assessment. Distal filter embolic protection devices significantly increased the risk of target revascularization by 39% although the

  7. Crystal structure of cod liver class I alcohol dehydrogenase: substrate pocket and structurally variable segments.

    PubMed Central

    Ramaswamy, S.; el Ahmad, M.; Danielsson, O.; Jörnvall, H.; Eklund, H.

    1996-01-01

    The structural framework of cod liver alcohol dehydrogenase is similar to that of horse and human alcohol dehydrogenases. In contrast, the substrate pocket differs significantly, and main differences are located in three loops. Nevertheless, the substrate pocket is hydrophobic like that of the mammalian class I enzymes and has a similar topography in spite of many main-chain and side-chain differences. The structural framework of alcohol dehydrogenase is also present in a number of related enzymes like glucose dehydrogenase and quinone oxidoreductase. These enzymes have completely different substrate specificity, but also for these enzymes, the corresponding loops of the substrate pocket have significantly different structures. The domains of the two subunits in the crystals of the cod enzyme further differ by a rotation of the catalytic domains by about 6 degrees. In one subunit, they close around the coenzyme similarly as in coenzyme complexes of the horse enzyme, but form a more open cleft in the other subunit, similar to the situation in coenzyme-free structures of the horse enzyme. The proton relay system differs from the mammalian class I alcohol dehydrogenases. His 51, which has been implicated in mammalian enzymes to be important for proton transfer from the buried active site to the surface is not present in the cod enzyme. A tyrosine in the corresponding position is turned into the substrate pocket and a water molecule occupies the same position in space as the His side chain, forming a shorter proton relay system. PMID:8845755

  8. Computer-aided classification of liver tumors in 3D ultrasound images with combined deformable model segmentation and support vector machine

    NASA Astrophysics Data System (ADS)

    Lee, Myungeun; Kim, Jong Hyo; Park, Moon Ho; Kim, Ye-Hoon; Seong, Yeong Kyeong; Cho, Baek Hwan; Woo, Kyoung-Gu

    2014-03-01

    In this study, we propose a computer-aided classification scheme of liver tumor in 3D ultrasound by using a combination of deformable model segmentation and support vector machine. For segmentation of tumors in 3D ultrasound images, a novel segmentation model was used which combined edge, region, and contour smoothness energies. Then four features were extracted from the segmented tumor including tumor edge, roundness, contrast, and internal texture. We used a support vector machine for the classification of features. The performance of the developed method was evaluated with a dataset of 79 cases including 20 cysts, 20 hemangiomas, and 39 hepatocellular carcinomas, as determined by the radiologist's visual scoring. Evaluation of the results showed that our proposed method produced tumor boundaries that were equal to or better than acceptable in 89.8% of cases, and achieved 93.7% accuracy in classification of cyst and hemangioma.

  9. Understanding Spatially Complex Segmental and Branch Anatomy Using 3D Printing: Liver, Lung, Prostate, Coronary Arteries, and Circle of Willis.

    PubMed

    Javan, Ramin; Herrin, Douglas; Tangestanipoor, Ardalan

    2016-09-01

    Three-dimensional (3D) manufacturing is shaping personalized medicine, in which radiologists can play a significant role, be it as consultants to surgeons for surgical planning or by creating powerful visual aids for communicating with patients, physicians, and trainees. This report illustrates the steps in development of custom 3D models that enhance the understanding of complex anatomy. We graphically designed 3D meshes or modified imported data from cross-sectional imaging to develop physical models targeted specifically for teaching complex segmental and branch anatomy. The 3D printing itself is easily accessible through online commercial services, and the models are made of polyamide or gypsum. Anatomic models of the liver, lungs, prostate, coronary arteries, and the Circle of Willis were created. These models have advantages that include customizable detail, relative low cost, full control of design focusing on subsegments, color-coding potential, and the utilization of cross-sectional imaging combined with graphic design. Radiologists have an opportunity to serve as leaders in medical education and clinical care with 3D printed models that provide beneficial interaction with patients, clinicians, and trainees across all specialties by proactively taking on the educator's role. Complex models can be developed to show normal anatomy or common pathology for medical educational purposes. There is a need for randomized trials, which radiologists can design, to demonstrate the utility and effectiveness of 3D printed models for teaching simple and complex anatomy, simulating interventions, measuring patient satisfaction, and improving clinical care. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  10. Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change.

    PubMed

    Williams, Owen A; Zeestraten, Eva A; Benjamin, Philip; Lambert, Christian; Lawrence, Andrew J; Mackinnon, Andrew D; Morris, Robin G; Markus, Hugh S; Charlton, Rebecca A; Barrick, Thomas R

    2017-01-01

    Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period. 98 patients (aged 43-89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ) describing the patients' brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials. Changes in brain structure described by DSEG θ were related to change in EF and IPS (p < 0.001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models (p = 0

  11. Implementation of an interactive liver surgery planning system

    NASA Astrophysics Data System (ADS)

    Wang, Luyao; Liu, Jingjing; Yuan, Rong; Gu, Shuguo; Yu, Long; Li, Zhitao; Li, Yanzhao; Li, Zhen; Xie, Qingguo; Hu, Daoyu

    2011-03-01

    Liver tumor, one of the most wide-spread diseases, has a very high mortality in China. To improve success rates of liver surgeries and life qualities of such patients, we implement an interactive liver surgery planning system based on contrastenhanced liver CT images. The system consists of five modules: pre-processing, segmentation, modeling, quantitative analysis and surgery simulation. The Graph Cuts method is utilized to automatically segment the liver based on an anatomical prior knowledge that liver is the biggest organ and has almost homogeneous gray value. The system supports users to build patient-specific liver segment and sub-segment models using interactive portal vein branch labeling, and to perform anatomical resection simulation. It also provides several tools to simulate atypical resection, including resection plane, sphere and curved surface. To match actual surgery resections well and simulate the process flexibly, we extend our work to develop a virtual scalpel model and simulate the scalpel movement in the hepatic tissue using multi-plane continuous resection. In addition, the quantitative analysis module makes it possible to assess the risk of a liver surgery. The preliminary results show that the system has the potential to offer an accurate 3D delineation of the liver anatomy, as well as the tumors' location in relation to vessels, and to facilitate liver resection surgeries. Furthermore, we are testing the system in a full-scale clinical trial.

  12. Spatial anatomy of the round ligament, gallbladder, and intrahepatic vessels in patients with right-sided round ligament of the liver.

    PubMed

    Ibukuro, Kenji; Takeguchi, Takaya; Fukuda, Hozumi; Abe, Shoko; Tobe, Kimiko

    2016-11-01

    To analyze the vascular structure of the liver in patients with a right-sided round ligament. We reviewed 16 patients with a right-sided round ligament and 3 polysplenia and situs inversus patients with a left-sided round ligament who underwent multidetector row CT with contrast media. The patient population consisted of 13 men and 6 women (mean 62 years). We analyzed the axial and volume-rendered images for the location of the round ligament, gallbladder, portal veins, hepatic veins, and hepatic artery. The following imaging findings for the patients with polysplenia and situs inversus were horizontally reversed. The prevalence of a right-sided round ligament with and without polysplenia was 75 and 0.11 %, respectively. The gallbladder was located to the right, below, and left of the round ligament in 27.7, 38.8 and 33.3 %, respectively. Independent branching of the right posterior portal vein was noted in 57.8 %. PV4 was difficult to identify in 36.8 %. The middle hepatic vein was located to the left of the round ligament. Two branching patterns for the lateral and medial branches of the right anterior hepatic artery were noted: the common (44.4 %) and separated types (55.5 %). Both of the right anterior hepatic artery and portal vein ramified into two segments; the lateral segment with many branches and the medial segment with a few branches. The right-sided round ligament divided the right anterior section into the lateral and medial segments based on the portal vein and hepatic artery anatomy.

  13. Validation of the vessel-specific leads (VSLs) for detection of acute ischemia on a dataset with non-ischemic ST-segment deviation.

    PubMed

    Wang, John J; Pahlm, Olle; Wagner, Galen S; Warren, James W; Horáček, B Milan; Sapp, John L

    Existing criteria recommended by ACC/ESC for identifying patients with ST elevation myocardial infarction (STEMI) from the 12-lead ECG perform with high specificity (SP) but low sensitivity (SE). In our previous studies, we found that the SE of acute ischemia detection can be markedly improved without any loss of SP by calculating, from the 12-lead ECG, ST deviation in 3 "optimal" vessel-specific leads (VSLs). To further validate the method, we evaluated the SP performance using a dataset with non-ischemic ST-segment changes. 12-lead ECGs of 100 patients (75 males/25 females, age range 12-83years, average age 52years) were retrieved from a centralized ECG management system at Skåne University Hospital, Lund, Sweden. These ECGs were chosen to represent five subgroups with various causes of pathological ST deviation, other than acute coronary occlusion: a) ventricular preexcitation (n=12), b) acute pericarditis (n=26), c) early repolarization syndrome (ERS) (n=14), d) left ventricular hypertrophy (LVH) with "strain" (n=26), and e) left bundle branch block (LBBB) (n=22). ECGs with inadequate signal quality, heart rate exceeding 120bpm and/or atrial flutter were not selected for this study population. Both STEMI criteria and VSLs criteria with and without a new augmented LVH-specific derived lead were tested. SP, calculated for each subgroup and combined, was used as the performance measure for comparison. SP test results for the STEMI criteria vs. the VSLs method without the augmented LVH lead were 100% vs. 92%, 4% vs. 88%, 29% vs. 100%, 100% vs. 77%, and 64% vs. 68% for the five subgroups with preexcitation, pericarditis, ERS, LVH, and LBBB, respectively. For the whole group, SP was 57% for the STEMI criteria and 83% for the VSLs criteria; this improvement was statistically significant (p<0.001). With the augmented LVH lead, SP for the VSLs improved from 77% to 96% for the LVH subgroup and SP for the other subgroups remained unchanged. For the whole study group, SP

  14. SU-E-I-87: Automated Liver Segmentation Method for CBCT Dataset by Combining Sparse Shape Composition and Probabilistic Atlas Construction

    SciTech Connect

    Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng

    2014-06-01

    Purpose: The aiming of this study was to extract liver structures for daily Cone beam CT (CBCT) images automatically. Methods: Datasets were collected from 50 intravenous contrast planning CT images, which were regarded as training dataset for probabilistic atlas and shape prior model construction. Firstly, probabilistic atlas and shape prior model based on sparse shape composition (SSC) were constructed by iterative deformable registration. Secondly, the artifacts and noise were removed from the daily CBCT image by an edge-preserving filtering using total variation with L1 norm (TV-L1). Furthermore, the initial liver region was obtained by registering the incoming CBCT image with the atlas utilizing edge-preserving deformable registration with multi-scale strategy, and then the initial liver region was converted to surface meshing which was registered with the shape model where the major variation of specific patient was modeled by sparse vectors. At the last stage, the shape and intensity information were incorporated into joint probabilistic model, and finally the liver structure was extracted by maximum a posteriori segmentation.Regarding the construction process, firstly the manually segmented contours were converted into meshes, and then arbitrary patient data was chosen as reference image to register with the rest of training datasets by deformable registration algorithm for constructing probabilistic atlas and prior shape model. To improve the efficiency of proposed method, the initial probabilistic atlas was used as reference image to register with other patient data for iterative construction for removing bias caused by arbitrary selection. Results: The experiment validated the accuracy of the segmentation results quantitatively by comparing with the manually ones. The volumetric overlap percentage between the automatically generated liver contours and the ground truth were on an average 88%–95% for CBCT images. Conclusion: The experiment demonstrated

  15. Functional Region Annotation of Liver CT Image Based on Vascular Tree

    PubMed Central

    Chen, Yufei; Wang, Gang

    2016-01-01

    Anatomical analysis of liver region is critical in diagnosis and treatment of liver diseases. The reports of liver region annotation are helpful for doctors to precisely evaluate liver system. One of the challenging issues is to annotate the functional regions of liver through analyzing Computed Tomography (CT) images. In this paper, we propose a vessel-tree-based liver annotation method for CT images. The first step of the proposed annotation method is to extract the liver region including vessels and tumors from the CT scans. And then a 3-dimensional thinning algorithm is applied to obtain the spatial skeleton and geometric structure of liver vessels. With the vessel skeleton, the topology of portal veins is further formulated by a directed acyclic graph with geometrical attributes. Finally, based on the topological graph, a hierarchical vascular tree is constructed to divide the liver into eight segments according to Couinaud classification theory and thereby annotate the functional regions. Abundant experimental results demonstrate that the proposed method is effective for precise liver annotation and helpful to support liver disease diagnosis. PMID:27891516

  16. Texture analysis with a new method in which the region of interest is segmented into multiple layers for radiofrequency amplitude histogram analysis of fibrous rat livers.

    PubMed

    Fujii, Yasutomo; Taniguchi, Nobuyuki; Takano, Ryuichi; Wang, Yi; Shigeta, Kouichiro; Omoto, Kiyoka; Ono, Tomoko; Satoh, Izumi; Itoh, Kouichi

    2004-03-01

    The aim of this study was to estimate the severity of fibrosis without a biopsy. The signal-to-noise ratio (SNR), skewness, and kurtosis were measured using a 10-MHz transducer with the texture analysis in conjunction with an alternative method for evaluating fibrous rat livers. This method segments the region of interest (ROI) into multiple layers (sub-ROIs). In each sub-ROI of a homogeneous medium, the histogram of enveloped amplitude of radiofrequency (RF) backscattered echoes resembles a Rayleigh distribution. In theory, SNR, skewness, and kurtosis for Rayleigh statistics are constant and independent of the mean scattering intensity, which is enhanced by such undesirable effects as tissue attenuation, beam diffraction, and incident waveforms. Thus, these values, which are averages of the corresponding sub-ROI values, constitute an unbiased estimator. All fibrous liver specimens were induced using the dimethylnitrosamine method. Fiber content was estimated quantitatively as the fibrosis index by computer processing of pathological images obtained by light microscopy. The SNR, skewness and kurtosis, expressed as averages of corresponding values from each sub-ROI, correlated closely with the fibrosis index. These results make it possible to predict the severity of liver fibrosis from data obtained without resorting to biopsy. The data, obtained from our earlier study on rats, may be used to evaluate human hepatitis quantitatively by measuring these three values. The method may make it possible to estimate the degree of severity of chronic liver disease noninvasively.

  17. Portal Vein Embolization in the Setting of Staged Hepatectomy with Preservation of Segment IV ± I Only for Bilobar Colorectal Liver Metastases: Safety, Efficacy, and Clinical Outcomes.

    PubMed

    Cassinotto, Christophe; Dohan, Anthony; Gallix, Benoît; Simoneau, Eve; Boucher, Louis-Martin; Metrakos, Peter; Cabrera, Tatiana; Torres, Carlos; Muchantef, Karl; Valenti, David A

    2017-07-01

    To assess frequency of adverse events, efficacy, and clinical outcomes of percutaneous portal vein embolization (PVE) in patients with bilobar colorectal liver metastases undergoing staged hepatectomy with preservation of segment IV ± I only. Retrospective analysis was performed of 40 consecutive patients who underwent right PVE after successful left lobectomy between 2005 and 2013. Rates of adverse events, future liver remnant (FLR) > 30% compared with baseline liver volume, clinical success (completion of staged hepatectomy with clearance of liver metastases), and overall survival were analyzed. PVE was performed using polyvinyl alcohol particles (n = 7; 17.5%), particles plus coils (n = 23; 57.5%), and N-butyl cyanoacrylate glue plus ethiodized oil (n = 10; 25%). Technical success was 100%. After PVE, 20% (n = 8) of patients exhibited portal venous thrombosis, ranging from isolated intrahepatic portal branch thrombosis to massive thrombosis of the main portal vein (n = 3) and responsible for periportal cavernoma and portal hypertension in 5 patients. Of patients, 23 (57.5%) had FLR ≥ 30%, and 21 (52.5%) had clinical success. Six patients had significant stenosis or occlusion of the left portal vein or biliary system after original left lobectomy, which was independently associated with FLR < 30% (R(2) = 0.24). Clinical success was the only independent variable associated with survival (R(2) = 0.25). PVE for staged hepatectomy with preservation of segment IV ± I only is technically feasible, leading to adequate hypertrophy and clinical success rates in these patients with poor oncologic prognosis. Portal venous thrombosis is greater after the procedure than in the setting of standard PVE. Copyright © 2017 SIR. Published by Elsevier Inc. All rights reserved.

  18. Computed analysis of three-dimensional cone-beam computed tomography angiography for determination of tumor-feeding vessels during chemoembolization of liver tumor: a pilot study.

    PubMed

    Deschamps, Frederic; Solomon, Stephen B; Thornton, Raymond H; Rao, Pramod; Hakime, Antoine; Kuoch, Viseth; de Baere, Thierry

    2010-12-01

    The purpose of this study was to evaluate computed analysis of three-dimensional (3D) cone-beam computed tomography angiography (CTA) of the liver for determination of subsegmental tumor-feeding vessels (FVs). Eighteen consecutive patients underwent transarterial chemoembolization (TACE) from January to October 2008 for 25 liver tumors (15 hepatocellular carcinomas [HCCs] and 10 neuroendocrine metastases). Anteroposterior projection angiogram (two-dimensional [2D]) and 3D cone-beam CTA images were acquired by injection of the common hepatic artery. Retrospectively, FVs were independently identified by three radiology technologists using a software package (S) that automatically determines FVs by analysis of 3D images. Subsequently, three interventional radiologists (IRs) independently identified FVs by reviewing the 2D images followed by examination of the 3D images. Finally, the "ground truth" for the number and location of FVs was obtained by consensus among the IRs, who were allowed to use any imaging-including 2D, 3D, and all oblique or selective angiograms-for such determination. Sensitivities, durations, and degrees of agreement for review of 2D, 3D, and S results were evaluated. Sensitivity of 3D (73%) was higher than 2D (64%) images for identification of FVs (P = 0.036). The sensitivity of S (93%) was higher than 2D (P = 0.02) and 3D (P = 0.005) imaging. The duration for review of 3D imaging was longer than that for 2D imaging (187 vs. 94 s, P = 0.0001) or for S (135 s, P = 0.0001). The degree of agreement between the IRs using 2D and 3D imaging were 54% and 62%, respectively, whereas it was 82% between the three radiology technologists using S. These preliminary data show that computed determination of FVs is both accurate and sensitive.

  19. Accurate quantitation of Ki67-positive proliferating hepatocytes in rabbit liver by a multicolor immunohistochemical (IHC) approach analyzed with automated tissue and cell segmentation software.

    PubMed

    van der Loos, Chris M; de Boer, Onno J; Mackaaij, Claire; Hoekstra, Lisette T; van Gulik, Thomas M; Verheij, Joanne

    2013-01-01

    Determination of hepatocyte proliferation activity is hampered by the presence of Ki67-positive non-parenchymal cells. We validated a multicolor immunohistochemical (IHC) approach using multispectral tissue and cell segmentation software. Portal vein branches to the cranial liver lobes of 10 rabbits were embolized, leading to atrophy of the cranial lobes and hyperplasia of the caudal lobes. Slides from cranial and caudal lobes (n=20) were double-stained (CK8+18 and Ki67) and triple-stained (CK8+18, Ki67, and CD31). The Ki67 proliferation index was calculated using automated tissue and cell segmentation software and compared with manual counting by two independent observers. A substantial variation was seen in the number of Ki67-positive hepatocytes in the different specimens in both double and triple staining (range, 0-50). Correlation coefficients between manual counting and the digital analysis were 0.76 for observer 1 (p<0.001) and 0.78 for observer 2 (p<0.001) with double staining and R(2) = 0.91 for observer 1 and R(2) = 0.89 for observer 2, p<0.001 with triple staining. In conclusion, in rabbit, the hepatocellular proliferation index can be reliably determined using automated tissue and cell segmentation software in combination with IHC multiple staining. Our findings may be useful in clinical practice when Ki67 proliferation index yields prognostic significance.

  20. Influence of coronary vessel dominance on short- and long-term outcome in patients after ST-segment elevation myocardial infarction.

    PubMed

    Veltman, Caroline E; van der Hoeven, Bas L; Hoogslag, Georgette E; Boden, Helèn; Kharbanda, Rohit K; de Graaf, Michiel A; Delgado, Victoria; van Zwet, Erik W; Schalij, Martin J; Bax, Jeroen J; Scholte, Arthur J H A

    2015-05-01

    Prognostic importance of coronary vessel dominance in patients with ST-elevation myocardial infarction (STEMI) remains uncertain. The aim of this study was to assess influence of coronary vessel dominance on the short- and long-term outcome after STEMI. Coronary angiographic images of consecutive patients presenting with first STEMI were retrospectively reviewed to assess coronary vessel dominance. Patients were followed after STEMI during a median period of 48 (IQR38-61) months for the occurrence of all-cause mortality and the composite of reinfarction and cardiac death. The population comprised 1131 patients of which 971 (86%) patients had a right dominant, 102 (9%) a left dominant, and 58 (5%) a balanced system. After 5 years of follow-up, the cumulative incidence of all-cause mortality was significantly higher in patients with a left dominant system, compared with a right dominant and balanced system (log-rank P = 0.013). Moreover, a left dominant system was an independent predictor for 30-day mortality (OR 2.51, 95% CI 1.11-5.67, P = 0.027) and the composite of reinfarction and cardiac death within 30-days after STEMI (OR 2.25, 95% CI 1.09-4.61, P = 0.028). In patients surviving first 30-days post-STEMI, coronary vessel dominance had no influence on long-term outcome. A left dominant coronary artery system is associated with a significantly increased risk of 30-day mortality and early reinfarction after STEMI. After surviving the first 30-days post-STEMI, coronary vessel dominance had no influence on long-term outcome. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

  1. Pressure vessel flex joint

    NASA Technical Reports Server (NTRS)

    Kahn, Jon B. (Inventor)

    1992-01-01

    An airtight, flexible joint is disclosed for the interfacing of two pressure vessels such as between the Space Station docking tunnel and the Space Shuttle Orbiter bulkhead adapter. The joint provides for flexibility while still retaining a structural link between the two vessels required due to the loading created by the internal/external pressure differential. The joint design provides for limiting the axial load carried across the joint to a specific value, a function returned in the Orbiter/Station tunnel interface. The flex joint comprises a floating structural segment which is permanently attached to one of the pressure vessels through the use of an inflatable seal. The geometric configuration of the joint causes the tension between the vessels created by the internal gas pressure to compress the inflatable seal. The inflation pressure of the seal is kept at a value above the internal/external pressure differential of the vessels in order to maintain a controlled distance between the floating segment and pressure vessel. The inflatable seal consists of either a hollow torus-shaped flexible bladder or two rolling convoluted diaphragm seals which may be reinforced by a system of straps or fabric anchored to the hard structures. The joint acts as a flexible link to allow both angular motion and lateral displacement while it still contains the internal pressure and holds the axial tension between the vessels.

  2. Vessel enhancing diffusion: a scale space representation of vessel structures.

    PubMed

    Manniesing, Rashindra; Viergever, Max A; Niessen, Wiro J

    2006-12-01

    A method is proposed to enhance vascular structures within the framework of scale space theory. We combine a smooth vessel filter which is based on a geometrical analysis of the Hessian's eigensystem, with a non-linear anisotropic diffusion scheme. The amount and orientation of diffusion depend on the local vessel likeliness. Vessel enhancing diffusion (VED) is applied to patient and phantom data and compared to linear, regularized Perona-Malik, edge and coherence enhancing diffusion. The method performs better than most of the existing techniques in visualizing vessels with varying radii and in enhancing vessel appearance. A diameter study on phantom data shows that VED least affects the accuracy of diameter measurements. It is shown that using VED as a preprocessing step improves level set based segmentation of the cerebral vasculature, in particular segmentation of the smaller vessels of the vasculature.

  3. Hepatic lesions segmentation in ultrasound nonlinear imaging

    NASA Astrophysics Data System (ADS)

    Kissi, Adelaide A.; Cormier, Stephane; Pourcelot, Leandre; Tranquart, Francois

    2005-04-01

    Doppler has been used for many years for cardiovascular exploration in order to visualize the vessels walls and anatomical or functional diseases. The use of ultrasound contrast agents makes it possible to improve ultrasonic information. Nonlinear ultrasound imaging highlights the detection of these agents within an organ and hence is a powerful technique to image perfusion of an organ in real-time. The visualization of flow and perfusion provides important information for the diagnosis of various diseases as well as for the detection of tumors. However, the images are buried in noise, the speckle, inherent in the image formation. Furthermore at portal phase, there is often an absence of clear contrast between lesions and surrounding tissues because the organ is filled with agents. In this context, we propose a new method of automatic liver lesions segmentation in nonlinear imaging sequences for the quantification of perfusion. Our method of segmentation is divided into two stages. Initially, we developed an anisotropic diffusion step which raised the structural characteristics to eliminate the speckle. Then, a fuzzy competitive clustering process allowed us to delineate liver lesions. This method has been used to detect focal hepatic lesions (metastasis, nodular hyperplasia, adenoma). Compared to medical expert"s report obtained on 15 varied lesions, the automatic segmentation allows us to identify and delineate focal liver lesions during the portal phase which high accuracy. Our results show that this method improves markedly the recognition of focal hepatic lesions and opens the way for future precise quantification of contrast enhancement.

  4. Optimal Elasticity cut-off value for discriminating Healthy to Pathological Fibrotic patients employing Fuzzy C-Means automatic segmentation in Liver Shear Wave Elastography images

    NASA Astrophysics Data System (ADS)

    Gatos, Ilias; Tsantis, Stavros; Skouroliakou, Aikaterini; Theotokas, Ioannis; Zoumpoulis, Pavlos S.; Kagadis, George C.

    2015-09-01

    The aim of the present study is to determine an optimal elasticity cut-off value for discriminating Healthy from Pathological fibrotic patients by means of Fuzzy C-Means automatic segmentation and maximum participation cluster mean value employment in Shear Wave Elastography (SWE) images. The clinical dataset comprised 32 subjects (16 Healthy and 16 histological or Fibroscan verified Chronic Liver Disease). An experienced Radiologist performed SWE measurement placing a region of interest (ROI) on each subject's right liver lobe providing a SWE image for each patient. Subsequently Fuzzy C-Means clustering was performed on every SWE image utilizing 5 clusters. Mean Stiffness value and pixels number of each cluster were calculated. The mean stiffness value feature of the cluster with maximum pixels number was then fed as input for ROC analysis. The selected Mean Stiffness value feature an Area Under the Curve (AUC) of 0.8633 with Optimum Cut-off value of 7.5 kPa with sensitivity and specificity values of 0.8438 and 0.875 and balanced accuracy of 0.8594. Examiner's classification measurements exhibited sensitivity, specificity and balanced accuracy value of 0.8125 with 7.1 kPa cutoff value. A new promising automatic algorithm was implemented with more objective criteria of defining optimum elasticity cut-off values for discriminating fibrosis stages for SWE. More subjects are needed in order to define if this algorithm is an objective tool to outperform manual ROI selection.

  5. Comparison of In-Hospital Mortality, Length of Stay, Postprocedural Complications, and Cost of Single-Vessel Versus Multivessel Percutaneous Coronary Intervention in Hemodynamically Stable Patients With ST-Segment Elevation Myocardial Infarction (from Nationwide Inpatient Sample [2006 to 2012]).

    PubMed

    Panaich, Sidakpal S; Arora, Shilpkumar; Patel, Nilay; Schreiber, Theodore; Patel, Nileshkumar J; Pandya, Bhavi; Gupta, Vishal; Grines, Cindy L; Deshmukh, Abhishek; Badheka, Apurva O

    2016-10-01

    The primary objective of our study was to evaluate the in-hospital outcomes in terms of mortality, procedural complications, hospitalization costs, and length of stay (LOS) after multivessel percutaneous coronary intervention (MVPCI) in hemodynamically stable patients with ST-segment elevation myocardial infarction (STEMI). The study cohort was derived from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample database, years 2006 to 2012. Percutaneous coronary interventions (PCI) performed during STEMI were identified using appropriate International Classification of Diseases, Ninth Revision, diagnostic and procedural codes. Patients in cardiogenic shock were excluded. Hierarchical mixed-effects logistic regression models were used for categorical dependent variables such as in-hospital mortality and composite of in-hospital mortality and complications, and hierarchical mixed-effects linear regression models were used for continuous dependent variables such as cost of hospitalization and LOS. We identified 106,317 (weighted n = 525,161) single-vessel PCI and 15,282 (weighted n = 74,543) MVPCIs. MVPCI (odds ratio, 95% confidence interval [CI], p value) was not associated with significant increase in in-hospital mortality (0.99, 0.85 to 1.15, 0.863) but predicted a higher composite end point of in-hospital mortality and postprocedural complications (1.09, 1.02 to 1.17, 0.013) compared to single-vessel PCI. MVPCI was also predictive of longer LOS (LOS +0.19 days, 95% CI +0.14 to +0.23 days, p <0.001) and higher hospitalization costs (cost +$4,445, 95% CI +$4,128 to +$4,762, p <0.001). MVPCI performed during STEMI in hemodynamically stable patients is associated with no increase in in-hospital mortality but a higher rate of postprocedural complications and longer LOS and greater hospitalization costs compared to single-vessel PCI.

  6. Automated segmentation of middle hepatic vein in non-contrast x-ray CT images based on an atlas-driven approach

    NASA Astrophysics Data System (ADS)

    Kitagawa, Teruhiko; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Yokoyama, Ryujiro; Kondo, Hiroshi; Kanematsu, Masayuki; Hoshi, Hiroaki

    2008-03-01

    In order to support the diagnosis of hepatic diseases, understanding the anatomical structures of hepatic lobes and hepatic vessels is necessary. Although viewing and understanding the hepatic vessels in contrast media-enhanced CT images is easy, the observation of the hepatic vessels in non-contrast X-ray CT images that are widely used for the screening purpose is difficult. We are developing a computer-aided diagnosis (CAD) system to support the liver diagnosis based on non-contrast X-ray CT images. This paper proposes a new approach to segment the middle hepatic vein (MHV), a key structure (landmark) for separating the liver region into left and right lobes. Extraction and classification of hepatic vessels are difficult in non-contrast X-ray CT images because the contrast between hepatic vessels and other liver tissues is low. Our approach uses an atlas-driven method by the following three stages. (1) Construction of liver atlases of left and right hepatic lobes using a learning datasets. (2) Fully-automated enhancement and extraction of hepatic vessels in liver regions. (3) Extraction of MHV based on the results of (1) and (2). The proposed approach was applied to 22 normal liver cases of non-contrast X-ray CT images. The preliminary results show that the proposed approach achieves the success in 14 cases for MHV extraction.

  7. Percutaneous segmental dilatation of portal stenosis after paediatric liver transplantation to avoid or postpone surgery: two cases and literature review.

    PubMed

    Fonio, Paolo; Righi, Dorigo; Discalzi, Andrea; Calandri, Marco; Faletti, Riccardo; Brunati, Andrea; Gandini, Giovanni

    2014-12-01

    The authors retrospectively reviewed the results obtained with percutaneous treatment of portal stenosis. In November 2005 and March 2008, two patients, 15 and 32 months old, underwent portal vein angioplasty at our centre. Both procedures were performed after ultrasound-guided portal vein puncture and measurement of pre- and postanastomotic pressure gradients. The diameters of the angioplasty catheters ranged from 5 to 10 mm and no stents were used. In both cases, it was possible to cross the stenoses, perform angioplasty and obtain an immediate reduction of the pressure gradients. There were no major complications after the procedure. In the first patient, percutaneous treatment allowed us to postpone surgical revision of the anastomosis; in the second case, angioplasty had to be repeated twice over a period of 4 years to finally achieve regular patency of the anastomosis and function of the graft. Percutaneous treatment of portal stenosis after paediatric liver transplantation is a safe and feasible treatment; if balloon dilatation does not guarantee functional recovery of the organ, it allows surgical revision to be postponed to a later date when the clinical condition is more stable.

  8. Automated 3D vascular segmentation in CT hepatic venography

    NASA Astrophysics Data System (ADS)

    Fetita, Catalin; Lucidarme, Olivier; Preteux, Francoise

    2005-08-01

    In the framework of preoperative evaluation of the hepatic venous anatomy in living-donor liver transplantation or oncologic rejections, this paper proposes an automated approach for the 3D segmentation of the liver vascular structure from 3D CT hepatic venography data. The developed segmentation approach takes into account the specificities of anatomical structures in terms of spatial location, connectivity and morphometric properties. It implements basic and advanced morphological operators (closing, geodesic dilation, gray-level reconstruction, sup-constrained connection cost) in mono- and multi-resolution filtering schemes in order to achieve an automated 3D reconstruction of the opacified hepatic vessels. A thorough investigation of the venous anatomy including morphometric parameter estimation is then possible via computer-vision 3D rendering, interaction and navigation capabilities.

  9. Vesselness-guided Active Contour: A Coronary Vessel Extraction Method

    PubMed Central

    Dehkordi, Maryam Taghizadeh; Jalalat, Morteza; Sadri, Saeed; Doosthoseini, Alimohamad; Ahmadzadeh, Mohammad Reza; Amirfattahi, Rasoul

    2014-01-01

    Vessel extraction is a critical task in clinical practice. In this paper, we propose a new approach for vessel extraction using an active contour model by defining a novel vesselness-based term, based on accurate analysis of the vessel structure in the image. To achieve the novel term, a simple and fast directional filter bank is proposed, which does not employ down sampling and resampling used in earlier versions of directional filter banks. The proposed model not only preserves the performance of the existing models on images with intensity inhomogeneity, but also overcomes their inability both to segment low contrast vessels and to omit non-vessel structures. Experimental results for synthetic images and coronary X-ray angiograms show desirable performance of our model. PMID:24761379

  10. Blood Vessel Tension Tester

    NASA Technical Reports Server (NTRS)

    1978-01-01

    In the photo, a medical researcher is using a specially designed laboratory apparatus for measuring blood vessel tension. It was designed by Langley Research Center as a service to researchers of Norfolk General Hospital and Eastern Virginia Medical School, Norfolk, Virginia. The investigators are studying how vascular smooth muscle-muscle in the walls of blood vessels-reacts to various stimulants, such as coffee, tea, alcohol or drugs. They sought help from Langley Research Center in devising a method of measuring the tension in blood vessel segments subjected to various stimuli. The task was complicated by the extremely small size of the specimens to be tested, blood vessel "loops" resembling small rubber bands, some only half a millimeter in diameter. Langley's Instrumentation Development Section responded with a miniaturized system whose key components are a "micropositioner" for stretching a length of blood vessel and a strain gage for measuring the smooth muscle tension developed. The micropositioner is a two-pronged holder. The loop of Mood vessel is hooked over the prongs and it is stretched by increasing the distance between the prongs in minute increments, fractions of a millimeter. At each increase, the tension developed is carefully measured. In some experiments, the holder and specimen are lowered into the test tubes shown, which contain a saline solution simulating body fluid; the effect of the compound on developed tension is then measured. The device has functioned well and the investigators say it has saved several months research time.

  11. Optimal Timing of Percutaneous Coronary Intervention for Nonculprit Vessel in Patients with ST-Segment Elevation Myocardial Infarction and Multivessel Disease

    PubMed Central

    Kim, Inna; Kim, Min Chul; Jeong, Hae Chang; Park, Keun Ho; Sim, Doo Sun; Hong, Young Joon; Kim, Ju Han; Jeong, Myung Ho; Cho, Jeong Gwan; Park, Jong Chun; Seung, Ki-Bae; Chang, Kiyuk

    2017-01-01

    Background and Objectives In patients with ST-segment elevation myocardial infarction (STEMI) and multivessel disease (MVD), the optimal timing of staged percutaneous coronary intervention (PCI) remains unclear. Subjects and Methods This study was a retrospective analysis of 753 STEMI patients with MVD who were treated by multivessel PCI in the Convergent Registry of Catholic and Chonnam University for Acute myocardial infarction (MI). Patients were divided into 3 groups according to the time from initial to staged PCI: group 1 (n=316, multivessel PCI performed during the index procedure), group 2 (n=360, staged PCI within 1 week), and group 3 (n=77, staged PCI after 1 week). The endpoint was major adverse cardiac events (MACEs), including all-cause mortality, non-fatal MI, and repeat PCI during 3.4 years follow-up. Results The incidence of composite MACEs was higher in group 3 than in group 1 (odds ratio [OR]: 1.83, 95% confidence interval [CI]: 1.06 to 3.18, p=0.031). However, the risk of MACEs in groups 1 and 2 was comparable (OR: 1.01, 95% CI: 0.70 to 1.46, p=0.950). In multivariate logistic regression, independent predictors of 3-year MACEs were high Killip class (OR: 2.72, 95% CI: 1.38 to 5.37, p=0.004), left ventricular ejection fraction <45% (OR: 1.57, 95% CI: 1.06 to 2.32, p=0.024), and group 3 (OR: 1.83, 95% CI: 1.06 to 3.18, p=0.009). Conclusion Deferred staged PCI after one week index PCI was associated with the highest MACE, as compared to both simultaneous multivessel PCI and early staged PCI <1 week. PMID:28154589

  12. Infarct related artery only versus complete revascularization in ST-segment elevation myocardial infarction and multi vessel disease: a meta-analysis

    PubMed Central

    Devarapally, Santhosh R.; Arora, Sameer

    2017-01-01

    Background The 2015 American College of Cardiology Foundation/American Heart Association (ACCF/AHA) focused update on primary percutaneous coronary intervention (PCI) for patients with ST-segment elevation myocardial infarction (STEMI) only gives a class II b (weak) indication for non-infarct artery intervention at the time of primary PCI. Recent randomized controlled trials, however, suggest strong evidence supporting complete revascularization. Methods A systematic search was conducted in PUBMED, MEDLINE, EMBASE and Cochrane central register for randomized controlled trials comparing complete versus infarct artery (IRA) only revascularization in patients with STEMI. A meta-analysis was performed using the data extracted from each study. Summary risk ratios (RR) and 95% confidence intervals (CI) were calculated for five outcomes. Results Six trials fulfilled the inclusion criteria yielding 1,792 patients. Follow up ranged from 6 months to 2.5 years. The incidence of major adverse cardiac events (MACE) was significantly lower in the complete revascularization group compared to the IRA only revascularization (13.8% vs. 25.1%, RR =0.51; 95% CI: 0.41–0.64, P<0.00001). It was attributed to significantly lower repeat revascularization rate in the complete revascularization group (8.2% vs. 18.9%, RR =0.41; 95% CI: 0.31–0.54, P<0.00001). This meta-analysis also showed a significant reduction in cardiovascular mortality (2.0% vs. 4.6%, RR =0.42; 95% CI: 0.24–0.74; P=0.003), non-fatal myocardial infarction (4.37% vs. 5.76%, RR =0.64; 95% CI: 0.34–1.20; P=0.16) and all-cause mortality rates [(4.6% vs. 6%), RR =0.75; 95% CI: 0.49–1.14, P=0.17] in the complete revascularization group, compared to the IRA revascularization group. Conclusions In patients who present with STEMI, complete revascularization is associated with lower rates of MACE and cardiovascular deaths as compared to revascularization of the IRA alone. Even though the outcomes of all-cause mortality and

  13. Application of fuzzy connectedness in 3D blood vessel extraction.

    PubMed

    Lv, Xinrong; Zou, Hua

    2010-01-01

    Three-dimensional (3D) segmentation of blood vessels plays a very important role in solving some practical problems such as diagnosis of vessels diseases. Because of the effective segmentation for 2D images, the fuzzy connectedness segmentation method is introduced to extract vascular structures from 3D blood vessel volume dataset. In the experiments, three segmentation methods including thresholding method, region growing method and fuzzy connectedness method are all used to extract the vascular structures, and their results are compared. The results indicate that fuzzy connectedness method is better than thresholding method in connectivity of segmentation results, and better than region growing method in precision of segmentation results.

  14. Current role of bloodless liver resection

    PubMed Central

    Delis, Spiros G; Madariaga, Juan; Bakoyiannis, A; Dervenis, Ch

    2007-01-01

    Liver resections are demanding operations which can have life threatening complications although they are performed by experienced liver surgeons. Recently new technologies are applied in the field of liver surgery, having one goal: safer and easier liver operations. The aim of this article is to address the issue of bloodless liver resection using radiofrequency energy. Radionics, Cool-tipTM System and Tissue Link are some of the devices which are using radiofrequency energy. All information included in this article, refers to these devices in which we have personal experience in our unit of liver surgery. These devices take advantage of its unique combination of radiofrequency current and internal electrode cooling to perform sealing of the small vessels and biliary radicals. Dissection is also feasible with the cool-tip probe. For the purposes of this study patient sex, age, type of disease and type of surgical procedure in association with the duration of parenchymal transection, blood loss, length of hospital stay, morbidity and mortality were analyzed. Cool-tip RF device may provide a unique, simple and rather safe method of bloodless liver resections if used properly. It is indicated mostly in cirrhotic patients with challenging hepatectomies (segment VIII, central resections). The total operative time is eliminated and the average blood loss is significantly decreased. It is important to note that this technique should not be applied near the hilum or the vena cava to avoid damage of these structures. PMID:17352009

  15. Current role of bloodless liver resection.

    PubMed

    Delis, Spiros G; Madariaga, Juan; Bakoyiannis, A; Dervenis, Ch

    2007-02-14

    Liver resections are demanding operations which can have life threatening complications although they are performed by experienced liver surgeons. Recently new technologies are applied in the field of liver surgery, having one goal: safer and easier liver operations. The aim of this article is to address the issue of bloodless liver resection using radiofrequency energy. Radionics, Cool-tip System and Tissue Link are some of the devices which are using radiofrequency energy. All information included in this article, refers to these devices in which we have personal experience in our unit of liver surgery. These devices take advantage of its unique combination of radiofrequency current and internal electrode cooling to perform sealing of the small vessels and biliary radicals. Dissection is also feasible with the cool-tip probe. For the purposes of this study patient sex, age, type of disease and type of surgical procedure in association with the duration of parenchymal transection, blood loss, length of hospital stay, morbidity and mortality were analyzed. Cool-tip RF device may provide a unique, simple and rather safe method of bloodless liver resections if used properly. It is indicated mostly in cirrhotic patients with challenging hepatectomies (segment VIII, central resections). The total operative time is eliminated and the average blood loss is significantly decreased. It is important to note that this technique should not be applied near the hilum or the vena cava to avoid damage of these structures.

  16. 2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection

    PubMed Central

    Raptis, Sotirios; Koutsouris, Dimitris

    2010-01-01

    The paper addresses the fine retinal-vessel's detection issue that is faced in diagnostic applications and aims at assisting in better recognizing fine vessel anomalies in 2D. Our innovation relies in separating key visual features vessels exhibit in order to make the diagnosis of eventual retinopathologies easier to detect. This allows focusing on vessel segments which present fine changes detectable at different sampling scales. We advocate that these changes can be addressed as subsequent stages of the same vessel detection procedure. We first carry out an initial estimate of the basic vessel-wall's network, define the main wall-body, and then try to approach the ridges and branches of the vasculature's using fine detection. Fine vessel screening looks into local structural inconsistencies in vessels properties, into noise, or into not expected intensity variations observed inside pre-known vessel-body areas. The vessels are first modelled sufficiently but not precisely by their walls with a tubular model-structure that is the result of an initial segmentation. This provides a chart of likely Vessel Wall Pixels (VWPs) yielding a form of a likelihood vessel map mainly based on gradient filter's intensity and spatial arrangement parameters (e.g., linear consistency). Specific vessel parameters (centerline, width, location, fall-away rate, main orientation) are post-computed by convolving the image with a set of pre-tuned spatial filters called Matched Filters (MFs). These are easily computed as Gaussian-like 2D forms that use a limited range sub-optimal parameters adjusted to the dominant vessel characteristics obtained by Spatial Grey Level Difference statistics limiting the range of search into vessel widths of 16, 32, and 64 pixels. Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing. Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions

  17. Augmented-reality-based segmentation refinement

    NASA Astrophysics Data System (ADS)

    Bornik, Alexander; Reitinger, Bernhard; Beichel, Reinhard; Sorantin, Erich; Werkgartner, Georg

    2004-05-01

    Planning of surgical liver tumor resections based on image data from X-ray computed tomography requires correct segmentation of the liver, liver vasculature and pathological structures. Automatic liver segmentation methods frequently fail in cases where the anatomy is degenerated by lesions or other present liver diseases. On the other hand performing a manual segmentation is a tedious and time consuming task. Therefore Augmented Reality based segmentation refinement tools are reported, that aid radiologists to efficiently correct incorrect segmentations in true 3D using head-mounted displays and tracked input devices. The developed methods facilitate segmentation refinement by interactively deforming a mesh data structure reconstructed from an initial segmentation. The variety of refinement methods are all accessible through the intuitive, direct 3D user interface of an Augmented Reality system.

  18. Probabilistic atlas based labeling of the cerebral vessel tree

    NASA Astrophysics Data System (ADS)

    Van de Giessen, Martijn; Janssen, Jasper P.; Brouwer, Patrick A.; Reiber, Johan H. C.; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke

    2015-03-01

    Preoperative imaging of the cerebral vessel tree is essential for planning therapy on intracranial stenoses and aneurysms. Usually, a magnetic resonance angiography (MRA) or computed tomography angiography (CTA) is acquired from which the cerebral vessel tree is segmented. Accurate analysis is helped by the labeling of the cerebral vessels, but labeling is non-trivial due to anatomical topological variability and missing branches due to acquisition issues. In recent literature, labeling the cerebral vasculature around the Circle of Willis has mainly been approached as a graph-based problem. The most successful method, however, requires the definition of all possible permutations of missing vessels, which limits application to subsets of the tree and ignores spatial information about the vessel locations. This research aims to perform labeling using probabilistic atlases that model spatial vessel and label likelihoods. A cerebral vessel tree is aligned to a probabilistic atlas and subsequently each vessel is labeled by computing the maximum label likelihood per segment from label-specific atlases. The proposed method was validated on 25 segmented cerebral vessel trees. Labeling accuracies were close to 100% for large vessels, but dropped to 50-60% for small vessels that were only present in less than 50% of the set. With this work we showed that using solely spatial information of the vessel labels, vessel segments from stable vessels (>50% presence) were reliably classified. This spatial information will form the basis for a future labeling strategy with a very loose topological model.

  19. Coexisting bicuspid aortic and pulmonary valves with normally related great vessels diagnosed by live/real time three-dimensional transesophageal echocardiography.

    PubMed

    Kemaloğlu Öz, Tuğba; Karadeniz, Fatma Özpamuk; Gundlapalli, Hareesh; Erer, Betul; Sharma, Rohit K; Ahmed, Mustafa; Nanda, Navin C; Yıldırım, Aydın; Orhan, Gökçen; Öz, Ayhan; Eren, Mehmet

    2014-02-01

    Coexistence of bicuspid aortic and pulmonary valves in the same patient is a very rare entity identified mainly during surgery and postmortem. To the best of our knowledge, only one case has been diagnosed by two-dimensional echocardiography in a newborn with malposition of the great arteries but no images were presented. Here, we are reporting the first case of bicuspid pulmonary and aortic valves diagnosed by live/real time three-dimensional transesophageal echocardiography in an adult with normally related great arteries. © 2014, Wiley Periodicals, Inc.

  20. Accuracy of estimation of graft size for living-related liver transplantation: first results of a semi-automated interactive software for CT-volumetry.

    PubMed

    Mokry, Theresa; Bellemann, Nadine; Müller, Dirk; Lorenzo Bermejo, Justo; Klauß, Miriam; Stampfl, Ulrike; Radeleff, Boris; Schemmer, Peter; Kauczor, Hans-Ulrich; Sommer, Christof-Matthias

    2014-01-01

    To evaluate accuracy of estimated graft size for living-related liver transplantation using a semi-automated interactive software for CT-volumetry. Sixteen donors for living-related liver transplantation (11 male; mean age: 38.2±9.6 years) underwent contrast-enhanced CT prior to graft removal. CT-volumetry was performed using a semi-automated interactive software (P), and compared with a manual commercial software (TR). For P, liver volumes were provided either with or without vessels. For TR, liver volumes were provided always with vessels. Intraoperative weight served as reference standard. Major study goals included analyses of volumes using absolute numbers, linear regression analyses and inter-observer agreements. Minor study goals included the description of the software workflow: degree of manual correction, speed for completion, and overall intuitiveness using five-point Likert scales: 1--markedly lower/faster/higher for P compared with TR, 2--slightly lower/faster/higher for P compared with TR, 3--identical for P and TR, 4--slightly lower/faster/higher for TR compared with P, and 5--markedly lower/faster/higher for TR compared with P. Liver segments II/III, II-IV and V-VIII served in 6, 3, and 7 donors as transplanted liver segments. Volumes were 642.9±368.8 ml for TR with vessels, 623.8±349.1 ml for P with vessels, and 605.2±345.8 ml for P without vessels (P<0.01). Regression equations between intraoperative weights and volumes were y = 0.94x+30.1 (R2 = 0.92; P<0.001) for TR with vessels, y = 1.00x+12.0 (R2 = 0.92; P<0.001) for P with vessels, and y = 1.01x+28.0 (R2 = 0.92; P<0.001) for P without vessels. Inter-observer agreement showed a bias of 1.8 ml for TR with vessels, 5.4 ml for P with vessels, and 4.6 ml for P without vessels. For the degree of manual correction, speed for completion and overall intuitiveness, scale values were 2.6±0.8, 2.4±0.5 and 2. CT-volumetry performed with P can predict accurately graft size for

  1. Accuracy of Estimation of Graft Size for Living-Related Liver Transplantation: First Results of a Semi-Automated Interactive Software for CT-Volumetry

    PubMed Central

    Mokry, Theresa; Bellemann, Nadine; Müller, Dirk; Lorenzo Bermejo, Justo; Klauß, Miriam; Stampfl, Ulrike; Radeleff, Boris; Schemmer, Peter; Kauczor, Hans-Ulrich; Sommer, Christof-Matthias

    2014-01-01

    Objectives To evaluate accuracy of estimated graft size for living-related liver transplantation using a semi-automated interactive software for CT-volumetry. Materials and Methods Sixteen donors for living-related liver transplantation (11 male; mean age: 38.2±9.6 years) underwent contrast-enhanced CT prior to graft removal. CT-volumetry was performed using a semi-automated interactive software (P), and compared with a manual commercial software (TR). For P, liver volumes were provided either with or without vessels. For TR, liver volumes were provided always with vessels. Intraoperative weight served as reference standard. Major study goals included analyses of volumes using absolute numbers, linear regression analyses and inter-observer agreements. Minor study goals included the description of the software workflow: degree of manual correction, speed for completion, and overall intuitiveness using five-point Likert scales: 1–markedly lower/faster/higher for P compared with TR, 2–slightly lower/faster/higher for P compared with TR, 3–identical for P and TR, 4–slightly lower/faster/higher for TR compared with P, and 5–markedly lower/faster/higher for TR compared with P. Results Liver segments II/III, II–IV and V–VIII served in 6, 3, and 7 donors as transplanted liver segments. Volumes were 642.9±368.8 ml for TR with vessels, 623.8±349.1 ml for P with vessels, and 605.2±345.8 ml for P without vessels (P<0.01). Regression equations between intraoperative weights and volumes were y = 0.94x+30.1 (R2 = 0.92; P<0.001) for TR with vessels, y = 1.00x+12.0 (R2 = 0.92; P<0.001) for P with vessels, and y = 1.01x+28.0 (R2 = 0.92; P<0.001) for P without vessels. Inter-observer agreement showed a bias of 1.8 ml for TR with vessels, 5.4 ml for P with vessels, and 4.6 ml for P without vessels. For the degree of manual correction, speed for completion and overall intuitiveness, scale values were 2.6±0.8, 2.4±0.5 and 2. Conclusions CT

  2. Retinal blood vessels extraction using probabilistic modelling.

    PubMed

    Kaba, Djibril; Wang, Chuang; Li, Yongmin; Salazar-Gonzalez, Ana; Liu, Xiaohui; Serag, Ahmed

    2014-01-01

    The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.

  3. Intraoperative augmented reality for minimally invasive liver interventions

    NASA Astrophysics Data System (ADS)

    Scheuering, Michael; Schenk, Andrea; Schneider, Armin; Preim, Bernhard; Greiner, Guenther

    2003-05-01

    Minimally invasive liver interventions demand a lot of experience due to the limited access to the field of operation. In particular, the correct placement of the trocar and the navigation within the patient's body are hampered. In this work, we present an intraoperative augmented reality system (IARS) that directly projects preoperatively planned information and structures extracted from CT data, onto the real laparoscopic video images. Our system consists of a preoperative planning tool for liver surgery and an intraoperative real time visualization component. The planning software takes into account the individual anatomy of the intrahepatic vessels and determines the vascular territories. Methods for fast segmentation of the liver parenchyma, of the intrahepatic vessels and of liver lesions are provided. In addition, very efficient algorithms for skeletonization and vascular analysis allowing the approximation of patient-individual liver vascular territories are included. The intraoperative visualization is based on a standard graphics adapter for hardware accelerated high performance direct volume rendering. The preoperative CT data is rigidly registered to the patient position by the use of fiducials that are attached to the patient's body, and anatomical landmarks in combination with an electro-magnetic navigation system. Our system was evaluated in vivo during a minimally invasive intervention simulation in a swine under anesthesia.

  4. Liver metastases

    MedlinePlus

    Metastases to the liver; Metastatic liver cancer; Liver cancer - metastatic; Colorectal cancer - liver metastases; Colon cancer - liver metastases; Esophageal cancer - liver metastases; Lung cancer - liver metastases; Melanoma - liver metastases

  5. Segmental approach to imaging of congenital heart disease.

    PubMed

    Lapierre, Chantale; Déry, Julie; Guérin, Ronald; Viremouneix, Loïc; Dubois, Josée; Garel, Laurent

    2010-03-01

    The segmental approach, which is widely used in the imaging work-up of congenital heart disease, consists of a three-step evaluation of the cardiac anatomy. In step 1, the visceroatrial situs is determined. Visceroatrial situs refers to the position of the atria in relation to the nearby anatomy (including the stomach, liver, spleen, and bronchi). Three different anatomic configurations may be observed: situs solitus (normal), situs inversus (inverted), or situs ambiguus (ambiguous). In step 2, the left- or rightward orientation of the ventricular loop is evaluated, and the positions of the ventricles are identified on the basis of their internal morphologic features. In step 3, the position of the great vessels is determined first, and any abnormalities are noted. Abnormalities in the origin of the great vessels, or conotruncal anomalies, are predominantly of three types: D-transposition (dextrotransposition), L-transposition (levotransposition), and D-malposition with double outlet right ventricle. Next, the relationships between the atria and ventricles and the ventricles and great vessels are determined at two levels: atrioventricular (concordant, discordant, ambiguous, double inlet, absence of right or left connection) and ventriculoarterial (concordant, discordant, double outlet). Last, a search is performed for any associated abnormalities of the cardiac chambers, septa, outflow tract, and great vessels. By executing these steps sequentially during image review, the radiologist can achieve a more accurate interpretation. Multiplanar reconstructions of cross-sectional image data obtained with computed tomography or magnetic resonance imaging are particularly useful for evaluating congenital heart disease.

  6. Multiresolution retinal vessel tracker based on directional smoothing

    NASA Astrophysics Data System (ADS)

    Englmeier, Karl-Hans; Bichler, Simon; Schmid, K.; Maurino, M.; Porta, Massimo; Bek, Toke; Ege, B.; Larsen, Ole V.; Hejlesen, Ok

    2002-04-01

    To support ophthalmologists in their routine and enable the quantitative assessment of vascular changes in color fundus photographs a multi-resolution approach was developed which segments the vessel tree efficiently and precisely in digital images of the retina. The algorithm starts at seed points, found in a preprocessing step and then follows the vessel, iteratively adjusting the direction of the search, and finding the center line of the vessels. As an addition, vessel branches and crossings are detected and stored in detailed lists. Every iteration of the Directional Smoothing Based (DSB) tracking process starts at a given point in the middle of a vessel. First rectangular windows for several directions in a neighborhood of this point are smoothed in the assumed direction of the vessel. The window, that results in the best contrast is then said to have the true direction of the vessel. The center point is moved into that direction 1/8th of the vessel width, and the algorithm continues with the next iteration. The vessel branch and crossing detection uses a list with unique vessel segment IDs and branch point IDs. During the tracking, when another vessel is crossed, the tracking is stopped. The newly traced vessel segment is stored in the vessel segment list, and the vessel, that had been traced before is broken up at the crossing- or branch point, and is stored as two different vessel segments. This approach has several advantages: - With directional smoothing, noise is eliminated, while the edges of the vessels are kept. - DSB works on high resolution images (3000 x 2000 pixel) as well as on low-resolution images (900 x 600 pixel), because a large area of the vessel is used to find the vessel direction - For the detection of venous beading the vessel width is measured for every step of the traced vessel. - With the lists of branch- and crossing points, we get a network of connected vessel segments, that can be used for further processing the retinal vessel

  7. Hepatic Arterial Configuration in Relation to the Segmental Anatomy of the Liver; Observations on MDCT and DSA Relevant to Radioembolization Treatment

    SciTech Connect

    Hoven, Andor F. van den Leeuwen, Maarten S. van Lam, Marnix G. E. H. Bosch, Maurice A. A. J. van den

    2015-02-15

    PurposeCurrent anatomical classifications do not include all variants relevant for radioembolization (RE). The purpose of this study was to assess the individual hepatic arterial configuration and segmental vascularization pattern and to develop an individualized RE treatment strategy based on an extended classification.MethodsThe hepatic vascular anatomy was assessed on MDCT and DSA in patients who received a workup for RE between February 2009 and November 2012. Reconstructed MDCT studies were assessed to determine the hepatic arterial configuration (origin of every hepatic arterial branch, branching pattern and anatomical course) and the hepatic segmental vascularization territory of all branches. Aberrant hepatic arteries were defined as hepatic arterial branches that did not originate from the celiac axis/CHA/PHA. Early branching patterns were defined as hepatic arterial branches originating from the celiac axis/CHA.ResultsThe hepatic arterial configuration and segmental vascularization pattern could be assessed in 110 of 133 patients. In 59 patients (54 %), no aberrant hepatic arteries or early branching was observed. Fourteen patients without aberrant hepatic arteries (13 %) had an early branching pattern. In the 37 patients (34 %) with aberrant hepatic arteries, five also had an early branching pattern. Sixteen different hepatic arterial segmental vascularization patterns were identified and described, differing by the presence of aberrant hepatic arteries, their respective vascular territory, and origin of the artery vascularizing segment four.ConclusionsThe hepatic arterial configuration and segmental vascularization pattern show marked individual variability beyond well-known classifications of anatomical variants. We developed an individualized RE treatment strategy based on an extended anatomical classification.

  8. Vessel discoloration detection in malarial retinopathy

    NASA Astrophysics Data System (ADS)

    Agurto, C.; Nemeth, S.; Barriga, S.; Soliz, P.; MacCormick, I.; Taylor, T.; Harding, S.; Lewallen, S.; Joshi, V.

    2016-03-01

    Cerebral malaria (CM) is a life-threatening clinical syndrome associated with malarial infection. It affects approximately 200 million people, mostly sub-Saharan African children under five years of age. Malarial retinopathy (MR) is a condition in which lesions such as whitening and vessel discoloration that are highly specific to CM appear in the retina. Other unrelated diseases can present with symptoms similar to CM, therefore the exact nature of the clinical symptoms must be ascertained in order to avoid misdiagnosis, which can lead to inappropriate treatment and, potentially, death. In this paper we outline the first system to detect the presence of discolored vessels associated with MR as a means to improve the CM diagnosis. We modified and improved our previous vessel segmentation algorithm by incorporating the `a' channel of the CIELab color space and noise reduction. We then divided the segmented vasculature into vessel segments and extracted features at the wall and in the centerline of the segment. Finally, we used a regression classifier to sort the segments into discolored and not-discolored vessel classes. By counting the abnormal vessel segments in each image, we were able to divide the analyzed images into two groups: normal and presence of vessel discoloration due to MR. We achieved an accuracy of 85% with sensitivity of 94% and specificity of 67%. In clinical practice, this algorithm would be combined with other MR retinal pathology detection algorithms. Therefore, a high specificity can be achieved. By choosing a different operating point in the ROC curve, our system achieved sensitivity of 67% with specificity of 100%.

  9. Construction of Realistic Liver Phantoms from Patient Images using 3D Printer and Its Application in CT Image Quality Assessment

    PubMed Central

    Leng, Shuai; Yu, Lifeng; Vrieze, Thomas; Kuhlmann, Joel; Chen, Baiyu; McCollough, Cynthia H.

    2016-01-01

    The purpose of this study is to use 3D printing techniques to construct a realistic liver phantom with heterogeneous background and anatomic structures from patient CT images, and to use the phantom to assess image quality with filtered backprojection and iterative reconstruction algorithms. Patient CT images were segmented into liver tissues, contrast-enhanced vessels, and liver lesions using commercial software, based on which stereolithography (STL) files were created and sent to a commercial 3D printer. A 3D liver phantom was printed after assigning different printing materials to each object to simulate appropriate attenuation of each segmented object. As high opacity materials are not available for the printer, we printed hollow vessels and filled them with iodine solutions of adjusted concentration to represent enhance levels in contrast-enhanced liver scans. The printed phantom was then placed in a 35×26 cm oblong-shaped water phantom and scanned repeatedly at 4 dose levels. Images were reconstructed using standard filtered backprojection and an iterative reconstruction algorithm with 3 different strength settings. Heterogeneous liver background were observed from the CT images and the difference in CT numbers between lesions and background were representative for low contrast lesions in liver CT studies. CT numbers in vessels filled with iodine solutions represented the enhancement of liver arteries and veins. Images were run through a Channelized Hotelling model observer with Garbor channels and ROC analysis was performed. The AUC values showed performance improvement using the iterative reconstruction algorithm and the amount of improvement increased with strength setting. PMID:27721555

  10. Construction of Realistic Liver Phantoms from Patient Images using 3D Printer and Its Application in CT Image Quality Assessment.

    PubMed

    Leng, Shuai; Yu, Lifeng; Vrieze, Thomas; Kuhlmann, Joel; Chen, Baiyu; McCollough, Cynthia H

    2015-01-01

    The purpose of this study is to use 3D printing techniques to construct a realistic liver phantom with heterogeneous background and anatomic structures from patient CT images, and to use the phantom to assess image quality with filtered backprojection and iterative reconstruction algorithms. Patient CT images were segmented into liver tissues, contrast-enhanced vessels, and liver lesions using commercial software, based on which stereolithography (STL) files were created and sent to a commercial 3D printer. A 3D liver phantom was printed after assigning different printing materials to each object to simulate appropriate attenuation of each segmented object. As high opacity materials are not available for the printer, we printed hollow vessels and filled them with iodine solutions of adjusted concentration to represent enhance levels in contrast-enhanced liver scans. The printed phantom was then placed in a 35×26 cm oblong-shaped water phantom and scanned repeatedly at 4 dose levels. Images were reconstructed using standard filtered backprojection and an iterative reconstruction algorithm with 3 different strength settings. Heterogeneous liver background were observed from the CT images and the difference in CT numbers between lesions and background were representative for low contrast lesions in liver CT studies. CT numbers in vessels filled with iodine solutions represented the enhancement of liver arteries and veins. Images were run through a Channelized Hotelling model observer with Garbor channels and ROC analysis was performed. The AUC values showed performance improvement using the iterative reconstruction algorithm and the amount of improvement increased with strength setting.

  11. Construction of realistic liver phantoms from patient images using 3D printer and its application in CT image quality assessment

    NASA Astrophysics Data System (ADS)

    Leng, Shuai; Yu, Lifeng; Vrieze, Thomas; Kuhlmann, Joel; Chen, Baiyu; McCollough, Cynthia H.

    2015-03-01

    The purpose of this study is to use 3D printing techniques to construct a realistic liver phantom with heterogeneous background and anatomic structures from patient CT images, and to use the phantom to assess image quality with filtered back-projection and iterative reconstruction algorithms. Patient CT images were segmented into liver tissues, contrast-enhanced vessels, and liver lesions using commercial software, based on which stereolithography (STL) files were created and sent to a commercial 3D printer. A 3D liver phantom was printed after assigning different printing materials to each object to simulate appropriate attenuation of each segmented object. As high opacity materials are not available for the printer, we printed hollow vessels and filled them with iodine solutions of adjusted concentration to represent enhance levels in contrast-enhanced liver scans. The printed phantom was then placed in a 35×26 cm oblong-shaped water phantom and scanned repeatedly at 4 dose levels. Images were reconstructed using standard filtered back-projection and an iterative reconstruction algorithm with 3 different strength settings. Heterogeneous liver background were observed from the CT images and the difference in CT numbers between lesions and background were representative for low contrast lesions in liver CT studies. CT numbers in vessels filled with iodine solutions represented the enhancement of liver arteries and veins. Images were run through a Channelized Hotelling model observer with Garbor channels and ROC analysis was performed. The AUC values showed performance improvement using the iterative reconstruction algorithm and the amount of improvement increased with strength setting.

  12. Liver transplant

    MedlinePlus

    Hepatic transplant; Transplant - liver; Orthotopic liver transplant; Liver failure - liver transplant; Cirrhosis - liver transplant ... The donated liver may be from: A donor who has recently died and has not had liver injury. This type of ...

  13. ["Adult form" of congenital liver fibrosis].

    PubMed

    Schacherer, D; Rümmele, P; Schölmerich, J

    2004-07-02

    During a routine check-up, a 37-year-old woman was found to have elevated levels of serum gamma-glutamyl transferase (gamma GTP) and IgA- and IgM-antibodies. One of the patient's brothers had died at the age of six from acute liver failure. We found a palpably enlarged liver with normal consistency and no particular helpful laboratory results. The abdominal ultrasound and computed tomography (CT) showed segmental and saccular dilatations of the biliary tract, hepatofugal flow in the portal vein, multiple collateral vessels as well as a mild splenomegaly. Histopathology revealed fibrotic liver parenchyma, a dilatated and branched biliary tract lined by cubical epithelium. Gastroscopy showed lowgrade esophageal varices. Seventeen months after the initial presentation there were no significant changes of the laboratory tests or the ultrasound. The defective remodelling of the ductal plate ("ductal plate malformation") is associated with dysplasia of the biliary tract. Depending on the localisation of the lesions within the biliary tract and whether it is a more cystic or more fibrotic component, there are different malformations caused by ductal plate malformation. We diagnosed congenital hepatic fibrosis, because of the atypical age of presentation, it could be the "adult form" of congenital liver fibrosis.

  14. Cover for a nuclear reactor pressure vessel

    SciTech Connect

    Gross, H.

    1980-03-11

    A pressure vessel, containment or burst shield for a nuclear reactor has a substantially circular cover surmounting the cylindrical part (Shell) of the vessel and is preferably comprised of a plurality of circular or polylateral segments arranged concentrically and stressed inwardly by annular prestressing means. At least the outer polylateral segments and preferably all of the circular segments are provided on the upper surface with upwardly open circular grooves receiving the prestressing arrangement. The latter can comprise an outwardly open channel-shaped (U-section) supporting member receiving the stressing cables and means for transferring the radial stress of the annular stressing arrangement to the ring segment. The latter means may be wedges inserted between the support and a wall of the groove after the stressing arrangement has been placed under stress, E.G. By hydraulic means for spreading the annular stressing arrangement.

  15. Elastic registration of multiphase CT images of liver

    NASA Astrophysics Data System (ADS)

    Heldmann, Stefan; Zidowitz, Stephan

    2009-02-01

    In this work we present a novel approach for elastic image registration of multi-phase contrast enhanced CT images of liver. A problem in registration of multiphase CT is that the images contain similar but complementary structures. In our application each image shows a different part of the vessel system, e.g., portal/hepatic venous/arterial, or biliary vessels. Portal, arterial and biliary vessels run in parallel and abut on each other forming the so called portal triad, while hepatic veins run independent. Naive registration will tend to align complementary vessel. Our new approach is based on minimizing a cost function consisting of a distance measure and a regularizer. For the distance we use the recently proposed normalized gradient field measure that focuses on the alignment of edges. For the regularizer we use the linear elastic potential. The key feature of our approach is an additional penalty term using segmentations of the different vessel systems in the images to avoid overlaps of complementary structures. We successfully demonstrate our new method by real data examples.

  16. Random Forests for Dura Mater Microvasculature Segmentation Using Epifluorescence Images

    PubMed Central

    Kassim, Yasmin M.; Surya Prasath, V. B.; Pelapur, Rengarajan; Glinskii, Olga V.; Maude, Richard J.; Glinsky, Vladislav V.; Huxley, Virginia H.; Palaniappan, Kannappan

    2016-01-01

    Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The latter multiscale line detector feature helps in detecting and connecting faint vessel structures that would otherwise be missed. Experimental results on epifluorescence imagery show that the RF approach produces foreground vessel regions that are almost 20 and 25 percent better than Niblack and Otsu threshold-based segmentations respectively. PMID:28261007

  17. Random Forests for Dura Mater Microvasculature Segmentation Using Epifluorescence Images.

    PubMed

    Kassim, Yasmin M; Surya Prasath, V B; Pelapur, Rengarajan; Glinskii, Olga V; Maude, Richard J; Glinsky, Vladislav V; Huxley, Virginia H; Palaniappan, Kannappan

    2016-08-01

    Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The latter multiscale line detector feature helps in detecting and connecting faint vessel structures that would otherwise be missed. Experimental results on epifluorescence imagery show that the RF approach produces foreground vessel regions that are almost 20 and 25 percent better than Niblack and Otsu threshold-based segmentations respectively.

  18. Liver Transplant

    MedlinePlus

    ... Home > Your Liver > Liver Disease Information > Liver Transplant Liver Transplant Explore this section to learn more about ... resource. www.paulcox.com.au Why is the liver important? The liver is the second largest organ ...

  19. Lactiferous vessel detection from microscopic cross-sectional images

    NASA Astrophysics Data System (ADS)

    Jariyawatthananon, Jirapath; Cooharojananone, Nagul; Lipikorn, Rajalida

    2014-04-01

    This paper presents the methods to detect and segment lactiferous vessels or rubber latex vessels from gray scale microscopic cross-sectional images using polynomial curve-fitting with maximum and minimum stationary points. Polynomial curve-fitting is used to detect the location of lactiferous vessels from an image of a non-dyed cross-sectional slice which was taken by a digital camera through microscope lens. The lactiferous vessels are then segmented from an image using maximum and minimum stationary points with morphological closing operation. Two species of rubber trees of age between one to two years old are sampled namely, RRIM600 and RRIT251. Two data sets contain 30 microscopic cross-sectional images of one-year old rubber tree's stems from each species are used in the experiments and the results reveal that most of the lactiferous vessel areas can be segmented correctly.

  20. Automatic detection of plaques with severe stenosis in coronary vessels of CT angiography

    NASA Astrophysics Data System (ADS)

    Dinesh, M. S.; Devarakota, Pandu; Kumar, Jitendra

    2010-03-01

    Coronary artery disease is the end result of the accumulation of atheromatous plaques within the walls of coronary arteries and is the leading cause of death worldwide. Computed tomography angiography (CTA) has been proved to be very useful for accurate noninvasive diagnosis and quantification of plaques. However, the existing methods to measure the stenosis in the plaques are not accurate enough in mid and distal segments where the vessels become narrower. To alleviate this, we propose a method that consists of three stages namely, automatic extraction of coronary vessels; vessels straightening; lumen extraction and stenosis evaluation. In the first stage, the coronary vessels are segmented using a parametric approach based on circular vessel model at each point on the centerline. It is assumed that centerline information is available in advance. Vessel straightening in the second stage performs multi-planar reformat (MPR) to straighten the curved vessels. MPR view of a vessel helps to visualize and measure the plaques better. On the straightened vessel, lumen and vessel wall are segregated using a nearest neighbor classification. To detect the plaques with severe stenosis in the vessel lumen, we propose a "Diameter Luminal Stenosis" method for analyzing the smaller segments of the vessel. Proposed measurement technique identifies the segments that have plaques and reports the top three severely stenosed segments. Proposed algorithm is applied on 24 coronary vessels belonging to multiple cases acquired from Sensation 64 - slice CT and initial results are promising.

  1. Active Segmentation

    PubMed Central

    Mishra, Ajay; Aloimonos, Yiannis

    2009-01-01

    The human visual system observes and understands a scene/image by making a series of fixations. Every fixation point lies inside a particular region of arbitrary shape and size in the scene which can either be an object or just a part of it. We define as a basic segmentation problem the task of segmenting that region containing the fixation point. Segmenting the region containing the fixation is equivalent to finding the enclosing contour- a connected set of boundary edge fragments in the edge map of the scene - around the fixation. This enclosing contour should be a depth boundary. We present here a novel algorithm that finds this bounding contour and achieves the segmentation of one object, given the fixation. The proposed segmentation framework combines monocular cues (color/intensity/texture) with stereo and/or motion, in a cue independent manner. The semantic robots of the immediate future will be able to use this algorithm to automatically find objects in any environment. The capability of automatically segmenting objects in their visual field can bring the visual processing to the next level. Our approach is different from current approaches. While existing work attempts to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Experiments with real imagery collected by our active robot and from the known databases 1 demonstrate the promise of the approach. PMID:20686671

  2. Stiffness Study of Wound-Filament Pressure Vessels

    NASA Technical Reports Server (NTRS)

    Verderaime, V.

    1986-01-01

    Report presents theoretical and experimental study of stiffness of lightweight, jointed pressure vessels made of wound graphite fibers and epoxy. Specimens fabricated from layers of graphite fibers, wet with epoxy, on aluminum mandrel. Segment ends thickened with interspersed layers of axially oriented fibers to reduce pinhole bearing stresses and local deformations. Segments cured at 390 degrees F (199 degrees C). Report presents results of vibrational tests of one-quarter-scale models of wound-filament pressure vessels.

  3. EVENT SEGMENTATION

    PubMed Central

    Zacks, Jeffrey M.; Swallow, Khena M.

    2012-01-01

    One way to understand something is to break it up into parts. New research indicates that segmenting ongoing activity into meaningful events is a core component of ongoing perception, with consequences for memory and learning. Behavioral and neuroimaging data suggest that event segmentation is automatic and that people spontaneously segment activity into hierarchically organized parts and sub-parts. This segmentation depends on the bottom-up processing of sensory features such as movement, and on the top-down processing of conceptual features such as actors’ goals. How people segment activity affects what they remember later; as a result, those who identify appropriate event boundaries during perception tend to remember more and learn more proficiently. PMID:22468032

  4. WE-D-18A-05: Construction of Realistic Liver Phantoms From Patient Images and a Commercial 3D Printer

    SciTech Connect

    Leng, S; Vrieze, T; Kuhlmann, J; Yu, L; Matsumoto, J; Morris, J; McCollough, C

    2014-06-15

    Purpose: To assess image quality and radiation dose reduction in abdominal CT imaging, physical phantoms having realistic background textures and lesions are highly desirable. The purpose of this work was to construct a liver phantom with realistic background and lesions using patient CT images and a 3D printer. Methods: Patient CT images containing liver lesions were segmented into liver tissue, contrast-enhanced vessels, and liver lesions using commercial software (Mimics, Materialise, Belgium). Stereolithography (STL) files of each segmented object were created and imported to a 3D printer (Object350 Connex, Stratasys, MN). After test scans were performed to map the eight available printing materials into CT numbers, printing materials were assigned to each object and a physical liver phantom printed. The printed phantom was scanned on a clinical CT scanner and resulting images were compared with the original patient CT images. Results: The eight available materials used to print the liver phantom had CT number ranging from 62 to 117 HU. In scans of the liver phantom, the liver lesions and veins represented in the STL files were all visible. Although the absolute value of the CT number in the background liver material (approx. 85 HU) was higher than in patients (approx. 40 HU), the difference in CT numbers between lesions and background were representative of the low contrast values needed for optimization tasks. Future work will investigate materials with contrast sufficient to emulate contrast-enhanced arteries. Conclusion: Realistic liver phantoms can be constructed from patient CT images using a commercial 3D printer. This technique may provide phantoms able to determine the effect of radiation dose reduction and noise reduction techniques on the ability to detect subtle liver lesions in the context of realistic background textures.

  5. Tracing retinal vessel trees by transductive inference

    PubMed Central

    2014-01-01

    Background Structural study of retinal blood vessels provides an early indication of diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. These studies require accurate tracing of retinal vessel tree structure from fundus images in an automated manner. However, the existing work encounters great difficulties when dealing with the crossover issue commonly-seen in vessel networks. Results In this paper, we consider a novel graph-based approach to address this tracing with crossover problem: After initial steps of segmentation and skeleton extraction, its graph representation can be established, where each segment in the skeleton map becomes a node, and a direct contact between two adjacent segments is translated to an undirected edge of the two corresponding nodes. The segments in the skeleton map touching the optical disk area are considered as root nodes. This determines the number of trees to-be-found in the vessel network, which is always equal to the number of root nodes. Based on this undirected graph representation, the tracing problem is further connected to the well-studied transductive inference in machine learning, where the goal becomes that of properly propagating the tree labels from those known root nodes to the rest of the graph, such that the graph is partitioned into disjoint sub-graphs, or equivalently, each of the trees is traced and separated from the rest of the vessel network. This connection enables us to address the tracing problem by exploiting established development in transductive inference. Empirical experiments on public available fundus image datasets demonstrate the applicability of our approach. Conclusions We provide a novel and systematic approach to trace retinal vessel trees with the present of crossovers by solving a transductive learning problem on induced undirected graphs. PMID:24438151

  6. Breathing motion compensated registration of laparoscopic liver ultrasound to CT

    NASA Astrophysics Data System (ADS)

    Ramalhinho, João.; Robu, Maria; Thompson, Stephen; Edwards, Philip; Schneider, Crispin; Gurusamy, Kurinchy; Hawkes, David; Davidson, Brian; Barratt, Dean; Clarkson, Matthew J.

    2017-03-01

    Laparoscopic Ultrasound (LUS) is regularly used during laparoscopic liver resection to locate critical vascular structures. Many tumours are iso-echoic, and registration to pre-operative CT or MR has been proposed as a method of image guidance. However, factors such as abdominal insufflation, LUS probe compression and breathing motion cause deformation of the liver, making this task far from trivial. Fortunately, within a smaller local region of interest a rigid solution can suffice. Also, the respiratory cycle can be expected to be consistent. Therefore, in this paper we propose a feature-based local rigid registration method to align tracked LUS data with CT while compensating for breathing motion. The method employs the Levenberg-Marquardt Iterative Closest Point (LMICP) algorithm, registers both on liver surface and vessels and requires two LUS datasets, one for registration and another for breathing estimation. Breathing compensation is achieved by fitting a 1D breathing model to the vessel points. We evaluate the algorithm by measuring the Target Registration Error (TRE) of three manually selected landmarks of a single porcine subject. Breathing compensation improves accuracy in 77% of the measurements. In the best case, TRE values below 3mm are obtained. We conclude that our method can potentially correct for breathing motion without gated acquisition of LUS and be integrated in the surgical workflow with an appropriate segmentation.

  7. BIOASSAY VESSEL FAILURE ANALYSIS

    SciTech Connect

    Vormelker, P

    2008-09-22

    Two high-pressure bioassay vessels failed at the Savannah River Site during a microwave heating process for biosample testing. Improper installation of the thermal shield in the first failure caused the vessel to burst during microwave heating. The second vessel failure is attributed to overpressurization during a test run. Vessel failure appeared to initiate in the mold parting line, the thinnest cross-section of the octagonal vessel. No material flaws were found in the vessel that would impair its structural performance. Content weight should be minimized to reduce operating temperature and pressure. Outer vessel life is dependent on actual temperature exposure. Since thermal aging of the vessels can be detrimental to their performance, it was recommended that the vessels be used for a limited number of cycles to be determined by additional testing.

  8. Intermittent ischemia enhances the uptake of indocyanine green to livers subject to ischemia and reperfusion.

    PubMed

    Steenks, Mathilde; Peters, Jeroen; Rademacher, Willem; Nieuwenhuijs, Vincent B; Padbury, Robert T A; Barritt, Greg J

    2017-03-01

    Intermittent ischemia is known to promote post perfusion bile flow, and hence recovery of liver function following ischemia reperfusion of the liver. However, the mechanisms involved are not well understood. The aim of this study was to identify the step(s) in the bile acid transport pathway altered by intermittent ischemia. Arat model of segmental hepatic ischemia in which the bilateral median and left lateral lobes were made ischemic by clamping the blood vessels was used. Indocyanine green (ICG), infrared spectroscopy, and compartmental kinetic analysis, were used to indirectly monitor the movement of bile acids across hepatocytes in situ. Rates of bile flow were measured gravimetrically. In control livers (not subjected to ischemia), the movement of ICG from the blood to bile fluid could be described by a three compartment model comprising the blood, a rapidly-exchangeable compartment, and the hepatocyte cytoplasmic space. In livers subjected to continuous clamping, the rates of ICG uptake to the liver, and outflow from the liver, were greatly reduced compared with those in control livers. Intermittent clamping (three episodes of 15 min clamping) compared with continuous clamping substantially increased the rate of ICG uptake from the blood but had less effect on the rate of ICG outflow from hepatocytes. It is concluded that intermittent ischemia promotes post reperfusion bile flow in the early phase of ischemia reperfusion injury principally by enhancing the movement of bile acids from the blood to hepatocytes. © 2012 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

  9. Decellularized liver as a practical scaffold with a vascular network template for liver tissue engineering.

    PubMed

    Shirakigawa, Nana; Ijima, Hiroyuki; Takei, Takayuki

    2012-11-01

    The construction of a functional liver-tissue equivalent using tissue engineering is a very important goal because the liver is a central organ in the body. However, the construction of functional organ-scale liver tissue is impossible because it requires a high-density blood vessel network. In this study, we focused on decellularization technology to solve this problem. Decellularized liver tissue with a fine vascular tree network template was obtained using Triton X-100. The distance between each vascular structure was less than 1 mm. Endothelialization of the blood vessel network with human umbilical vein endothelial cells (HUVECs) was successfully performed without any leakage of HUVECs to the outside of the vessel structure. Furthermore, hepatocytes/spheroids could be located around the blood vessel structure. This study indicates that decellularized liver tissue is a potential scaffold for creating a practical liver tissue using tissue engineering technology.

  10. [Automatic detection of vessels in color fundus images].

    PubMed

    Jiménez, S; Alemany, P; Fondón, I; Foncubierta, A; Acha, B; Serrano, C

    2010-03-01

    The main purpose of the paper is to evaluate an automated method for blood vessels segmentation in color fundus images, due to its important role in the diagnosis of several pathologies such as diabetes. The final objective is to introduce the algorithm into a Computer Aided Diagnosis (CAD) tool that would be available in those local medical centers without specialists. An automated method for blood vessels segmentation in color fundus images was implemented and tested. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. The outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images, resulting from vessel width dependent morphological filters. The method was evaluated using the images of two publicly available databases (STARE and DRIVE) and a database with 24 images. The algorithm outperforms other published algorithms and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity. In addition, results have been subject to the experts' valuation that they think that retinal vessels remain represented with valuable accuracy on having analyzed the test's images. Due to the good segmentation results, the algorithm proposed could be implemented as part of a complete CAD tool in the local medical centers. This would reduce cost and diagnosis time.

  11. Optimized Performance of FlightPlan during Chemoembolization for Hepatocellular Carcinoma: Importance of the Proportion of Segmented Tumor Area

    PubMed Central

    Joo, Seung-Moon; Kim, Yong Pyo; Yum, Tae Jun; Eun, Na Lae; Lee, Dahye

    2016-01-01

    Objective To evaluate retrospectively the clinical effectiveness of FlightPlan for Liver (FPFL), an automated tumor-feeding artery detection software in cone-beam CT angiography (CBCTA), in identifying tumor-feeding arteries for the treatment of hepatocellular carcinoma (HCC) using three different segmentation sensitivities. Materials and Methods The study included 50 patients with 80 HCC nodules who received transarterial chemoembolization. Standard digital subtracted angiography (DSA) and CBCTA were systematically performed and analyzed. Three settings of the FPFL software for vascular tree segmentation were tested for each tumor: the default, Group D; adjusting the proportion of segmented tumor area between 30 to 50%, Group L; and between 50 to 80%, Group H. Results In total, 109 feeder vessels supplying 80 HCC nodules were identified. The negative predictive value of DSA, FPFL in groups D, L, and H was 56.8%, 87.7%, 94.2%, 98.5%, respectively. The accuracy of DSA, FPFL in groups D, L, and H was 62.6%, 86.8%, 93.4%, 95.6%, respectively. The sensitivity, negative predictive value (NPV), and accuracy of FPFL were higher in Group H than in Group D (p = 0.041, 0.034, 0.005). All three segmentation sensitivity groups showed higher specificity, positive predictive value, NPV, and accuracy of FPFL, as compared to DSA. Conclusion FlightPlan for Liver is a valuable tool for increasing detection of HCC tumor feeding vessels, as compared to standard DSA analysis, particularly in small HCC. Manual adjustment of segmentation sensitivity improves the accuracy of FPFL. PMID:27587967

  12. Monitoring of Total and Regional Liver Function after SIRT.

    PubMed

    Bennink, Roelof J; Cieslak, Kasia P; van Delden, Otto M; van Lienden, Krijn P; Klümpen, Heinz-Josef; Jansen, Peter L; van Gulik, Thomas M

    2014-01-01

    Selective internal radiation therapy (SIRT) is a promising treatment modality for advanced hepatocellular carcinoma or metastatic liver cancer. SIRT is usually well tolerated. However, in most patients, SIRT will result in a (temporary) decreased liver function. Occasionally patients develop radioembolization-induced liver disease (REILD). In case of a high tumor burden of the liver, it could be beneficial to perform SIRT in two sessions enabling the primary untreated liver segments to guarantee liver function until function in the treated segments has recovered or functional hypertrophy has occurred. Clinically used liver function tests provide evidence of only one of the many liver functions, though all of them lack the possibility of assessment of segmental (regional) liver function. Hepatobiliary scintigraphy (HBS) has been validated as a tool to assess total and regional liver function in liver surgery. It is also used to assess segmental liver function before and after portal vein embolization. HBS is considered as a valuable quantitative liver function test enabling assessment of segmental liver function recovery after regional intervention and determination of future remnant liver function. We present two cases in which HBS was used to monitor total and regional liver function in a patient after repeated whole liver SIRT complicated with REILD and a patient treated unilaterally without complications.

  13. Method of fabricating a prestressed cast iron vessel

    DOEpatents

    Lampe, Robert F.

    1982-01-01

    A method of fabricating a prestressed cast iron vessel wherein double wall cast iron body segments each have an arcuate inner wall and a spaced apart substantially parallel outer wall with a plurality of radially extending webs interconnecting the inner wall and the outer wall, the bottom surface and the two exposed radial side surfaces of each body segment are machined and eight body segments are formed into a ring. The top surfaces and outer surfaces of the outer walls are machined and keyways are provided across the juncture of adjacent end walls of the body segments. A liner segment complementary in shape to a selected inner wall of one of the body segments is mounted to each of the body segments and again formed into a ring. The liner segments of each ring are welded to form unitary liner rings and thereafter the cast iron body segments are prestressed to complete the ring assembly. Ring assemblies are stacked to form the vessel and adjacent unitary liner rings are welded. A top head covers the top ring assembly to close the vessel and axially extending tendons retain the top and bottom heads in place under pressure.

  14. Curvature affects Doppler investigation of vessels: implications for clinical practice.

    PubMed

    Balbis, S; Roatta, S; Guiot, C

    2005-01-01

    In clinical practice, blood velocity estimations from Doppler examination of curved vascular segments are normally different from those of nearby straight segments. The observed "accelerations," sometimes considered as a sort of stochastic disturbances, can actually be related to very specific physical effects due to vessel curvature (i.e., the development of nonaxial velocity [NAV] components) and the spreading of the axial velocity direction in the Doppler sample volume with respect to the insonation axis. The relevant phenomena and their dependence on the radius of curvature of the vessels and on the insonation angle are investigated with a beam-vessel geometry as close as possible to clinical setting, with the simplifying assumptions of steady flow, mild vessel curvature, uniform ultrasonic beam and complete vessel insonation. The insonation angles that minimize the errors are provided on the basis of the study results.

  15. Beyond Frangi: an improved multiscale vesselness filter

    NASA Astrophysics Data System (ADS)

    Jerman, Tim; Pernuš, Franjo; Likar, Boštjan; Špiclin, Žiga

    2015-03-01

    Vascular diseases are among the top three causes of death in the developed countries. Effective diagnosis of vascular pathologies from angiographic images is therefore very important and usually relies on segmentation and visualization of vascular structures. To enhance the vascular structures prior to their segmentation and visualization, and to suppress non-vascular structures and image noise, the filters enhancing vascular structures are used extensively. Even though several enhancement filters are widely used, the responses of these filters are typically not uniform between vessels of different radii and, compared to the response in the central part of vessels, their response is lower at vessels' edges and bifurcations, and vascular pathologies like aneurysm. In this paper, we propose a novel enhancement filter based on ratio of multiscale Hessian eigenvalues, which yields a close-to-uniform response in all vascular structures and accurately enhances the border between the vascular structures and the background. The proposed and four state-of-the-art enhancement filters were evaluated and compared on a 3D synthetic image containing tubular structures and a clinical dataset of 15 cerebral 3D digitally subtracted angiograms with manual expert segmentations. The evaluation was based on quantitative metrics of segmentation performance, computed as area under the precision-recall curve, signal-to-noise ratio of the vessel enhancement and the response uniformity within vascular structures. The proposed filter achieved the best scores in all three metrics and thus has a high potential to further improve the performance of existing or encourage the development of more advanced methods for segmentation and visualization of vascular structures.

  16. Culprit Vessel-Only vs. Staged Multivessel Percutaneous Coronary Intervention Strategies in Patients With Multivessel Coronary Artery Disease Undergoing Primary Percutaneous Coronary Intervention for ST-Segment Elevation Myocardial Infarction.

    PubMed

    Toyota, Toshiaki; Shiomi, Hiroki; Taniguchi, Tomohiko; Morimoto, Takeshi; Furukawa, Yutaka; Nakagawa, Yoshihisa; Horie, Minoru; Kimura, Takeshi

    2016-01-01

    We assessed the current status of treatment strategy in ST-segment elevation myocardial infarction (STEMI) with multivessel disease (MVD) in real world practice, focusing on the benefit of staged percutaneous coronary intervention (PCI). From the CREDO-Kyoto AMI Registry, 2,010 STEMI patients with MVD undergoing primary PCI were analyzed. Only 96 patients (4.8%) received acute multivessel PCI, and the majority of patients (n=1,914, 95.2%) had culprit-only PCI acutely. After excluding 699 patients (acute multivessel PCI, Killip class ≥3, age ≥90 years, coronary artery bypass grafting within 90 days, or clinical events within 90 days), 681 MVD patients underwent staged PCI for angiographically significant non-culprit lesions within 90 days (staged PCI group), while 630 MVD patients received primary PCI only (culprit-only PCI group). The cumulative 5-year incidence of and adjusted risk for all-cause death were significantly lower in the staged PCI group compared with the culprit-only PCI group (9.5% vs. 16.0%, P<0.001; HR, 0.69; 95% CI: 0.50-0.96, P=0.03). The risks for MI and any coronary revascularization favored the staged PCI strategy. The staged PCI strategy for angiographically significant non-culprit lesions was associated with lower 5-year mortality compared with the culprit-only PCI strategy in STEMI patients with MVD who underwent primary PCI.

  17. Single vessel air injection estimates of xylem resistance to cavitation are affected by vessel network characteristics and sample length.

    PubMed

    Venturas, Martin D; Rodriguez-Zaccaro, F Daniela; Percolla, Marta I; Crous, Casparus J; Jacobsen, Anna L; Pratt, R Brandon

    2016-10-01

    Xylem resistance to cavitation is an important trait that is related to the ecology and survival of plant species. Vessel network characteristics, such as vessel length and connectivity, could affect the spread of emboli from gas-filled vessels to functional ones, triggering their cavitation. We hypothesized that the cavitation resistance of xylem vessels is randomly distributed throughout the vessel network. We predicted that single vessel air injection (SVAI) vulnerability curves (VCs) would thus be affected by sample length. Longer stem samples were predicted to appear more resistant than shorter samples due to the sampled path including greater numbers of vessels. We evaluated the vessel network characteristics of grapevine (Vitis vinifera L.), English oak (Quercus robur L.) and black cottonwood (Populus trichocarpa Torr. & A. Gray), and constructed SVAI VCs for 5- and 20-cm-long segments. We also constructed VCs with a standard centrifuge method and used computer modelling to estimate the curve shift expected for pathways composed of different numbers of vessels. For all three species, the SVAI VCs for 5 cm segments rose exponentially and were more vulnerable than the 20 cm segments. The 5 cm curve shapes were exponential and were consistent with centrifuge VCs. Modelling data supported the observed SVAI VC shifts, which were related to path length and vessel network characteristics. These results suggest that exponential VCs represent the most realistic curve shape for individual vessel resistance distributions for these species. At the network level, the presence of some vessels with a higher resistance to cavitation may help avoid emboli spread during tissue dehydration. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  18. Quantification of carotid vessel atherosclerosis

    NASA Astrophysics Data System (ADS)

    Chiu, Bernard; Egger, Micaela; Spence, J. D.; Parraga, Grace; Fenster, Aaron

    2006-03-01

    Atherosclerosis is characterized by the development of plaques in the arterial wall, which ultimately leads to heart attacks and stroke. 3D ultrasound (US) has been used to screen patients' carotid arteries. Plaque measurements obtained from these images may aid in the management and monitoring of patients, and in evaluating the effect of new treatment options. Different types of measures for ultrasound phenotypes of atherosclerosis have been proposed. Here, we report on the development and application of a method used to analyze changes in carotid plaque morphology from 3D US images obtained at two different time points. We evaluated our technique using manual segmentations of the wall and lumen of the carotid artery from images acquired in two US scanning sessions. To incorporate the effect of intraobserver variability in our evaluation, manual segmentation was performed five times each for the arterial wall and lumen. From this set of five segmentations, the mean wall and lumen surfaces were reconstructed, with the standard deviation at each point mapped onto the surfaces. A correspondence map between the mean wall and lumen surfaces was then established, and the thickness of the atherosclerotic plaque at each point in the vessel was estimated to be the distance between each correspondence pairs. The two-sample Student's t-test was used to judge whether the difference between the thickness values at each pair corresponding points of the arteries in the two 3D US images was statistically significant.

  19. Registration-free laparoscope augmentation for intra-operative liver resection planning

    NASA Astrophysics Data System (ADS)

    Feuerstein, Marco; Mussack, Thomas; Heining, Sandro M.; Navab, Nassir

    2007-03-01

    In recent years, an increasing number of liver tumor indications were treated by minimally invasive laparoscopic resection. Besides the restricted view, a major issue in laparoscopic liver resection is the enhanced visualization of (hidden) vessels, which supply the tumorous liver segment and thus need to be divided prior to the resection. To navigate the surgeon to these vessels, pre-operative abdominal imaging data can hardly be used due to intraoperative organ deformations mainly caused by appliance of carbon dioxide pneumoperitoneum and respiratory motion. While regular respiratory motion can be gated and synchronized intra-operatively, motion caused by pneumoperitoneum is individual for every patient and difficult to estimate. Therefore, we propose to use an optically tracked mobile C-arm providing cone-beam CT imaging capability intraoperatively. The C-arm is able to visualize soft tissue by means of its new flat panel detector and is calibrated offline to relate its current position and orientation to the coordinate system of a reconstructed volume. Also the laparoscope is optically tracked and calibrated offline, so both laparoscope and C-arm are registered in the same tracking coordinate system. Intra-operatively, after patient positioning, port placement, and carbon dioxide insufflation, the liver vessels are contrasted and scanned during patient exhalation. Immediately, a three-dimensional volume is reconstructed. Without any further need for patient registration, the volume can be directly augmented on the live laparoscope video, visualizing the contrasted vessels. This augmentation provides the surgeon with advanced visual aid for the localization of veins, arteries, and bile ducts to be divided or sealed.

  20. Application of morphological bit planes in retinal blood vessel extraction.

    PubMed

    Fraz, M M; Basit, A; Barman, S A

    2013-04-01

    The appearance of the retinal blood vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal blood vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of blood vessels in retinal images. A unique combination of methods for retinal blood vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the blood vessels from the color retinal images. The skeleton of main vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented vessel tree.

  1. Multi-focus Image Fusion Using Epifluorescence Microscopy for Robust Vascular Segmentation

    PubMed Central

    Pelapur, Rengarajan; Prasath, V. B. Surya; Bunyak, Filiz; Glinskii, Olga V.; Glinsky, Vladislav V.; Huxley, Virginia H.; Palaniappan, Kannappan

    2015-01-01

    Automatic segmentation of three-dimensional microvascular structures is needed for quantifying morphological changes to blood vessels during development, disease and treatment processes. Single focus two-dimensional epifluorescent imagery lead to unsatisfactory segmentations due to multiple out of focus vessel regions that have blurred edge structures and lack of detail. Additional segmentation challenges include varying contrast levels due to diffusivity of the lectin stain, leakage out of vessels and fine morphological vessel structure. We propose an approach for vessel segmentation that combines multi-focus image fusion with robust adaptive filtering. The robust adaptive filtering scheme handles noise without destroying small structures, while multi-focus image fusion considerably improves segmentation quality by deblurring out-of-focus regions through incorporating 3D structure information from multiple focus steps. Experiments using epifluorescence images of mice dura mater show an average of 30.4% improvement compared to single focus microvasculature segmentation. PMID:25571050

  2. Multi-focus image fusion using epifluorescence microscopy for robust vascular segmentation.

    PubMed

    Pelapur, Rengarajan; Prasath, V B Surya; Bunyak, Filiz; Glinskii, Olga V; Glinsky, Vladislav V; Huxley, Virginia H; Palaniappan, Kannappan

    2014-01-01

    Automatic segmentation of three-dimensional mi-crovascular structures is needed for quantifying morphological changes to blood vessels during development, disease and treatment processes. Single focus two-dimensional epifluorescent imagery lead to unsatisfactory segmentations due to multiple out of focus vessel regions that have blurred edge structures and lack of detail. Additional segmentation challenges include varying contrast levels due to diffusivity of the lectin stain, leakage out of vessels and fine morphological vessel structure. We propose an approach for vessel segmentation that combines multi-focus image fusion with robust adaptive filtering. The robust adaptive filtering scheme handles noise without destroying small structures, while multi-focus image fusion considerably improves segmentation quality by deblurring out-of-focus regions through incorporating 3D structure information from multiple focus steps. Experiments using epifluorescence images of mice dura mater show an average of 30.4% improvement compared to single focus microvasculature segmentation.

  3. Centerline correction of incorrectly segmented coronary arteries in CT angiography

    NASA Astrophysics Data System (ADS)

    Fu, Ling; Kang, Yan

    2013-03-01

    For computer-aided diagnosis of cardiovascular diseases, accurately extracted centerlines of coronary arteries are important. However, centerlines extracted from incorrectly segmented vessels are usually unsatisfactory. For this reason, we propose two automatic centerline correction methods in this paper. First, a method based on the local volume comparison and the morphological comparison is presented to remove false centerlines from over-segmented tissues. Second, another method based on the judgment of vessel identity and the gradient-SDF (source distance field) calculation is presented to add missing centerlines of under-segmented vessels. We have validated the proposed centerline correction methods on real CT angiographic datasets of coronary arteries. The quantitative evaluation results show that the proposed methods can effectively correct centerline errors arising from erroneous vessel segmentation in most cases.

  4. Managing synchronous liver metastases from colorectal cancer: a multidisciplinary international consensus.

    PubMed

    Adam, René; de Gramont, Aimery; Figueras, Joan; Kokudo, Norihiro; Kunstlinger, Francis; Loyer, Evelyne; Poston, Graeme; Rougier, Philippe; Rubbia-Brandt, Laura; Sobrero, Alberto; Teh, Catherine; Tejpar, Sabine; Van Cutsem, Eric; Vauthey, Jean-Nicolas; Påhlman, Lars

    2015-11-01

    An international panel of multidisciplinary experts convened to develop recommendations for managing patients with colorectal cancer (CRC) and synchronous liver metastases (CRCLM). A modified Delphi method was used. CRCLM is defined as liver metastases detected at or before diagnosis of the primary CRC. Early and late metachronous metastases are defined as those detected ⩽12months and >12months after surgery, respectively. To provide information on potential curability, use of high-quality contrast-enhanced computed tomography (CT) before chemotherapy is recommended. Magnetic resonance imaging is increasingly being used preoperatively to aid detection of subcentimetric metastases, and alongside CT in difficult situations. To evaluate operability, radiology should provide information on: nodule size and number, segmental localization and relationship with major vessels, response after neoadjuvant chemotherapy, non-tumoral liver condition and anticipated remnant liver volume. Pathological evaluation should assess response to preoperative chemotherapy for both the primary tumour and metastases, and provide information on the tumour, margin size and micrometastases. Although the treatment strategy depends on the clinical scenario, the consensus was for chemotherapy before surgery in most cases. When the primary CRC is asymptomatic, liver surgery may be performed first (reverse approach). When CRCLM are unresectable, the goal of preoperative chemotherapy is to downsize tumours to allow resection. Hepatic resection should not be denied to patients with stable disease after optimal chemotherapy, provided an adequate liver remnant with inflow and outflow preservation remains. All patients with synchronous CRCLM should be evaluated by a hepatobiliary multidisciplinary team.

  5. SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

    SciTech Connect

    Gou, S; Rapacchi, S; Hu, P; Sheng, K

    2014-06-01

    Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagated to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on

  6. Phospholipase D1 is required for angiogenesis of intersegmental blood vessels in zebrafish

    PubMed Central

    Zeng, Xin-Xin I.; Zheng, Xiangjian; Xiang, Yun; Cho, Hyekyung P.; Jessen, Jason R.; Zhong, Tao P.; Solnica-Krezel, Lilianna; Brown, H. Alex

    2009-01-01

    Phospholipase D (PLD) hydrolyzes phosphatidylcholine to generate phosphatidic acid and choline. Studies in cultured cells and Drosophila melanogaster have implicated PLD in the regulation of many cellular functions, including intracellular vesicle trafficking, cell proliferation and differentiation. However, the function of PLD in vertebrate development has not been explored. Here we report cloning and characterization of a zebrafish PLD1 (pld1) homolog. Like mammalian PLDs, zebrafish Pld1 contains two conservative HKD motifs. Maternally contributed pld1 transcripts are uniformly distributed in early embryo. Localized expression of pld1 is observed in the notochord during early segmentation, in the somites during later segmentation and in the liver at the larval stages. Studies in intact and cell-free preparations demonstrate evolutionary conservation of regulation. Inhibition of Pld1 expression using antisense morpholino oligonucleotides (MO) interfering with the translation or splicing of pld1 impaired intersegmental vessel (ISV) development. Incubating embryos with 1-butanol, which diverts production of phosphatidic acid to a phosphatidylalcohol, caused similar ISV defects. To determine where pld1 is required for ISV development we performed transplantation experiments. Analyses of the mosaic pld1 deficient embryos showed partial suppression of ISV defects in the segments containing transplanted wild-type somitic and notochord cells, or notochord cells alone. These results provide the first evidence that function of Pld1 in the developing notochord is essential for vascular development in vertebrates. PMID:19389349

  7. Intraoperative laparoscope augmentation for port placement and resection planning in minimally invasive liver resection.

    PubMed

    Feuerstein, Marco; Mussack, Thomas; Heining, Sandro M; Navab, Nassir

    2008-03-01

    In recent years, an increasing number of liver tumor indications were treated by minimally invasive laparoscopic resection. Besides the restricted view, two major intraoperative issues in laparoscopic liver resection are the optimal planning of ports as well as the enhanced visualization of (hidden) vessels, which supply the tumorous liver segment and thus need to be divided (e.g., clipped) prior to the resection. We propose an intuitive and precise method to plan the placement of ports. Preoperatively, self-adhesive fiducials are affixed to the patient's skin and a computed tomography (CT) data set is acquired while contrasting the liver vessels. Immediately prior to the intervention, the laparoscope is moved around these fiducials, which are automatically reconstructed to register the patient to its preoperative imaging data set. This enables the simulation of a camera flight through the patient's interior along the laparoscope's or instruments' axes to easily validate potential ports. Intraoperatively, surgeons need to update their surgical planning based on actual patient data after organ deformations mainly caused by application of carbon dioxide pneumoperitoneum. Therefore, preoperative imaging data can hardly be used. Instead, we propose to use an optically tracked mobile C-arm providing cone-beam CT imaging capability intraoperatively. After patient positioning, port placement, and carbon dioxide insufflation, the liver vessels are contrasted and a 3-D volume is reconstructed during patient exhalation. Without any further need for patient registration, the reconstructed volume can be directly augmented on the live laparoscope video, since prior calibration enables both the volume and the laparoscope to be positioned and oriented in the tracking coordinate frame. The augmentation provides the surgeon with advanced visual aid for the localization of veins, arteries, and bile ducts to be divided or sealed.

  8. Precise renal artery segmentation for estimation of renal vascular dominant regions

    NASA Astrophysics Data System (ADS)

    Wang, Chenglong; Kagajo, Mitsuru; Nakamura, Yoshihiko; Oda, Masahiro; Yoshino, Yasushi; Yamamoto, Tokunori; Mori, Kensaku

    2016-03-01

    This paper presents a novel renal artery segmentation method combining graph-cut and template-based tracking methods and its application to estimation of renal vascular dominant region. For the purpose of giving a computer assisted diagnose for kidney surgery planning, it is important to obtain the correct topological structures of renal artery for estimation of renal vascular dominant regions. Renal artery has a low contrast, and its precise extraction is a difficult task. Previous method utilizing vesselness measure based on Hessian analysis, still cannot extract the tiny blood vessels in low-contrast area. Although model-based methods including superellipsoid model or cylindrical intensity model are low-contrast sensitive to the tiny blood vessels, problems including over-segmentation and poor bifurcations detection still remain. In this paper, we propose a novel blood vessel segmentation method combining a new Hessian-based graph-cut and template modeling tracking method. Firstly, graph-cut algorithm is utilized to obtain the rough segmentation result. Then template model tracking method is utilized to improve the accuracy of tiny blood vessel segmentation result. Rough segmentation utilizing graph-cut solves the bifurcations detection problem effectively. Precise segmentation utilizing template model tracking focuses on the segmentation of tiny blood vessels. By combining these two approaches, our proposed method segmented 70% of the renal artery of 1mm in diameter or larger. In addition, we demonstrate such precise segmentation can contribute to divide renal regions into a set of blood vessel dominant regions utilizing Voronoi diagram method.

  9. Bioengineered blood vessels.

    PubMed

    Niu, Guoguang; Sapoznik, Etai; Soker, Shay

    2014-04-01

    Cardiovascular disease (CVD) affecting blood vessel function is a leading cause of death around the world. A common treatment option to replace the diseased blood vessels is vascular grafting using the patient's own blood vessels. However, patients with CVD are usually lacking vessels for grafting. Recent advances in tissue engineering are now providing alternatives to autologous vascular grafts in the form of tissue-engineered blood vessels (TEBVs). In this review, we will describe the use of different scaffolding systems, cell sources and conditioning approaches for creating fully functional blood vessels. Additionally, we will present the methods used for assessing TEBV functions and describe preclinical and clinical trials for TEBV. Although the early results were encouraging, current designs of TEBV still fall short as a viable clinical option. Implementing the current knowledge in vascular development can lead to improved fabrication and function of TEBV and hasten clinical translation.

  10. Pressure vessel bottle mount

    NASA Technical Reports Server (NTRS)

    Wingett, Paul (Inventor)

    2001-01-01

    A mounting assembly for mounting a composite pressure vessel to a vehicle includes a saddle having a curved surface extending between two pillars for receiving the vessel. The saddle also has flanged portions which can be bolted to the vehicle. Each of the pillars has hole in which is mounted the shaft portion of an attachment member. A resilient member is disposed between each of the shaft portions and the holes and loaded by a tightening nut. External to the holes, each of the attachment members has a head portion to which a steel band is attached. The steel band circumscribes the vessel and translates the load on the springs into a clamping force on the vessel. As the vessel expands and contracts, the resilient members expand and contract so that the clamping force applied by the band to the vessel remains constant.

  11. Tumor Blood Vessel Dynamics

    NASA Astrophysics Data System (ADS)

    Munn, Lance

    2009-11-01

    ``Normalization'' of tumor blood vessels has shown promise to improve the efficacy of chemotherapeutics. In theory, anti-angiogenic drugs targeting endothelial VEGF signaling can improve vessel network structure and function, enhancing the transport of subsequent cytotoxic drugs to cancer cells. In practice, the effects are unpredictable, with varying levels of success. The predominant effects of anti-VEGF therapies are decreased vessel leakiness (hydraulic conductivity), decreased vessel diameters and pruning of the immature vessel network. It is thought that each of these can influence perfusion of the vessel network, inducing flow in regions that were previously sluggish or stagnant. Unfortunately, when anti-VEGF therapies affect vessel structure and function, the changes are dynamic and overlapping in time, and it has been difficult to identify a consistent and predictable normalization ``window'' during which perfusion and subsequent drug delivery is optimal. This is largely due to the non-linearity in the system, and the inability to distinguish the effects of decreased vessel leakiness from those due to network structural changes in clinical trials or animal studies. We have developed a mathematical model to calculate blood flow in complex tumor networks imaged by two-photon microscopy. The model incorporates the necessary and sufficient components for addressing the problem of normalization of tumor vasculature: i) lattice-Boltzmann calculations of the full flow field within the vasculature and within the tissue, ii) diffusion and convection of soluble species such as oxygen or drugs within vessels and the tissue domain, iii) distinct and spatially-resolved vessel hydraulic conductivities and permeabilities for each species, iv) erythrocyte particles advecting in the flow and delivering oxygen with real oxygen release kinetics, v) shear stress-mediated vascular remodeling. This model, guided by multi-parameter intravital imaging of tumor vessel structure

  12. Automatic detection of lung vessel bifurcation in thoracic CT images

    NASA Astrophysics Data System (ADS)

    Maduskar, Pragnya; Vikal, Siddharth; Devarakota, Pandu

    2011-03-01

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

  13. Heat-Irrigate Effect' of Radiofrequency Ablation on Relevant Regional Hepatocyte in Living Swine Liver-Initial Study on Pathology.

    PubMed

    Jiang, Kai; Chen, Jiye; Liu, Yang; Liu, Jiang; Liu, Aijun; Dong, Jiahong; Huang, Zhiqiang

    2015-05-01

    Radiofrequency ablation (RFA) is one of the effective methods for HCC treatment. However, because of the "heat-sink effect" (HSE), it is very difficult to achieve a complete ablation in intrahepatic tumors. This study establishes the animal model of RFA on living swine liver and observes the 'heat-irrigate effect' on relevant regional hepatocytes. Three liver segments of 6 Guangxi Bama mini-pigs were selected to be ablated closed to segmental outflow vessel under surveillance of sonography for 6 min, and pathological changes of relevant downstream region were observed. We observed an elliptic shape of ablated area with diameter of 2.2 ± 1.1 cm on gross liver. Thermal damage was seen in downstream regional of relevant portal vein under microscope. However, adjacent area around the vessel was remained intact. In conclusion, the 'heat-irrigate effect' in RFA could cause thermal damage along the downstream region of relevant portal vein and this influence decreased gradually toward the surface.

  14. Segmental arterial mediolysis.

    PubMed

    Pillai, Anil Kumar; Iqbal, Shams I; Liu, Raymond W; Rachamreddy, Niranjan; Kalva, Sanjeeva P

    2014-06-01

    Segmental arterial mediolysis (SAM) is an uncommon, nonatherosclerotic, noninflammatory, large- to medium-sized arteriopathy first described in 1976. It is characterized histologically by vacuolization and lysis of the outer arterial media leading to dissecting aneurysms and vessel rupture presenting clinically with self-limiting abdominal pain or catastrophic hemorrhages in the abdomen. Patients of all ages are affected with a greater incidence at the fifth and sixth decades. There is a slight male predominance. Imaging findings overlap with inflammatory vasculitis, collagen vascular disease, and fibromuscular dysplasia. The presence of segmental dissections involving the celiac, mesenteric, and/or renal arteries is the key distinguishing features of SAM. Inflammatory markers, genetic tests for collagen vascular disorders, and hypercoagulable studies are negative. Anti-inflammatory agents and immunosuppressants are not effective. A mortality rate of 50 % has been attributed to the acute presentation with aneurysmal rupture necessitating urgent surgical or endovascular treatments; in the absence of the acute presentation, SAM is a self-limiting disease and is treated conservatively. There are no established guidelines on medical therapy, although optimal control of blood pressure is considered the main cornerstone of medical therapy. The long-term prognosis is not known.

  15. Analysis of HRCT-derived xylem network reveals reverse flow in some vessels.

    PubMed

    Lee, Eric F; Matthews, Mark A; McElrone, Andrew J; Phillips, Ronald J; Shackel, Kenneth A; Brodersen, Craig R

    2013-09-21

    Long distance water and nutrient transport in plants is dependent on the proper functioning of xylem networks, a series of interconnected pipe-like cells that are vulnerable to hydraulic dysfunction as a result of drought-induced embolism and/or xylem-dwelling pathogens. Here, flow in xylem vessels was modeled to determine the role of vessel connectivity by using three dimensional xylem networks derived from High Resolution Computed Tomography (HRCT) images of grapevine (Vitis vinifera cv. 'Chardonnay') stems. Flow in 4-27% of the vessel segments (i.e. any section of vessel elements between connection points associated with intervessel pits) was found to be oriented in the direction opposite to the bulk flow under normal transpiration conditions. In order for the flow in a segment to be in the reverse direction, specific requirements were determined for the location of connections, distribution of vessel endings, diameters of the connected vessels, and the conductivity of the connections. Increasing connectivity and decreasing vessel length yielded increasing numbers of reverse flow segments until a maximum value was reached, after which more interconnected networks and smaller average vessel lengths yielded a decrease in the number of reverse flow segments. Xylem vessel relays also encouraged the formation of reverse flow segments. Based on the calculated flow rates in the xylem network, the downward spread of Xylella fastidiosa bacteria in grape stems was modeled, and reverse flow was shown to be an additional mechanism for the movement of bacteria to the trunk of grapevine.

  16. Automatic registration between 3D intra-operative ultrasound and pre-operative CT images of the liver based on robust edge matching

    NASA Astrophysics Data System (ADS)

    Nam, Woo Hyun; Kang, Dong-Goo; Lee, Duhgoon; Lee, Jae Young; Ra, Jong Beom

    2012-01-01

    The registration of a three-dimensional (3D) ultrasound (US) image with a computed tomography (CT) or magnetic resonance image is beneficial in various clinical applications such as diagnosis and image-guided intervention of the liver. However, conventional methods usually require a time-consuming and inconvenient manual process for pre-alignment, and the success of this process strongly depends on the proper selection of initial transformation parameters. In this paper, we present an automatic feature-based affine registration procedure of 3D intra-operative US and pre-operative CT images of the liver. In the registration procedure, we first segment vessel lumens and the liver surface from a 3D B-mode US image. We then automatically estimate an initial registration transformation by using the proposed edge matching algorithm. The algorithm finds the most likely correspondences between the vessel centerlines of both images in a non-iterative manner based on a modified Viterbi algorithm. Finally, the registration is iteratively refined on the basis of the global affine transformation by jointly using the vessel and liver surface information. The proposed registration algorithm is validated on synthesized datasets and 20 clinical datasets, through both qualitative and quantitative evaluations. Experimental results show that automatic registration can be successfully achieved between 3D B-mode US and CT images even with a large initial misalignment.

  17. Pattern Recognition Of Blood Vessel Networks In Ocular Fundus Images

    NASA Astrophysics Data System (ADS)

    Akita, K.; Kuga, H.

    1982-11-01

    We propose a computer method of recognizing blood vessel networks in color ocular fundus images which are used in the mass diagnosis of adult diseases such as hypertension and diabetes. A line detection algorithm is applied to extract the blood vessels, and the skeleton patterns of them are made to analyze and describe their structures. The recognition of line segments of arteries and/or veins in the vessel networks consists of three stages. First, a few segments which satisfy a certain constraint are picked up and discriminated as arteries or veins. This is the initial labeling. Then the remaining unknown ones are labeled by utilizing the physical level knowledge. We propose two schemes for this stage : a deterministic labeling and a probabilistic relaxation labeling. Finally the label of each line segment is checked so as to minimize the total number of labeling contradictions. Some experimental results are also presented.

  18. A web-based computer aided system for liver surgery planning: initial implementation on RayPlus

    NASA Astrophysics Data System (ADS)

    Luo, Ming; Yuan, Rong; Sun, Zhi; Li, Tianhong; Xie, Qingguo

    2016-03-01

    At present, computer aided systems for liver surgery design and risk evaluation are widely used in clinical all over the world. However, most systems are local applications that run on high-performance workstations, and the images have to processed offline. Compared with local applications, a web-based system is accessible anywhere and for a range of regardless of relative processing power or operating system. RayPlus (http://rayplus.life.hust.edu.cn), a B/S platform for medical image processing, was developed to give a jump start on web-based medical image processing. In this paper, we implement a computer aided system for liver surgery planning on the architecture of RayPlus. The system consists of a series of processing to CT images including filtering, segmentation, visualization and analyzing. Each processing is packaged into an executable program and runs on the server side. CT images in DICOM format are processed step by to interactive modeling on browser with zero-installation and server-side computing. The system supports users to semi-automatically segment the liver, intrahepatic vessel and tumor from the pre-processed images. Then, surface and volume models are built to analyze the vessel structure and the relative position between adjacent organs. The results show that the initial implementation meets satisfactorily its first-order objectives and provide an accurate 3D delineation of the liver anatomy. Vessel labeling and resection simulation are planned to add in the future. The system is available on Internet at the link mentioned above and an open username for testing is offered.

  19. Thickness dependent tortuosity estimation for retinal blood vessels.

    PubMed

    Azegrouz, Hind; Trucco, Emanuele; Dhillon, Baljean; MacGillivray, Thomas; MacCormick, I J

    2006-01-01

    This paper describes a framework for the automated estimation of vessel tortuosity in retinal images. We introduce a new tortuosity metric that takes into account vessel thickness, yielding estimates plausibly closer to intuition and medical judgement than those from previous metrics. We also propose an algorithm identifying automatically a vasculature segment connecting two points specified manually. Starting from a binary image of the vasculature, the algorithm computes a skeletal (medial axis) representation on which all terminal and branching points are located. This is then converted to a graph representation including connectivity as well as thickness information for all vessels. Target segments for tortuosity estimation are identified automatically from end points selected manually using a shortest-path algorithm. Results are presented and compared with those provided by clinical classification on 50 vessels from DRIVE images. An overall agreement with clinical judgement of 92.4% is achieved, superior to that of comparison measures.

  20. VESSELS IN SOME APOCYNACEAE

    PubMed Central

    Nag, Anita; Kshetrapal, Shashikala

    1988-01-01

    In the present investigation vessels of 24 species of the family Apocynaceae have been studied. Lot of variation exist in the size and shape of vessels, number of perforation plates and intervascular thickening of walls in the taxa of Apocynaceae. PMID:22557619

  1. REUSABLE REACTION VESSEL

    DOEpatents

    Soine, T.S.

    1963-02-26

    This patent shows a reusable reaction vessel for such high temperature reactions as the reduction of actinide metal chlorides by calcium metal. The vessel consists of an outer metal shell, an inner container of refractory material such as sintered magnesia, and between these, a bed of loose refractory material impregnated with thermally conductive inorganic salts. (AEC)

  2. Imprinted Clay Coil Vessels

    ERIC Educational Resources Information Center

    Lohr, Tresa Rae

    2006-01-01

    The author teaches clay vessel construction in the fifth grade, and it is amazing what can be accomplished in one forty-five minute period when the expectations are clarified in the initial lesson. The author introduces clay coil vessels with a discussion of the sources of clay and how clay relates to fifth-grade science curriculum concepts such…

  3. Imprinted Clay Coil Vessels

    ERIC Educational Resources Information Center

    Lohr, Tresa Rae

    2006-01-01

    The author teaches clay vessel construction in the fifth grade, and it is amazing what can be accomplished in one forty-five minute period when the expectations are clarified in the initial lesson. The author introduces clay coil vessels with a discussion of the sources of clay and how clay relates to fifth-grade science curriculum concepts such…

  4. Dual shell pressure balanced vessel

    DOEpatents

    Fassbender, Alexander G.

    1992-01-01

    A dual-wall pressure balanced vessel for processing high viscosity slurries at high temperatures and pressures having an outer pressure vessel and an inner vessel with an annular space between the vessels pressurized at a pressure slightly less than or equivalent to the pressure within the inner vessel.

  5. Visualization of risk structures for interactive planning of image guided radiofrequency ablation of liver tumors

    NASA Astrophysics Data System (ADS)

    Rieder, Christian; Schwier, Michael; Weihusen, Andreas; Zidowitz, Stephan; Peitgen, Heinz-Otto

    2009-02-01

    Image guided radiofrequency ablation (RFA) is becoming a standard procedure as a minimally invasive method for tumor treatment in the clinical routine. The visualization of pathological tissue and potential risk structures like vessels or important organs gives essential support in image guided pre-interventional RFA planning. In this work our aim is to present novel visualization techniques for interactive RFA planning to support the physician with spatial information of pathological structures as well as the finding of trajectories without harming vitally important tissue. Furthermore, we illustrate three-dimensional applicator models of different manufactures combined with corresponding ablation areas in homogenous tissue, as specified by the manufacturers, to enhance the estimated amount of cell destruction caused by ablation. The visualization techniques are embedded in a workflow oriented application, designed for the use in the clinical routine. To allow a high-quality volume rendering we integrated a visualization method using the fuzzy c-means algorithm. This method automatically defines a transfer function for volume visualization of vessels without the need of a segmentation mask. However, insufficient visualization results of the displayed vessels caused by low data quality can be improved using local vessel segmentation in the vicinity of the lesion. We also provide an interactive segmentation technique of liver tumors for the volumetric measurement and for the visualization of pathological tissue combined with anatomical structures. In order to support coagulation estimation with respect to the heat-sink effect of the cooling blood flow which decreases thermal ablation, a numerical simulation of the heat distribution is provided.

  6. Liver Diseases

    MedlinePlus

    ... remove poisons. There are many kinds of liver diseases. Viruses cause some of them, like hepatitis A, ... the skin, can be one sign of liver disease. Cancer can affect the liver. You could also ...

  7. Liver biopsy

    MedlinePlus

    Biopsy - liver; Percutaneous biopsy ... the biopsy needle to be inserted into the liver. This is often done by using ultrasound. The ... the chance of damage to the lung or liver. The needle is removed quickly. Pressure will be ...

  8. Dissolver vessel bottom assembly

    DOEpatents

    Kilian, Douglas C.

    1976-01-01

    An improved bottom assembly is provided for a nuclear reactor fuel reprocessing dissolver vessel wherein fuel elements are dissolved as the initial step in recovering fissile material from spent fuel rods. A shock-absorbing crash plate with a convex upper surface is disposed at the bottom of the dissolver vessel so as to provide an annular space between the crash plate and the dissolver vessel wall. A sparging ring is disposed within the annular space to enable a fluid discharged from the sparging ring to agitate the solids which deposit on the bottom of the dissolver vessel and accumulate in the annular space. An inlet tangential to the annular space permits a fluid pumped into the annular space through the inlet to flush these solids from the dissolver vessel through tangential outlets oppositely facing the inlet. The sparging ring is protected against damage from the impact of fuel elements being charged to the dissolver vessel by making the crash plate of such a diameter that the width of the annular space between the crash plate and the vessel wall is less than the diameter of the fuel elements.

  9. Reactor vessel support system

    DOEpatents

    Golden, Martin P.; Holley, John C.

    1982-01-01

    A reactor vessel support system includes a support ring at the reactor top supported through a box ring on a ledge of the reactor containment. The box ring includes an annular space in the center of its cross-section to reduce heat flow and is keyed to the support ledge to transmit seismic forces from the reactor vessel to the containment structure. A coolant channel is provided at the outside circumference of the support ring to supply coolant gas through the keyways to channels between the reactor vessel and support ledge into the containment space.

  10. Confinement Vessel Dynamic Analysis

    SciTech Connect

    R. Robert Stevens; Stephen P. Rojas

    1999-08-01

    A series of hydrodynamic and structural analyses of a spherical confinement vessel has been performed. The analyses used a hydrodynamic code to estimate the dynamic blast pressures at the vessel's internal surfaces caused by the detonation of a mass of high explosive, then used those blast pressures as applied loads in an explicit finite element model to simulate the vessel's structural response. Numerous load cases were considered. Particular attention was paid to the bolted port connections and the O-ring pressure seals. The analysis methods and results are discussed, and comparisons to experimental results are made.

  11. Disposal of Vessels at Sea

    EPA Pesticide Factsheets

    Vessel disposal general permits are issued by the EPA under the Marine Protection, Research and Sanctuaries Act. Information is provided for vessel disposal permit applicants and where to dispose a vessel.

  12. Retinal vessel oximetry-calibration, compensation for vessel diameter and fundus pigmentation, and reproducibility.

    PubMed

    Hammer, Martin; Vilser, Walthard; Riemer, Thomas; Schweitzer, Dietrich

    2008-01-01

    The purpose of this study was to measure the hemoglobin oxygenation in retinal vessels and to evaluate the sensitivity and reproducibility of the measurement. Using a fundus camera equipped with a special dual wavelength transmission filter and a color charge-coupled device camera, two monochromatic fundus images at 548 and 610 nm were recorded simultaneously. The optical densities of retinal vessels for both wavelengths and their ratio, which is known to be proportional to the oxygen saturation, were calculated. From 50-deg images, the used semiautomatic vessel recognition and tracking algorithm recognized and measured vessels of 100 microm or more in diameter. On average, arterial and venous oxygen saturations were measured at 98+/-10.1% and 65+/-11.7%, respectively. For measurements in the same vessel segments from the five images per subject, standard deviations of 2.52% and 3.25% oxygen saturation were found in arteries and veins, respectively. Respiration of 100% oxygen increased the mean arterial and venous oxygen saturation by 2% and 7% respectively. A simple system for noninvasive optical oximetry, consisting of a special filter in a fundus camera and software, was introduced. It is able to measure the oxygen saturation in retinal branch vessels with reproducibility and sensitivity suitable for clinical investigations.

  13. Automatic extraction of coronary vessels from digital subtraction angiography

    NASA Astrophysics Data System (ADS)

    Tang, Songyuan; Wen, Junhai; Wang, Yongtian; Chen, Yan-wei

    2007-03-01

    In the X-ray coronary digital subtraction angiography, there are serious motion artifacts and noises, and backgrounds such as ribs, spine, cathers and etc, which are tube structures and like vessels. It's difficult to separate vessels from the background automatically if they are close each other. In this paper, an automatic extraction of coronary vessels from X-ray digital subtraction angiography is proposed. We used edge preserving smooth filter to reduce the noises in the images and keep the vessel edge firstly. Then affine and B-spline based FFD nonrigid registration is applied to the images. Compared with the segmentation method, the proposed method can remove background greatly and extract the coronary vessel very well.

  14. Vessel extraction in X-ray angiograms using deep learning.

    PubMed

    Nasr-Esfahani, E; Samavi, S; Karimi, N; Soroushmehr, S M R; Ward, K; Jafari, M H; Felfeliyan, B; Nallamothu, B; Najarian, K

    2016-08-01

    Coronary artery disease (CAD) is the most common type of heart disease which is the leading cause of death all over the world. X-ray angiography is currently the gold standard imaging technique for CAD diagnosis. These images usually suffer from low quality and presence of noise. Therefore, vessel enhancement and vessel segmentation play important roles in CAD diagnosis. In this paper a deep learning approach using convolutional neural networks (CNN) is proposed for detecting vessel regions in angiography images. Initially, an input angiogram is preprocessed to enhance its contrast. Afterward, the image is evaluated using patches of pixels and the network determines the vessel and background regions. A set of 1,040,000 patches is used in order to train the deep CNN. Experimental results on angiography images of a dataset show that our proposed method has a superior performance in extraction of vessel regions.

  15. Improvement of retinal blood vessel detection using morphological component analysis.

    PubMed

    Imani, Elaheh; Javidi, Malihe; Pourreza, Hamid-Reza

    2015-03-01

    Detection and quantitative measurement of variations in the retinal blood vessels can help diagnose several diseases including diabetic retinopathy. Intrinsic characteristics of abnormal retinal images make blood vessel detection difficult. The major problem with traditional vessel segmentation algorithms is producing false positive vessels in the presence of diabetic retinopathy lesions. To overcome this problem, a novel scheme for extracting retinal blood vessels based on morphological component analysis (MCA) algorithm is presented in this paper. MCA was developed based on sparse representation of signals. This algorithm assumes that each signal is a linear combination of several morphologically distinct components. In the proposed method, the MCA algorithm with appropriate transforms is adopted to separate vessels and lesions from each other. Afterwards, the Morlet Wavelet Transform is applied to enhance the retinal vessels. The final vessel map is obtained by adaptive thresholding. The performance of the proposed method is measured on the publicly available DRIVE and STARE datasets and compared with several state-of-the-art methods. An accuracy of 0.9523 and 0.9590 has been respectively achieved on the DRIVE and STARE datasets, which are not only greater than most methods, but are also superior to the second human observer's performance. The results show that the proposed method can achieve improved detection in abnormal retinal images and decrease false positive vessels in pathological regions compared to other methods. Also, the robustness of the method in the presence of noise is shown via experimental result.

  16. Pressurized Vessel Slurry Pumping

    SciTech Connect

    Pound, C.R.

    2001-09-17

    This report summarizes testing of an alternate ''pressurized vessel slurry pumping'' apparatus. The principle is similar to rural domestic water systems and ''acid eggs'' used in chemical laboratories in that material is extruded by displacement with compressed air.

  17. Vessel Sewage Discharges

    EPA Pesticide Factsheets

    Vessel sewage discharges are regulated under Section 312 of the Clean Water Act, which is jointly implemented by the EPA and Coast Guard. This homepage links to information on marine sanitation devices and no discharge zones.

  18. LANL Robotic Vessel Scanning

    SciTech Connect

    Webber, Nels W.

    2015-11-25

    Los Alamos National Laboratory in J-1 DARHT Operations Group uses 6ft spherical vessels to contain hazardous materials produced in a hydrodynamic experiment. These contaminated vessels must be analyzed by means of a worker entering the vessel to locate, measure, and document every penetration mark on the vessel. If the worker can be replaced by a highly automated robotic system with a high precision scanner, it will eliminate the risks to the worker and provide management with an accurate 3D model of the vessel presenting the existing damage with the flexibility to manipulate the model for better and more in-depth assessment.The project was successful in meeting the primary goal of installing an automated system which scanned a 6ft vessel with an elapsed time of 45 minutes. This robotic system reduces the total time for the original scope of work by 75 minutes and results in excellent data accumulation and transmission to the 3D model imaging program.

  19. Interaction of antithrombin III with bovine aortic segments. Role of heparin in binding and enhanced anticoagulant activity

    SciTech Connect

    Stern, D.; Nawroth, P.; Marcum, J.; Handley, D.; Kisiel, W.; Rosenberg, R.; Stern, K.

    1985-01-01

    Bovine antithrombin III (AT III) interaction with the luminal surface of bovine aortic segments with a continuous layer of endothelium was examined. Incubation of /sup 125/I-AT III with vessel segments, previously washed free of endogenous AT III, demonstrated specific, time-dependent binding to the protease inhibitor to the endothelium. Half-maximal binding was observed at an added AT III concentration of 14 nM. Binding of /sup 125/I-AT III to the vessel wall was reversible (50% dissociated in 4 min), and addition of either heparin or Factor Xa accelerated displacement of /sup 125/I-AT III from the vessel segment. Dissociation of /sup 125/I-AT III from the vessel segment in the presence of factor Xa coincided with the formation of a Factor Xa-/sup 125/I-AT III complex. Inactivation of Factor IXa and Factor Xa by AT III was facilitated in the presence of vessel segments. Pretreatment of vessel segments with highly purified Flavobacterium heparinase precluded the vessel-dependent augmentation of AT III anticoagulant activity as well as specific binding of /sup 125/I-AT III to the vessel endothelium. In contrast, pretreatment of the vessel segments with chrondroitinases (ABC or AC) had no detectable effect on /sup 125/I-AT III binding or on AT III anticoagulant activity. AT III binding to vessel segments was competitively inhibited by increasing concentration of platelet factor 4. Binding of the protease inhibitor to vessel segments was inhibited by chemical modification of AT III lysyl or tryptophan residues. These AT III derivatives retained progressive inhibitory activity. These data suggest that heparin-like molecules are present on the aortic vessel wall and mediate binding of AT III to the vessel surface, as well as enhancing the anticoagulant activity of AT III at these sites.

  20. Intuitionistic fuzzy segmentation of medical images.

    PubMed

    Chaira, Tamalika

    2010-06-01

    This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy set theory to segment blood vessels and also the blood cells in pathological images. This type of segmentation is very important in detecting different types of human diseases, e.g., an increase in the number of vessels may lead to cancer in prostates, mammary, etc. The medical images are not properly illuminated, and segmentation in that case becomes very difficult. A novel image segmentation approach using intuitionistic fuzzy set theory and a new membership function is proposed using restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. Local thresholding is applied to threshold medical images. The results showed a much better performance on poor contrast medical images, where almost all the blood vessels and blood cells are visible properly. There are several fuzzy and intuitionistic fuzzy thresholding methods, but these methods are not related to the medical images. To make a comparison with the proposed method with other thresholding methods, the method is compared with six nonfuzzy, fuzzy, and intuitionistic fuzzy methods.

  1. Reductions in post-hepatectomy liver failure and related mortality after implementation of the LiMAx algorithm in preoperative work-up: a single-centre analysis of 1170 hepatectomies of one or more segments

    PubMed Central

    Jara, Maximilian; Reese, Tim; Malinowski, Maciej; Valle, Erika; Seehofer, Daniel; Puhl, Gero; Neuhaus, Peter; Pratschke, Johann; Stockmann, Martin

    2015-01-01

    Objectives Post-hepatectomy liver failure has a major impact on patient outcome. This study aims to explore the impact of the integration of a novel patient-centred evaluation, the LiMAx algorithm, on perioperative patient outcome after hepatectomy. Methods Trends in perioperative variables and morbidity and mortality rates in 1170 consecutive patients undergoing elective hepatectomy between January 2006 and December 2011 were analysed retrospectively. Propensity score matching was used to compare the effects on morbidity and mortality of the integration of the LiMAx algorithm into clinical practice. Results Over the study period, the proportion of complex hepatectomies increased from 29.1% in 2006 to 37.7% in 2011 (P = 0.034). Similarly, the proportion of patients with liver cirrhosis selected for hepatic surgery rose from 6.9% in 2006 to 11.3% in 2011 (P = 0.039). Despite these increases, rates of post-hepatectomy liver failure fell from 24.7% in 2006 to 9.0% in 2011 (P < 0.001) and liver failure-related postoperative mortality decreased from 4.0% in 2006 to 0.9% in 2011 (P = 0.014). Propensity score matching was associated with reduced rates of post-hepatectomy liver failure [24.7% (n = 77) versus 11.2% (n = 35); P < 0.001] and related mortality [3.8% (n = 12) versus 1.0% (n = 3); P = 0.035]. Conclusions Postoperative liver failure and postoperative liver failure-related mortality decreased in patients undergoing hepatectomy following the implementation of the LiMAx algorithm. PMID:26058324

  2. Retinal imaging analysis based on vessel detection.

    PubMed

    Jamal, Arshad; Hazim Alkawaz, Mohammed; Rehman, Amjad; Saba, Tanzila

    2017-03-13

    With an increase in the advancement of digital imaging and computing power, computationally intelligent technologies are in high demand to be used in ophthalmology cure and treatment. In current research, Retina Image Analysis (RIA) is developed for optometrist at Eye Care Center in Management and Science University. This research aims to analyze the retina through vessel detection. The RIA assists in the analysis of the retinal images and specialists are served with various options like saving, processing and analyzing retinal images through its advanced interface layout. Additionally, RIA assists in the selection process of vessel segment; processing these vessels by calculating its diameter, standard deviation, length, and displaying detected vessel on the retina. The Agile Unified Process is adopted as the methodology in developing this research. To conclude, Retina Image Analysis might help the optometrist to get better understanding in analyzing the patient's retina. Finally, the Retina Image Analysis procedure is developed using MATLAB (R2011b). Promising results are attained that are comparable in the state of art.

  3. Acrylic vessel cleaning tests

    SciTech Connect

    Earle, D.; Hahn, R.L.; Boger, J.; Bonvin, E.

    1997-02-26

    The acrylic vessel as constructed is dirty. The dirt includes blue tape, Al tape, grease pencil, gemak, the glue or residue form these tapes, finger prints and dust of an unknown composition but probably mostly acrylic dust. This dirt has to be removed and once removed, the vessel has to be kept clean or at least to be easily cleanable at some future stage when access becomes much more difficult. The authors report on the results of a series of tests designed: (a) to prepare typical dirty samples of acrylic; (b) to remove dirt stuck to the acrylic surface; and (c) to measure the optical quality and Th concentration after cleaning. Specifications of the vessel call for very low levels of Th which could come from tape residues, the grease pencil, or other sources of dirt. This report does not address the concerns of how to keep the vessel clean after an initial cleaning and during the removal of the scaffolding. Alconox is recommended as the cleaner of choice. This acrylic vessel will be used in the Sudbury Neutrino Observatory.

  4. Reactor vessel annealing system

    DOEpatents

    Miller, Phillip E.; Katz, Leonoard R.; Nath, Raymond J.; Blaushild, Ronald M.; Tatch, Michael D.; Kordalski, Frank J.; Wykstra, Donald T.; Kavalkovich, William M.

    1991-01-01

    A system for annealing a vessel (14) in situ by heating the vessel (14) to a defined temperature, composed of: an electrically operated heater assembly (10) insertable into the vessel (14) for heating the vessel (14) to the defined temperature; temperature monitoring components positioned relative to the heater assembly (10) for monitoring the temperature of the vessel (14); a controllable electric power supply unit (32-60) for supplying electric power required by the heater assembly (10); a control unit (80-86) for controlling the power supplied by the power supply unit (32-60); a first vehicle (2) containing the power supply unit (32-60); a second vehicle (4) containing the control unit (80-86); power conductors (18,22) connectable between the power supply unit (32-60) and the heater unit (10) for delivering the power supplied by the power supply unit (32-60) to the heater assembly (10); signal conductors (20,24) connectable between the temperature monitoring components and the control unit (80-86) for delivering temperature indicating signals from the temperature monitoring components to the control unit (80-86); and control conductors (8) connectable between the control unit (80-86) and the power supply unit (32-60) for delivering to the power supply unit (32-60) control signals for controlling the level of power supplied by the power supply unit (32-60) to the heater assembly (10).

  5. [New Approach of Fundus Image Segmentation Evaluation Based on Topology Structure].

    PubMed

    Sheng, Hanwei; Dai, Peishan; Liu, Zhihang; Zhang-Wen, Miaoyun; Zhao, Yali; Fan, Min

    2015-10-01

    In view of the evaluation of fundus image segmentation, a new evaluation method was proposed to make up insufficiency of the traditional evaluation method which only considers the overlap of pixels and neglects topology structure of the retinal vessel. Mathematical morphology and thinning algorithm were used to obtain the retinal vascular topology structure. Then three features of retinal vessel, including mutual information, correlation coefficient and ratio of nodes, were calculated. The features of the thinned images taken as topology structure of blood vessel were used to evaluate retinal image segmentation. The manually-labeled images and their eroded ones of STARE database were used in the experiment. The result showed that these features, including mutual information, correlation coefficient and ratio of nodes, could be used to evaluate the segmentation quality of retinal vessel on fundus image through topology structure, and the algorithm was simple. The method is of significance to the supplement of traditional segmentation evaluation of retinal vessel on fundus image.

  6. Developmental control of segment numbers in vertebrates

    PubMed Central

    Gomez, Céline; Pourquié, Olivier

    2011-01-01

    Segmentation or metamery in vertebrates is best illustrated by the repetition of the vertebrae and ribs, their associated skeletal muscles and blood vessels, and the spinal nerves and ganglia. The segment number varies tremendously among the different vertebrate species, ranging from as few as six vertebrae in some frogs to as many as several hundred in some snakes and fish. In vertebrates, metameric segments or somites form sequentially during body axis formation. This results in the embryonic axis becoming entirely segmented into metameric units from the level of the otic vesicle almost to the very tip of the tail. The total segment number mostly depends on two parameters: (1) the control of the posterior growth of the body axis during somitogenesis—more same-size segments can be formed in a longer axis and (2) segment size—more smaller-size segments can be formed in a same-size body axis. During evolution, independent variations of these parameters could explain the huge diversity in segment numbers observed among vertebrate species. These variations in segment numbers are accompanied by diversity in the regionalization of the vertebral column. For example, amniotes can exhibit up to five different types of vertebrae: cervical, thoracic, lumbar, sacral and caudal, the number of which varies according to the species. This regionalization of the vertebral column is controlled by the Hox family of transcription factors. We propose that during development, dissociation of the Hox- and segmentation-clock-dependent vertebral patterning systems explains the enormous diversity of vertebral formulae observed in vertebrates. PMID:19621429

  7. A Segmentation Framework of Pulmonary Nodules in Lung CT Images.

    PubMed

    Mukhopadhyay, Sudipta

    2016-02-01

    Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The well-circumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/part-solid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

  8. Segmentation of the foveal microvasculature using deep learning networks

    NASA Astrophysics Data System (ADS)

    Prentašić, Pavle; Heisler, Morgan; Mammo, Zaid; Lee, Sieun; Merkur, Andrew; Navajas, Eduardo; Beg, Mirza Faisal; Šarunić, Marinko; Lončarić, Sven

    2016-07-01

    Accurate segmentation of the retinal microvasculature is a critical step in the quantitative analysis of the retinal circulation, which can be an important marker in evaluating the severity of retinal diseases. As manual segmentation remains the gold standard for segmentation of optical coherence tomography angiography (OCT-A) images, we present a method for automating the segmentation of OCT-A images using deep neural networks (DNNs). Eighty OCT-A images of the foveal region in 12 eyes from 6 healthy volunteers were acquired using a prototype OCT-A system and subsequently manually segmented. The automated segmentation of the blood vessels in the OCT-A images was then performed by classifying each pixel into vessel or nonvessel class using deep convolutional neural networks. When the automated results were compared against the manual segmentation results, a maximum mean accuracy of 0.83 was obtained. When the automated results were compared with inter and intrarater accuracies, the automated results were shown to be comparable to the human raters suggesting that segmentation using DNNs is comparable to a second manual rater. As manually segmenting the retinal microvasculature is a tedious task, having a reliable automated output such as automated segmentation by DNNs, is an important step in creating an automated output.

  9. Segmentation and segment connection of obstructed colon

    NASA Astrophysics Data System (ADS)

    Medved, Mario; Truyen, Roel; Likar, Bostjan; Pernus, Franjo

    2004-05-01

    Segmentation of colon CT images is the main factor that inhibits automation of virtual colonoscopy. There are two main reasons that make efficient colon segmentation difficult. First, besides the colon, the small bowel, lungs, and stomach are also gas-filled organs in the abdomen. Second, peristalsis or residual feces often obstruct the colon, so that it consists of multiple gas-filled segments. In virtual colonoscopy, it is very useful to automatically connect the centerlines of these segments into a single colon centerline. Unfortunately, in some cases this is a difficult task. In this study a novel method for automated colon segmentation and connection of colon segments' centerlines is proposed. The method successfully combines features of segments, such as centerline and thickness, with information on main colon segments. The results on twenty colon cases show that the method performs well in cases of small obstructions of the colon. Larger obstructions are mostly also resolved properly, especially if they do not appear in the sigmoid part of the colon. Obstructions in the sigmoid part of the colon sometimes cause improper classification of the small bowel segments. If a segment is too small, it is classified as the small bowel segment. However, such misclassifications have little impact on colon analysis.

  10. Sapphire tube pressure vessel

    DOEpatents

    Outwater, John O.

    2000-01-01

    A pressure vessel is provided for observing corrosive fluids at high temperatures and pressures. A transparent Teflon bag contains the corrosive fluid and provides an inert barrier. The Teflon bag is placed within a sapphire tube, which forms a pressure boundary. The tube is received within a pipe including a viewing window. The combination of the Teflon bag, sapphire tube and pipe provides a strong and inert pressure vessel. In an alternative embodiment, tie rods connect together compression fittings at opposite ends of the sapphire tube.

  11. Sapphire tube pressure vessel

    SciTech Connect

    Outwater, J.O.

    2000-05-23

    A pressure vessel is provided for observing corrosive fluids at high temperatures and pressures. A transparent Teflon bag contains the corrosive fluid and provides an inert barrier. The Teflon bag is placed within a sapphire tube, which forms a pressure boundary. The tube is received within a pipe including a viewing window. The combination of the Teflon bag, sapphire tube and pipe provides a strong and inert pressure vessel. In an alternative embodiment, tie rods connect together compression fittings at opposite ends of the sapphire tube.

  12. Interactive vessel-tracking with a hybrid model-based and graph-based approach

    NASA Astrophysics Data System (ADS)

    Fritz, Dominik; Beck, Thomas; Scheuering, Michael

    2009-02-01

    For assessment of coronary artery disease (CAD) and peripheral artery disease (PAD) the automatic extraction of vessel centerlines is a crucial technology. In the most common approach two seed points have to be manually placed in the vessel and the centerline is automatically computed between these points. This methodology is appropriate for the quantitative analysis of single vessel segments. However, for an interactive and fast reading of complete datasets a more interactive approach would be beneficial. In this work we introduce an interactive vessel-tracking approach which eases the reading of cardiac and vascular CTA datasets. Starting with a single seed point a local vessel-tracking is initialized and extended in both directions while the user "walks" along the vessel centerline. For a robust tracking of a wide variety of vessel diameters, from coronaries to the aorta, we combine a local A*-graph-search for tiny vessels and a model-based tracking for larger vessels to an hybrid model-based and graph-based approach. In order to further ease the reading of cardiac and vascular CTA datasets, we introduce a subdivision of the interactively acquired centerline into segments that can be approximated by a single plane. This subdivision allows the visualization of the vessel in optimally oriented multi-planar reformations (MPR). The proposed visualization combines the advantage of a curved planar reformation (CPR), showing a large part of the vessel in one view, with the benefits of a MPR, having a non distorted more trustable image.

  13. Automatic classification of retinal vessels into arteries and veins

    NASA Astrophysics Data System (ADS)

    Niemeijer, Meindert; van Ginneken, Bram; Abràmoff, Michael D.

    2009-02-01

    Separating the retinal vascular tree into arteries and veins is important for quantifying vessel changes that preferentially affect either the veins or the arteries. For example the ratio of arterial to venous diameter, the retinal a/v ratio, is well established to be predictive of stroke and other cardiovascular events in adults, as well as the staging of retinopathy of prematurity in premature infants. This work presents a supervised, automatic method that can determine whether a vessel is an artery or a vein based on intensity and derivative information. After thinning of the vessel segmentation, vessel crossing and bifurcation points are removed leaving a set of vessel segments containing centerline pixels. A set of features is extracted from each centerline pixel and using these each is assigned a soft label indicating the likelihood that it is part of a vein. As all centerline pixels in a connected segment should be the same type we average the soft labels and assign this average label to each centerline pixel in the segment. We train and test the algorithm using the data (40 color fundus photographs) from the DRIVE database1 with an enhanced reference standard. In the enhanced reference standard a fellowship trained retinal specialist (MDA) labeled all vessels for which it was possible to visually determine whether it was a vein or an artery. After applying the proposed method to the 20 images of the DRIVE test set we obtained an area under the receiver operator characteristic (ROC) curve of 0.88 for correctly assigning centerline pixels to either the vein or artery classes.

  14. Lymphatic vessels clean up your arteries.

    PubMed

    Fernández-Hernando, Carlos

    2013-04-01

    Reverse cholesterol transport (RCT) is the pathway by which cholesterol accumulated in peripheral tissues, including the artery wall, is transported to the liver for excretion. There is strong evidence suggesting that interventions that increase macrophage cholesterol efflux and RCT would be antiatherogenic. In this issue of the JCI, Martel et al. investigate the contribution of lymphatic vasculature to RCT. Their results support the concept that the lymphatic vessel route is critical for RCT from atherosclerotic plaques. Therefore, strategies to improve lymphatic transport might be useful for treating atherosclerotic vascular disease.

  15. Multiquadric Spline-Based Interactive Segmentation of Vascular Networks

    PubMed Central

    Meena, Sachin; Surya Prasath, V. B.; Kassim, Yasmin M.; Maude, Richard J.; Glinskii, Olga V.; Glinsky, Vladislav V.; Huxley, Virginia H.; Palaniappan, Kannappan

    2016-01-01

    Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segment for quantitative applications due to challenging tissue preparation, imaging conditions, and thin, faint structures. Experimental results based on twenty epifluorescence images is used to illustrate the benefits of using a set of seed points to obtain fast and accurate interactive segmentation compared to four interactive and automatic segmentation approaches. PMID:28261011

  16. Multiquadric Spline-Based Interactive Segmentation of Vascular Networks.

    PubMed

    Meena, Sachin; Surya Prasath, V B; Kassim, Yasmin M; Maude, Richard J; Glinskii, Olga V; Glinsky, Vladislav V; Huxley, Virginia H; Palaniappan, Kannappan

    2016-08-01

    Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segm