An automated method for accurate vessel segmentation.

PubMed

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

2017-05-07

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

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.

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

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.

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.

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.

Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics.

PubMed

Moccia, Sara; De Momi, Elena; El Hadji, Sara; Mattos, Leonardo S

2018-05-01

Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic

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.

Segmentation of liver region with tumorous tissues

NASA Astrophysics Data System (ADS)

Zhang, Xuejun; Lee, Gobert; Tajima, Tetsuji; Kitagawa, Teruhiko; Kanematsu, Masayuki; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Yokoyama, Ryujiro; Kondo, Hiroshi; Hoshi, Hiroaki; Nawano, Shigeru; Shinozaki, Kenji

2007-03-01

Segmentation of an abnormal liver region based on CT or MR images is a crucial step in surgical planning. However, precisely carrying out this step remains a challenge due to either connectivities of the liver to other organs or the shape, internal texture, and homogeneity of liver that maybe extensively affected in case of liver diseases. Here, we propose a non-density based method for extracting the liver region containing tumor tissues by edge detection processing. False extracted regions are eliminated by a shape analysis method and thresholding processing. If the multi-phased images are available then the overall outcome of segmentation can be improved by subtracting two phase images, and the connectivities can be further eliminated by referring to the intensity on another phase image. Within an edge liver map, tumor candidates are identified by their different gray values relative to the liver. After elimination of the small and nonspherical over-extracted regions, the final liver region integrates the tumor region with the liver tissue. In our experiment, 40 cases of MDCT images were used and the result showed that our fully automatic method for the segmentation of liver region is effective and robust despite the presence of hepatic tumors within the liver.

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.

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

Automatic liver volume segmentation and fibrosis classification

NASA Astrophysics Data System (ADS)

Bal, Evgeny; Klang, Eyal; Amitai, Michal; Greenspan, Hayit

2018-02-01

In this work, we present an automatic method for liver segmentation and fibrosis classification in liver computed-tomography (CT) portal phase scans. The input is a full abdomen CT scan with an unknown number of slices, and the output is a liver volume segmentation mask and a fibrosis grade. A multi-stage analysis scheme is applied to each scan, including: volume segmentation, texture features extraction and SVM based classification. Data contains portal phase CT examinations from 80 patients, taken with different scanners. Each examination has a matching Fibroscan grade. The dataset was subdivided into two groups: first group contains healthy cases and mild fibrosis, second group contains moderate fibrosis, severe fibrosis and cirrhosis. Using our automated algorithm, we achieved an average dice index of 0.93 ± 0.05 for segmentation and a sensitivity of 0.92 and specificity of 0.81for classification. To the best of our knowledge, this is a first end to end automatic framework for liver fibrosis classification; an approach that, once validated, can have a great potential value in the clinic.

Multi-level deep supervised networks for retinal vessel segmentation.

PubMed

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

Retinal blood vessel segmentation using fully convolutional network with transfer learning.

PubMed

Jiang, Zhexin; Zhang, Hao; Wang, Yi; Ko, Seok-Bum

2018-04-26

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automated or computer-aided diagnosis systems. In this paper, a supervised method is presented based on a pre-trained fully convolutional network through transfer learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging. Meanwhile, additional unsupervised image post-processing techniques are applied to this proposed method so as to refine the final result. Extensive experiments have been conducted on DRIVE, STARE, CHASE_DB1 and HRF databases, and the accuracy of the cross-database test on these four databases is state-of-the-art, which also presents the high robustness of the proposed approach. This successful result has not only contributed to the area of automated retinal blood vessel segmentation but also supports the effectiveness of transfer learning when applying deep learning technique to medical imaging. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans

PubMed Central

2014-01-01

An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results. PMID:25276219

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.

Patch-based automatic retinal vessel segmentation in global and local structural context.

PubMed

Cao, Shuoying; Bharath, Anil A; Parker, Kim H; Ng, Jeffrey

2012-01-01

In this paper, we extend our published work [1] and propose an automated system to segment retinal vessel bed in digital fundus images with enough adaptability to analyze images from fluorescein angiography. This approach takes into account both the global and local context and enables both vessel segmentation and microvascular centreline extraction. These tools should allow researchers and clinicians to estimate and assess vessel diameter, capillary blood volume and microvascular topology for early stage disease detection, monitoring and treatment. Global vessel bed segmentation is achieved by combining phase-invariant orientation fields with neighbourhood pixel intensities in a patch-based feature vector for supervised learning. This approach is evaluated against benchmarks on the DRIVE database [2]. Local microvascular centrelines within Regions-of-Interest (ROIs) are segmented by linking the phase-invariant orientation measures with phase-selective local structure features. Our global and local structural segmentation can be used to assess both pathological structural alterations and microemboli occurrence in non-invasive clinical settings in a longitudinal study.

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

2017-07-01

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%. Copyright © 2017 Elsevier B.V. All rights reserved.

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

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

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

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

Zhou Chuan; Chan, H.-P.; Sahiner, Berkman

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 atmore » 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

A web-based procedure for liver segmentation in CT images

NASA Astrophysics Data System (ADS)

Yuan, Rong; Luo, Ming; Wang, Luyao; Xie, Qingguo

2015-03-01

Liver segmentation in CT images has been acknowledged as a basic and indispensable part in systems of computer aided liver surgery for operation design and risk evaluation. In this paper, we will introduce and implement a web-based procedure for liver segmentation to help radiologists and surgeons get an accurate result efficiently and expediently. Several clinical datasets are used to evaluate the accessibility and the accuracy. This procedure seems a promising approach for extraction of liver volumetry of various shapes. Moreover, it is possible for user to access the segmentation wherever the Internet is available without any specific machine.

Extreme liver resections with preservation of segment 4 only

PubMed Central

Balzan, Silvio Marcio Pegoraro; Gava, Vinícius Grando; Magalhães, Marcelo Arbo; Dotto, Marcelo Luiz

2017-01-01

AIM To evaluate safety and outcomes of a new technique for extreme hepatic resections with preservation of segment 4 only. METHODS The new method of extreme liver resection consists of a two-stage hepatectomy. The first stage involves a right hepatectomy with middle hepatic vein preservation and induction of left lobe congestion; the second stage involves a left lobectomy. Thus, the remnant liver is represented by the segment 4 only (with or without segment 1, ± S1). Five patients underwent the new two-stage hepatectomy (congestion group). Data from volumetric assessment made before the second stage was compared with that of 10 matched patients (comparison group) that underwent a single-stage right hepatectomy with middle hepatic vein preservation. RESULTS The two stages of the procedure were successfully carried out on all 5 patients. For the congestion group, the overall volume of the left hemiliver had increased 103% (mean increase from 438 mL to 890 mL) at 4 wk after the first stage of the procedure. Hypertrophy of the future liver remnant (i.e., segment 4 ± S1) was higher than that of segments 2 and 3 (144% vs 54%, respectively, P < 0.05). The median remnant liver volume-to-body weight ratio was 0.3 (range, 0.28-0.40) before the first stage and 0.8 (range, 0.45-0.97) before the second stage. For the comparison group, the rate of hypertrophy of the left liver after right hepatectomy with middle hepatic vein preservation was 116% ± 34%. Hypertrophy rates of segments 2 and 3 (123% ± 47%) and of segment 4 (108% ± 60%, P > 0.05) were proportional. The mean preoperative volume of segments 2 and 3 was 256 ± 64 cc and increased to 572 ± 257 cc after right hepatectomy. Mean preoperative volume of segment 4 increased from 211 ± 75 cc to 439 ± 180 cc after surgery. CONCLUSION The proposed method for extreme hepatectomy with preservation of segment 4 only represents a technique that could allow complete resection of multiple bilateral liver metastases. PMID

Automatic anatomical segmentation of the liver by separation planes

NASA Astrophysics Data System (ADS)

Boltcheva, Dobrina; Passat, Nicolas; Agnus, Vincent; Jacob-Da, Marie-Andrée, , Col; Ronse, Christian; Soler, Luc

2006-03-01

Surgical planning in oncological liver surgery is based on the location of the 8 anatomical segments according to Couinaud's definition and tumors inside these structures. The detection of the boundaries between the segments is then the first step of the preoperative planning. The proposed method, devoted to binary images of livers segmented from CT-scans, has been designed to delineate these segments. It automatically detects a set of landmarks using a priori anatomical knowledge and differential geometry criteria. These landmarks are then used to position the Couinaud's segments. Validations performed on 7 clinical cases tend to prove that the method is reliable for most of these separation planes.

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.

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. Copyright © 2011 Elsevier Inc. All rights reserved.

Active contour based segmentation of resected livers in CT images

NASA Astrophysics Data System (ADS)

Oelmann, Simon; Oyarzun Laura, Cristina; Drechsler, Klaus; Wesarg, Stefan

2015-03-01

The majority of state of the art segmentation algorithms are able to give proper results in healthy organs but not in pathological ones. However, many clinical applications require an accurate segmentation of pathological organs. The determination of the target boundaries for radiotherapy or liver volumetry calculations are examples of this. Volumetry measurements are of special interest after tumor resection for follow up of liver regrow. The segmentation of resected livers presents additional challenges that were not addressed by state of the art algorithms. This paper presents a snakes based algorithm specially developed for the segmentation of resected livers. The algorithm is enhanced with a novel dynamic smoothing technique that allows the active contour to propagate with different speeds depending on the intensities visible in its neighborhood. The algorithm is evaluated in 6 clinical CT images as well as 18 artificial datasets generated from additional clinical CT images.

Comparison of vessel enhancement algorithms applied to time-of-flight MRA images for cerebrovascular segmentation.

PubMed

Phellan, Renzo; Forkert, Nils D

2017-11-01

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 flux (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 5 healthy subjects and 10 patients with an arteriovenous malformation. Additionally, five synthetic angiographic datasets with corresponding ground truth segmentation were generated with three different noise levels (low, medium, and 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 nonenhanced 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

Automatic segmentation of cortical vessels in pre- and post-tumor resection laser range scan images

NASA Astrophysics Data System (ADS)

Ding, Siyi; Miga, Michael I.; Thompson, Reid C.; Garg, Ishita; Dawant, Benoit M.

2009-02-01

Measurement of intra-operative cortical brain movement is necessary to drive mechanical models developed to predict sub-cortical shift. At our institution, this is done with a tracked laser range scanner. This device acquires both 3D range data and 2D photographic images. 3D cortical brain movement can be estimated if 2D photographic images acquired over time can be registered. Previously, we have developed a method, which permits this registration using vessels visible in the images. But, vessel segmentation required the localization of starting and ending points for each vessel segment. Here, we propose a method, which automates the segmentation process further. This method involves several steps: (1) correction of lighting artifacts, (2) vessel enhancement, and (3) vessels' centerline extraction. Result obtained on 5 images obtained in the operating room suggests that our method is robust and is able to segment vessels reliably.

Surgical anatomy of segmental liver transplantation.

PubMed

Deshpande, R R; Heaton, N D; Rela, M

2002-09-01

The emergence of split and living donor liver transplantation has necessitated re-evaluation of liver anatomy in greater depth and from a different perspective than before. Early attempts at split liver transplantation were met with significant numbers of vascular and biliary complications. Technical innovations in this field have evolved largely by recognizing anatomical anomalies and variations at operation, and devising novel ways of dealing with them. This has led to increasing acceptance of these procedures and decreased morbidity and mortality rates, similar to those observed with whole liver transplantation. The following review is based on clinical experience of more than 180 split and living related liver transplantations in adults and children, performed over a 7-year period from 1994 to 2001. A comprehensive understanding and application of surgical anatomy of the liver is essential to improve and maintain the excellent results of segmental liver transplantation.

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.

Vessel co-option mediates resistance to anti-angiogenic therapy in liver metastases.

PubMed

Frentzas, Sophia; Simoneau, Eve; Bridgeman, Victoria L; Vermeulen, Peter B; Foo, Shane; Kostaras, Eleftherios; Nathan, Mark; Wotherspoon, Andrew; Gao, Zu-Hua; Shi, Yu; Van den Eynden, Gert; Daley, Frances; Peckitt, Clare; Tan, Xianming; Salman, Ayat; Lazaris, Anthoula; Gazinska, Patrycja; Berg, Tracy J; Eltahir, Zak; Ritsma, Laila; Van Rheenen, Jacco; Khashper, Alla; Brown, Gina; Nystrom, Hanna; Sund, Malin; Van Laere, Steven; Loyer, Evelyne; Dirix, Luc; Cunningham, David; Metrakos, Peter; Reynolds, Andrew R

2016-11-01

The efficacy of angiogenesis inhibitors in cancer is limited by resistance mechanisms that are poorly understood. Notably, instead of through the induction of angiogenesis, tumor vascularization can occur through the nonangiogenic mechanism of vessel co-option. Here we show that vessel co-option is associated with a poor response to the anti-angiogenic agent bevacizumab in patients with colorectal cancer liver metastases. Moreover, we find that vessel co-option is also prevalent in human breast cancer liver metastases, a setting in which results with anti-angiogenic therapy have been disappointing. In preclinical mechanistic studies, we found that cancer cell motility mediated by the actin-related protein 2/3 complex (Arp2/3) is required for vessel co-option in liver metastases in vivo and that, in this setting, combined inhibition of angiogenesis and vessel co-option is more effective than the inhibition of angiogenesis alone. Vessel co-option is therefore a clinically relevant mechanism of resistance to anti-angiogenic therapy and combined inhibition of angiogenesis and vessel co-option might be a warranted therapeutic strategy.

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.

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

Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation

NASA Astrophysics Data System (ADS)

Qin, Wenjian; Wu, Jia; Han, Fei; Yuan, Yixuan; Zhao, Wei; Ibragimov, Bulat; Gu, Jia; Xing, Lei

2018-05-01

Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

A variational approach to liver segmentation using statistics from multiple sources

NASA Astrophysics Data System (ADS)

Zheng, Shenhai; Fang, Bin; Li, Laquan; Gao, Mingqi; Wang, Yi

2018-01-01

Medical image segmentation plays an important role in digital medical research, and therapy planning and delivery. However, the presence of noise and low contrast renders automatic liver segmentation an extremely challenging task. In this study, we focus on a variational approach to liver segmentation in computed tomography scan volumes in a semiautomatic and slice-by-slice manner. In this method, one slice is selected and its connected component liver region is determined manually to initialize the subsequent automatic segmentation process. From this guiding slice, we execute the proposed method downward to the last one and upward to the first one, respectively. A segmentation energy function is proposed by combining the statistical shape prior, global Gaussian intensity analysis, and enforced local statistical feature under the level set framework. During segmentation, the shape of the liver shape is estimated by minimization of this function. The improved Chan-Vese model is used to refine the shape to capture the long and narrow regions of the liver. The proposed method was verified on two independent public databases, the 3D-IRCADb and the SLIVER07. Among all the tested methods, our method yielded the best volumetric overlap error (VOE) of 6.5 +/- 2.8 % , the best root mean square symmetric surface distance (RMSD) of 2.1 +/- 0.8 mm, the best maximum symmetric surface distance (MSD) of 18.9 +/- 8.3 mm in 3D-IRCADb dataset, and the best average symmetric surface distance (ASD) of 0.8 +/- 0.5 mm, the best RMSD of 1.5 +/- 1.1 mm in SLIVER07 dataset, respectively. The results of the quantitative comparison show that the proposed liver segmentation method achieves competitive segmentation performance with state-of-the-art techniques.

Automated liver segmentation using a normalized probabilistic atlas

NASA Astrophysics Data System (ADS)

Linguraru, Marius George; Li, Zhixi; Shah, Furhawn; Chin, See; Summers, Ronald M.

2009-02-01

Probabilistic atlases of anatomical organs, especially the brain and the heart, have become popular in medical image analysis. We propose the construction of probabilistic atlases which retain structural variability by using a size-preserving modified affine registration. The organ positions are modeled in the physical space by normalizing the physical organ locations to an anatomical landmark. In this paper, a liver probabilistic atlas is constructed and exploited to automatically segment liver volumes from abdominal CT data. The atlas is aligned with the patient data through a succession of affine and non-linear registrations. The overlap and correlation with manual segmentations are 0.91 (0.93 DICE coefficient) and 0.99 respectively. Little work has taken place on the integration of volumetric measures of liver abnormality to clinical evaluations, which rely on linear estimates of liver height. Our application measures the liver height at the mid-hepatic line (0.94 correlation with manual measurements) and indicates that its combination with volumetric estimates could assist the development of a noninvasive tool to assess hepatomegaly.

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. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Raghupathi, Laks; Lakare, Sarang

2009-02-01

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

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

Semiautomatic segmentation of liver metastases on volumetric CT images

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

Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng, E-mail: bz2166@cumc.columbia.edu

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 accuratelymore » 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

Tumor Burden Analysis on Computed Tomography by Automated Liver and Tumor Segmentation

PubMed Central

Linguraru, Marius George; Richbourg, William J.; Liu, Jianfei; Watt, Jeremy M.; Pamulapati, Vivek; Wang, Shijun; Summers, Ronald M.

2013-01-01

The paper presents the automated computation of hepatic tumor burden from abdominal CT images of diseased populations with images with inconsistent enhancement. The automated segmentation of livers is addressed first. A novel three-dimensional (3D) affine invariant shape parameterization is employed to compare local shape across organs. By generating a regular sampling of the organ's surface, this parameterization can be effectively used to compare features of a set of closed 3D surfaces point-to-point, while avoiding common problems with the parameterization of concave surfaces. From an initial segmentation of the livers, the areas of atypical local shape are determined using training sets. A geodesic active contour corrects locally the segmentations of the livers in abnormal images. Graph cuts segment the hepatic tumors using shape and enhancement constraints. Liver segmentation errors are reduced significantly and all tumors are detected. Finally, support vector machines and feature selection are employed to reduce the number of false tumor detections. The tumor detection true position fraction of 100% is achieved at 2.3 false positives/case and the tumor burden is estimated with 0.9% error. Results from the test data demonstrate the method's robustness to analyze livers from difficult clinical cases to allow the temporal monitoring of patients with hepatic cancer. PMID:22893379

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

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

Ibrahim, Saad M.; Lewandowski, Robert J.; Ryu, Robert K.

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.more » 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.« less

Radiographic response to yttrium-90 radioembolization in anterior versus posterior liver segments.

PubMed

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

2008-01-01

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(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.

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.

Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction.

PubMed

Almazroa, Ahmed; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

2017-01-01

We introduce here a new technique for segmenting optic cup using two-dimensional fundus images. Cup segmentation is the most challenging part of image processing of the optic nerve head due to the complexity of its structure. Using the blood vessels to segment the cup is important. Here, we report on blood vessel extraction using first a top-hat transform and Otsu's segmentation function to detect the curves in the blood vessels (kinks) which indicate the cup boundary. This was followed by an interval type-II fuzzy entropy procedure. Finally, the Hough transform was applied to approximate the cup boundary. The algorithm was evaluated on 550 fundus images from a large dataset, which contained three different sets of images, where the cup was manually marked by six ophthalmologists. On one side, the accuracy of the algorithm was tested on the three image sets independently. The final cup detection accuracy in terms of area and centroid was calculated to be 78.2% of 441 images. Finally, we compared the algorithm performance with manual markings done by the six ophthalmologists. The agreement was determined between the ophthalmologists as well as the algorithm. The best agreement was between ophthalmologists one, two and five in 398 of 550 images, while the algorithm agreed with them in 356 images.

Retina Image Vessel Segmentation Using a Hybrid CGLI Level Set Method

PubMed Central

Chen, Meizhu; Li, Jichun; Zhang, Encai

2017-01-01

As a nonintrusive method, the retina imaging provides us with a better way for the diagnosis of ophthalmologic diseases. Extracting the vessel profile automatically from the retina image is an important step in analyzing retina images. A novel hybrid active contour model is proposed to segment the fundus image automatically in this paper. It combines the signed pressure force function introduced by the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model with the local intensity property introduced by the Local Binary fitting (LBF) model to overcome the difficulty of the low contrast in segmentation process. It is more robust to the initial condition than the traditional methods and is easily implemented compared to the supervised vessel extraction methods. Proposed segmentation method was evaluated on two public datasets, DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structured Analysis of the Retina) (the average accuracy of 0.9390 with 0.7358 sensitivity and 0.9680 specificity on DRIVE datasets and average accuracy of 0.9409 with 0.7449 sensitivity and 0.9690 specificity on STARE datasets). The experimental results show that our method is effective and our method is also robust to some kinds of pathology images compared with the traditional level set methods. PMID:28840122

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).

Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier.

PubMed

Memari, Nogol; Ramli, Abd Rahman; Bin Saripan, M Iqbal; Mashohor, Syamsiah; Moghbel, Mehrdad

2017-01-01

The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE) and Child Heart and Health Study in England (CHASE_DB1) datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively.

Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

PubMed

Weijers, Gert; Starke, Alexander; Haudum, Alois; Thijssen, Johan M; Rehage, Jürgen; De Korte, Chris L

2010-07-01

The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty liver disease, to test this hypothesis. Five transcutaneous and five intraoperative US liver images were acquired in each animal and a liverbiopsy was taken. In liver tissue samples, triacylglycerol (TAG) was measured by biochemical analysis and hepatic diseases other than hepatic lipidosis were excluded by histopathologic examination. Ultrasonic tissue characterization (UTC) parameters--Mean echo level, standard deviation (SD) of echo level, signal-to-noise ratio (SNR), residual attenuation coefficient (ResAtt) and axial and lateral speckle size--were derived using a computer-aided US (CAUS) protocol and software package. First, the liver tissue was interactively segmented by two observers. With increasing fat content, fewer hepatic vessels were visible in the ultrasound images and, therefore, a smaller proportion of the liver needed to be excluded from these images. Automatic-segmentation algorithms were implemented and it was investigated whether better results could be achieved than with the subjective and time-consuming interactive-segmentation procedure. The automatic-segmentation algorithms were based on both fixed and adaptive thresholding techniques in combination with a 'speckle'-shaped moving-window exclusion technique. All data were analyzed with and without postprocessing as contained in CAUS and with different automated-segmentation techniques. This enabled us to study the effect of the applied postprocessing steps on single and multiple linear regressions ofthe various UTC parameters with TAG. Improved correlations for all US parameters were found by using automatic-segmentation techniques. Stepwise multiple linear-regression formulas where derived and used

Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction

PubMed Central

Almazroa, Ahmed; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

2017-01-01

We introduce here a new technique for segmenting optic cup using two-dimensional fundus images. Cup segmentation is the most challenging part of image processing of the optic nerve head due to the complexity of its structure. Using the blood vessels to segment the cup is important. Here, we report on blood vessel extraction using first a top-hat transform and Otsu’s segmentation function to detect the curves in the blood vessels (kinks) which indicate the cup boundary. This was followed by an interval type-II fuzzy entropy procedure. Finally, the Hough transform was applied to approximate the cup boundary. The algorithm was evaluated on 550 fundus images from a large dataset, which contained three different sets of images, where the cup was manually marked by six ophthalmologists. On one side, the accuracy of the algorithm was tested on the three image sets independently. The final cup detection accuracy in terms of area and centroid was calculated to be 78.2% of 441 images. Finally, we compared the algorithm performance with manual markings done by the six ophthalmologists. The agreement was determined between the ophthalmologists as well as the algorithm. The best agreement was between ophthalmologists one, two and five in 398 of 550 images, while the algorithm agreed with them in 356 images. PMID:28515636

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.

Pulmonary vessel segmentation utilizing curved planar reformation and optimal path finding (CROP) in computed tomographic pulmonary angiography (CTPA) for CAD applications

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Kuriakose, Jean W.; Chughtai, Aamer; Wei, Jun; Hadjiiski, Lubomir M.; Guo, Yanhui; Patel, Smita; Kazerooni, Ella A.

2012-03-01

Vessel segmentation is a fundamental step in an automated pulmonary embolism (PE) detection system. The purpose of this study is to improve the segmentation scheme for pulmonary vessels affected by PE and other lung diseases. We have developed a multiscale hierarchical vessel enhancement and segmentation (MHES) method for pulmonary vessel tree extraction based on the analysis of eigenvalues of Hessian matrices. However, it is difficult to segment the pulmonary vessels accurately under suboptimal conditions, such as vessels occluded by PEs, surrounded by lymphoid tissues or lung diseases, and crossing with other vessels. In this study, we developed a new vessel refinement method utilizing curved planar reformation (CPR) technique combined with optimal path finding method (MHES-CROP). The MHES segmented vessels straightened in the CPR volume was refined using adaptive gray level thresholding where the local threshold was obtained from least-square estimation of a spline curve fitted to the gray levels of the vessel along the straightened volume. An optimal path finding method based on Dijkstra's algorithm was finally used to trace the correct path for the vessel of interest. Two and eight CTPA scans were randomly selected as training and test data sets, respectively. Forty volumes of interest (VOIs) containing "representative" vessels were manually segmented by a radiologist experienced in CTPA interpretation and used as reference standard. The results show that, for the 32 test VOIs, the average percentage volume error relative to the reference standard was improved from 32.9+/-10.2% using the MHES method to 9.9+/-7.9% using the MHES-CROP method. The accuracy of vessel segmentation was improved significantly (p<0.05). The intraclass correlation coefficient (ICC) of the segmented vessel volume between the automated segmentation and the reference standard was improved from 0.919 to 0.988. Quantitative comparison of the MHES method and the MHES-CROP method with the

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.

Blood vessels segmentation of hatching eggs based on fully convolutional networks

NASA Astrophysics Data System (ADS)

Geng, Lei; Qiu, Ling; Wu, Jun; Xiao, Zhitao

2018-04-01

FCN, trained end-to-end, pixels-to-pixels, predict result of each pixel. It has been widely used for semantic segmentation. In order to realize the blood vessels segmentation of hatching eggs, a method based on FCN is proposed in this paper. The training datasets are composed of patches extracted from very few images to augment data. The network combines with lower layer and deconvolution to enables precise segmentation. The proposed method frees from the problem that training deep networks need large scale samples. Experimental results on hatching eggs demonstrate that this method can yield more accurate segmentation outputs than previous researches. It provides a convenient reference for fertility detection subsequently.

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). Copyright © 2016 Elsevier Ltd. All rights reserved.

Dr. Liver: A preoperative planning system of liver graft volumetry for living donor liver transplantation.

PubMed

Yang, Xiaopeng; Yang, Jae Do; Yu, Hee Chul; Choi, Younggeun; Yang, Kwangho; Lee, Tae Beom; Hwang, Hong Pil; Ahn, Sungwoo; You, Heecheon

2018-05-01

Manual tracing of the right and left liver lobes from computed tomography (CT) images for graft volumetry in preoperative surgery planning of living donor liver transplantation (LDLT) is common at most medical centers. This study aims to develop an automatic system with advanced image processing algorithms and user-friendly interfaces for liver graft volumetry and evaluate its accuracy and efficiency in comparison with a manual tracing method. The proposed system provides a sequential procedure consisting of (1) liver segmentation, (2) blood vessel segmentation, and (3) virtual liver resection for liver graft volumetry. Automatic segmentation algorithms using histogram analysis, hybrid level-set methods, and a customized region growing method were developed. User-friendly interfaces such as sequential and hierarchical user menus, context-sensitive on-screen hotkey menus, and real-time sound and visual feedback were implemented. Blood vessels were excluded from the liver for accurate liver graft volumetry. A large sphere-based interactive method was developed for dividing the liver into left and right lobes with a customized cutting plane. The proposed system was evaluated using 50 CT datasets in terms of graft weight estimation accuracy and task completion time through comparison to the manual tracing method. The accuracy of liver graft weight estimation was assessed by absolute difference (AD) and percentage of AD (%AD) between preoperatively estimated graft weight and intraoperatively measured graft weight. Intra- and inter-observer agreements of liver graft weight estimation were assessed by intraclass correlation coefficients (ICCs) using ten cases randomly selected. The proposed system showed significantly higher accuracy and efficiency in liver graft weight estimation (AD = 21.0 ± 18.4 g; %AD = 3.1% ± 2.8%; percentage of %AD > 10% = none; task completion time = 7.3 ± 1.4 min) than the manual tracing method (AD = 70

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

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

Yuan, Yading, E-mail: yading.yuan@mssm.edu; Chao, Ming; Sheu, Ren-Dih

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.more » 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

Analysis of cerebral vessels dynamics using experimental data with missed segments

NASA Astrophysics Data System (ADS)

Pavlova, O. N.; Abdurashitov, A. S.; Ulanova, M. V.; Shihalov, G. M.; Semyachkina-Glushkovskaya, O. V.; Pavlov, A. N.

2018-04-01

Physiological signals often contain various bad segments that occur due to artifacts, failures of the recording equipment or varying experimental conditions. The related experimental data need to be preprocessed to avoid such parts of recordings. In the case of few bad segments, they can simply be removed from the signal and its analysis is further performed. However, when there are many extracted segments, the internal structure of the analyzed physiological process may be destroyed, and it is unclear whether such signal can be used in diagnostic-related studies. In this paper we address this problem for the case of cerebral vessels dynamics. We perform analysis of simulated data in order to reveal general features of quantifying scaling features of complex signals with distinct correlation properties and show that the effects of data loss are significantly different for experimental data with long-range correlations and anti-correlations. We conclude that the cerebral vessels dynamics is significantly less sensitive to missed data fragments as compared with signals with anti-correlated statistics.

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

Automatic liver segmentation in computed tomography using general-purpose shape modeling methods.

PubMed

Spinczyk, Dominik; Krasoń, Agata

2018-05-29

Liver segmentation in computed tomography is required in many clinical applications. The segmentation methods used can be classified according to a number of criteria. One important criterion for method selection is the shape representation of the segmented organ. The aim of the work is automatic liver segmentation using general purpose shape modeling methods. As part of the research, methods based on shape information at various levels of advancement were used. The single atlas based segmentation method was used as the simplest shape-based method. This method is derived from a single atlas using the deformable free-form deformation of the control point curves. Subsequently, the classic and modified Active Shape Model (ASM) was used, using medium body shape models. As the most advanced and main method generalized statistical shape models, Gaussian Process Morphable Models was used, which are based on multi-dimensional Gaussian distributions of the shape deformation field. Mutual information and sum os square distance were used as similarity measures. The poorest results were obtained for the single atlas method. For the ASM method in 10 analyzed cases for seven test images, the Dice coefficient was above 55[Formula: see text], of which for three of them the coefficient was over 70[Formula: see text], which placed the method in second place. The best results were obtained for the method of generalized statistical distribution of the deformation field. The DICE coefficient for this method was 88.5[Formula: see text] CONCLUSIONS: This value of 88.5 [Formula: see text] Dice coefficient can be explained by the use of general-purpose shape modeling methods with a large variance of the shape of the modeled object-the liver and limitations on the size of our training data set, which was limited to 10 cases. The obtained results in presented fully automatic method are comparable with dedicated methods for liver segmentation. In addition, the deforamtion features of the

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.

Blood vessel segmentation in color fundus images based on regional and Hessian features.

PubMed

Shah, Syed Ayaz Ali; Tang, Tong Boon; Faye, Ibrahima; Laude, Augustinus

2017-08-01

To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis. Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel. The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy. Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.

Venous drainage of the dorsal sector of the liver: differences between segments I and IX. A study on corrosion casts of the human liver.

PubMed

Gadzijev, E M; Ravnik, D; Stanisavljevic, D; Trotovsek, B

1997-01-01

The aim of this study was to determine the venous drainage of the dorsal sector of the liver in order to define the differences between segments I and IX and their implications for sectorially and segmentally oriented hepatic surgery. The study was based on corrosion casts of 61 macroscopically healthy livers. The drainage pathways of veins at least 10 mm long and 1 mm wide were evaluated and statistically analysed. On average, 9 veins drained the two segments and three veins from both segments entered the inferior vena cava. In 95% of cases the veins from segment I drained predominantly into the inferior vena cava, whereas in segment IX this pathway was dominant in only 30% of cases. In 64% of cases a vein originating in segment IX entered the right hepatic v. The difference in the venous drainage of the two segments suggests that segment IX partly belongs to the neighbouring segments and may thus be only a paracaval region of the right liver.

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.

A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images.

PubMed

Gloger, Oliver; Kühn, Jens; Stanski, Adam; Völzke, Henry; Puls, Ralf

2010-07-01

Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties. Copyright 2010 Elsevier Inc. All rights reserved.

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

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

NASA Astrophysics Data System (ADS)

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.

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...

Automatic lumen and outer wall segmentation of the carotid artery using deformable three-dimensional models in MR angiography and vessel wall images.

PubMed

van 't Klooster, Ronald; de Koning, Patrick J H; Dehnavi, Reza Alizadeh; Tamsma, Jouke T; de Roos, Albert; Reiber, Johan H C; van der Geest, Rob J

2012-01-01

To develop and validate an automated segmentation technique for the detection of the lumen and outer wall boundaries in MR vessel wall studies of the common carotid artery. A new segmentation method was developed using a three-dimensional (3D) deformable vessel model requiring only one single user interaction by combining 3D MR angiography (MRA) and 2D vessel wall images. This vessel model is a 3D cylindrical Non-Uniform Rational B-Spline (NURBS) surface which can be deformed to fit the underlying image data. Image data of 45 subjects was used to validate the method by comparing manual and automatic segmentations. Vessel wall thickness and volume measurements obtained by both methods were compared. Substantial agreement was observed between manual and automatic segmentation; over 85% of the vessel wall contours were segmented successfully. The interclass correlation was 0.690 for the vessel wall thickness and 0.793 for the vessel wall volume. Compared with manual image analysis, the automated method demonstrated improved interobserver agreement and inter-scan reproducibility. Additionally, the proposed automated image analysis approach was substantially faster. This new automated method can reduce analysis time and enhance reproducibility of the quantification of vessel wall dimensions in clinical studies. Copyright © 2011 Wiley Periodicals, Inc.

Segmental Bile Duct-Targeted Liver Resection for Right-Sided Intrahepatic Stones.

PubMed

Li, Shao-Qiang; Hua, Yun-Peng; Shen, Shun-Li; Hu, Wen-Jie; Peng, Bao-Gang; Liang, Li-Jian

2015-07-01

Hepatectomy is a safe and effective treatment for intrahepatic stones (IHSs). However, the resection plane for right-sided stones distributed within 2 segments is obstacle because of atrophy-hypertrophy complex formation of the liver and difficult dissection of segmental pedicle within the Glissonean plate by conventional approach. Thus, we devised segmental bile duct-targeted liver resection (SBDLR) for IHS, which aimed at completely resection of diseased bile ducts. This study aimed to evaluate the outcomes of SBDLR for right-sided IHSs. From January 2009 to December 2013, 107 patients with IHS treated by SBDLR in our center were reviewed in a prospective database. Patients' intermediate and long-term outcomes after SBDLR were analyzed. A total of 40 (37.4%) patients with localized right-sided stone and 67 (62.7%) patients with bilateral stones underwent SBDLR alone and SBDLR combined with left-sided hepatectomy, respectively. There was no hospital mortality of this cohort of patients. The postoperative morbidity was 35.5%. The mean intraoperative blood loss was 414  mL (range: 100-2500). Twenty-one (19.6%) patients needed red blood cells transfusion. The intermediate stone clearance rate was 94.4%; the final clearance rate reached 100% after subsequent postoperative cholangioscopic lithotomy. Only 2.8% patients developed stone recurrence in a median follow-up period of 38.3 months. SBDLR is a safe and effective treatment for right-sided IHS distributed within 2 segments. It is especially suitable for a subgroup of patients with bilateral stones whose right-sided stones are within 2 segments and bilateral liver resection is needed.

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.

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

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

Xu, Yan, E-mail: xuyan04@gmail.com; Xu, Chenchao, E-mail: chenchaoxu33@gmail.com; Kuang, Xiao, E-mail: kuangxiao.ace@gmail.com

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.more » 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.« less

Graph representation of hepatic vessel based on centerline extraction and junction detection

NASA Astrophysics Data System (ADS)

Zhang, Xing; Tian, Jie; Deng, Kexin; Li, Xiuli; Yang, Fei

2012-02-01

In the area of computer-aided diagnosis (CAD), segmentation and analysis of hepatic vessel is a prerequisite for hepatic diseases diagnosis and surgery planning. For liver surgery planning, it is crucial to provide the surgeon with a patient-individual three-dimensional representation of the liver along with its vasculature and lesions. The representation allows an exploration of the vascular anatomy and the measurement of vessel diameters, following by intra-patient registration, as well as the analysis of the shape and volume of vascular territories. In this paper, we present an approach for generation of hepatic vessel graph based on centerline extraction and junction detection. The proposed approach involves the following concepts and methods: 1) Flux driven automatic centerline extraction; 2) Junction detection on the centerline using hollow sphere filtering; 3) Graph representation of hepatic vessel based on the centerline and junction. The approach is evaluated on contrast-enhanced liver CT datasets to demonstrate its availability and effectiveness.

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.

Liver segment IV hypoplasia as a risk factor for bile duct injury.

PubMed

Mercado, Miguel Angel; Franssen, Bernardo; Arriola, Juan Carlos; Garcia-Badiola, Artemio; Arámburo, Rigoberto; Elnecavé, Alejandro; Cortés-González, Rubén

2011-09-01

Bile duct injury remains constant in the era of laparoscopic cholecystectomy and misidentification of structures remains one of the most common causes of such injuries. Abnormalities in liver segment IV, which is fully visible during laparoscopic cholecystectomy, may contribute to misidentification as proposed herein. We describe the case of a 36-year-old female who had a bile duct injury during a laparoscopic cholecystectomy where the surgeon noticed an unusually small distance between the gallbladder and the round ligament. We define hypoplasia of liver segment IV as well as describe the variation of the biliary anatomy in the case. We also intend to fit it in a broader spectrum of developmental anomalies that have both hyopoplasia of some portion of the liver and variations in gallbladder and bile duct anatomy that may contribute to bile duct injury. To our knowledge, hypoplasia of liver segment IV has not been suggested in the literature as a risk factor for bile duct injury except in the extreme case of a left-sided gallbladder. Surgeons should be vigilant during laparoscopic cholecystectomy when they become aware of an unusually small distance between the gallbladder bed and the round ligament prior to beginning their dissection, variations in the common bile duct and cystic duct should be expected.

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.

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.

A vessel segmentation method for multi-modality angiographic images based on multi-scale filtering and statistical models.

PubMed

Lu, Pei; Xia, Jun; Li, Zhicheng; Xiong, Jing; Yang, Jian; Zhou, Shoujun; Wang, Lei; Chen, Mingyang; Wang, Cheng

2016-11-08

Accurate segmentation of blood vessels plays an important role in the computer-aided diagnosis and interventional treatment of vascular diseases. The statistical method is an important component of effective vessel segmentation; however, several limitations discourage the segmentation effect, i.e., dependence of the image modality, uneven contrast media, bias field, and overlapping intensity distribution of the object and background. In addition, the mixture models of the statistical methods are constructed relaying on the characteristics of the image histograms. Thus, it is a challenging issue for the traditional methods to be available in vessel segmentation from multi-modality angiographic images. To overcome these limitations, a flexible segmentation method with a fixed mixture model has been proposed for various angiography modalities. Our method mainly consists of three parts. Firstly, multi-scale filtering algorithm was used on the original images to enhance vessels and suppress noises. As a result, the filtered data achieved a new statistical characteristic. Secondly, a mixture model formed by three probabilistic distributions (two Exponential distributions and one Gaussian distribution) was built to fit the histogram curve of the filtered data, where the expectation maximization (EM) algorithm was used for parameters estimation. Finally, three-dimensional (3D) Markov random field (MRF) were employed to improve the accuracy of pixel-wise classification and posterior probability estimation. To quantitatively evaluate the performance of the proposed method, two phantoms simulating blood vessels with different tubular structures and noises have been devised. Meanwhile, four clinical angiographic data sets from different human organs have been used to qualitatively validate the method. To further test the performance, comparison tests between the proposed method and the traditional ones have been conducted on two different brain magnetic resonance angiography

Segmental liver ischemia/infarction after elective transjugular intrahepatic portosystemic shunt creation: clinical outcomes in 10 patients.

PubMed

Lopera, Jorge E; Katabathina, Venkata; Bosworth, Brian; Garg, Deepak; Kroma, Ghazwan; Garza-Berlanga, Andres; Suri, Rajeev; Wholey, Michael

2015-06-01

To determine the clinical significance and potential mechanisms of segmental liver ischemia and infarction following elective creation of a transjugular intrahepatic portosystemic shunt (TIPS). A retrospective review of 374 elective TIPS creations between March 2006 and September 2014 was performed, yielding 77 contrast-enhanced scans for review. Patients with imaging evidence of segmental perfusion defects were identified. Model for End-stage Liver Disease scores, liver volume, and percentage of liver ischemia/infarct were calculated. Clinical outcomes after TIPS creation were reviewed. Ten patients showed segmental liver ischemia/infarction on contrast-enhanced imaging after elective TIPS creation. Associated imaging findings included thrombosis of the posterior division (n = 7) and anterior division (n = 3) of the right portal vein (PV). The right hepatic vein was thrombosed in 5 patients, as was the middle hepatic vein in 3 and the left hepatic vein in 1. One patient had acute thrombosis of the shunt and main PV. Three patients developed acute liver failure: 2 died within 30 days and 1 required emergent liver transplantation. One patient died of acute renal failure 20 days after TIPS creation. A large infarct in a transplant recipient resulted in biloma formation. Five patients survived without additional interventions with follow-up times ranging from 3 months to 5 years. Segmental perfusion defects are not an uncommon imaging finding after elective TIPS creation. Segmental ischemia was associated with thrombosis of major branches of the PVs and often of the hepatic veins. Clinical outcomes varied significantly, from transient problems to acute liver failure with high mortality rates. Copyright © 2015 SIR. Published by Elsevier Inc. All rights reserved.

Surgical Anatomy of the Left Lateral Segment as Applied to Living-Donor and Split-Liver Transplantation

PubMed Central

Reichert, Paulo R.; Renz, John F.; D’Albuquerque, Luiz A. C.; Rosenthal, Philip; Lim, Robert C.; Roberts, John P.; Ascher, Nancy L.; Emond, Jean C.

2000-01-01

Objective To evaluate intrahepatic vascular and biliary anatomy of the left lateral segment (LLS) as applied to living-donor and split-liver transplantation. Summary Background Data Living-donor and split-liver transplantation are innovative surgical techniques that have expanded the donor pool. Fundamental to the application of these techniques is an understanding of intrahepatic vascular and biliary anatomy. Methods Pathologic data obtained from cadaveric liver corrosion casts and liver dissections were clinically correlated with the anatomical findings obtained during split-liver, living-donor, and reduced-liver transplants. Results The anatomical relation of the left bile duct system with respect to the left portal venous system was constant, with the left bile duct superior to the extrahepatic transverse portion of the left portal vein. Four specific patterns of left biliary anatomy and three patterns of left hepatic venous drainage were identified and described. Conclusions Although highly variable, the biliary and hepatic venous anatomy of the LLS can be broadly categorized into distinct patterns. The identification of the LLS duct origin lateral to the umbilical fissure in segment 4 in 50% of cast specimens is significant in the performance of split-liver and living-donor transplantation, because dissection of the graft pedicle at the level of the round ligament will result in separate ducts from segments 2 and 3 in most patients, with the further possibility of an anterior segment 4 duct. A connective tissue bile duct plate, which can be clinically identified, is described to guide dissection of the segment 2 and 3 biliary radicles. PMID:11066137

Segmental analysis of respiratory liver motion in patients with and without a history of abdominal surgery.

PubMed

Shimizu, Yasuhiro; Takamatsu, Shigeyuki; Yamamoto, Kazutaka; Maeda, Yoshikazu; Sasaki, Makoto; Tamamura, Hiroyasu; Bou, Sayuri; Kumano, Tomoyasu; Gabata, Toshifumi

2018-06-20

The purpose of this study was to analyze the respiratory motion of each segment of the liver in patients with or without a history of abdominal surgery using four-dimensional computed tomography. In total, 57 patients treated for abdominal tumors using proton beam therapy were enrolled. Eighteen patients had a history of abdominal surgery and 39 did not. The positions of clearly demarcated, high-density regions in the liver were measured as evaluation points with which to quantify the motion of each liver segment according to the Couinaud classification. In total, 218 evaluation points were analyzed. Comparison of differences in the motion of individual liver segments showed that among patients without a history of surgery, the maximum was 29.0 (7.2-42.1) mm in S6 and the minimum was 15.1 (10.6-19.3) mm in S4. Among patients with a history of surgery, the maximum was 28.0 (9.0-37.4) mm in S7 and the minimum was 6.3 (4.1-9.3) mm in S3. The distances and directions of respiratory motion differed for each liver segment, and a history of abdominal surgery reduced the respiratory motion of the liver. It is necessary to selectively use the internal margin setting.

Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation.

PubMed

Zana, F; Klein, J C

2001-01-01

This paper presents an algorithm based on mathematical morphology and curvature evaluation for the detection of vessel-like patterns in a noisy environment. Such patterns are very common in medical images. Vessel detection is interesting for the computation of parameters related to blood flow. Its tree-like geometry makes it a usable feature for registration between images that can be of a different nature. In order to define vessel-like patterns, segmentation is performed with respect to a precise model. We define a vessel as a bright pattern, piece-wise connected, and locally linear, mathematical morphology is very well adapted to this description, however other patterns fit such a morphological description. In order to differentiate vessels from analogous background patterns, a cross-curvature evaluation is performed. They are separated out as they have a specific Gaussian-like profile whose curvature varies smoothly along the vessel. The detection algorithm that derives directly from this modeling is based on four steps: (1) noise reduction; (2) linear pattern with Gaussian-like profile improvement; (3) cross-curvature evaluation; (4) linear filtering. We present its theoretical background and illustrate it on real images of various natures, then evaluate its robustness and its accuracy with respect to noise.

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.

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.

Dynamic gadoxetate-enhanced MRI for the assessment of total and segmental liver function and volume in primary sclerosing cholangitis.

PubMed

Nilsson, Henrik; Blomqvist, Lennart; Douglas, Lena; Nordell, Anders; Jacobsson, Hans; Hagen, Karin; Bergquist, Annika; Jonas, Eduard

2014-04-01

To evaluate dynamic hepatocyte-specific contrast-enhanced MRI (DHCE-MRI) for the assessment of global and segmental liver volume and function in patients with primary sclerosing cholangitis (PSC), and to explore the heterogeneous distribution of liver function in this patient group. Twelve patients with primary sclerosing cholangitis (PSC) and 20 healthy volunteers were examined using DHCE-MRI with Gd-EOB-DTPA. Segmental and total liver volume were calculated, and functional parameters (hepatic extraction fraction [HEF], input relative blood-flow [irBF], and mean transit time [MTT]) were calculated in each liver voxel using deconvolutional analysis. In each study subject, and incongruence score (IS) was constructed to describe the mismatch between segmental function and volume. Among patients, the liver function parameters were correlated to bile duct obstruction and to established scoring models for liver disease. Liver function was significantly more heterogeneously distributed in the patient group (IS 1.0 versus 0.4). There were significant correlations between biliary obstruction and segmental functional parameters (HEF rho -0.24; irBF rho -0.45), and the Mayo risk score correlated significantly with the total liver extraction capacity of Gd-EOB-DTPA (rho -0.85). The study demonstrates a new method to quantify total and segmental liver function using DHCE-MRI in patients with PSC. Copyright © 2013 Wiley Periodicals, Inc.

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

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.

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.

Hepatic parenchymal atrophy induction for intractable segmental bile duct injury after liver resection.

PubMed

Hwang, Shin; Park, Gil-Chun; Ha, Tae-Yong; Ko, Gi-Young; Gwon, Dong-Il; Choi, Young-Il; Song, Gi-Won; Lee, Sung-Gyu

2012-05-01

Liver resection can result in various types of bile duct injuries but their treatment is usually difficult and often leads to intractable clinical course. We present an unusual case of hepatic segment III duct (B3) injury, which occurred after left medial sectionectomy for large hepatocellular carcinoma and was incidentally detected 1 week later due to bile leak. Since the pattern of this B3 injury was not adequate for operative biliary reconstruction, atrophy induction of the involved hepatic parenchyma was attempted. This treatment consisted of embolization of the segment III portal branch to inhibit bile production, induction of heavy adhesion at the bile leak site and clamping of the percutaneous transhepatic biliary drainage (PTBD) tube to accelerate segment III atrophy. This entire procedure, from liver resection to PTBD tube removal took 4 months. This patient has shown no other complication or tumor recurrence for 4 years to date. These findings suggest that percutaneous segmental portal vein embolization, followed by intentional clamping of external biliary drainage, can effectively control intractable bile leak from segmental bile duct injury.

Automatic 3D liver location and segmentation via convolutional neural network and graph cut.

PubMed

Lu, Fang; Wu, Fa; Hu, Peijun; Peng, Zhiyi; Kong, Dexing

2017-02-01

Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.

Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters.

PubMed

Schneider, Matthias; Hirsch, Sven; Weber, Bruno; Székely, Gábor; Menze, Bjoern H

2015-01-01

We propose a novel framework for joint 3-D vessel segmentation and centerline extraction. The approach is based on multivariate Hough voting and oblique random forests (RFs) that we learn from noisy annotations. It relies on steerable filters for the efficient computation of local image features at different scales and orientations. We validate both the segmentation performance and the centerline accuracy of our approach both on synthetic vascular data and four 3-D imaging datasets of the rat visual cortex at 700 nm resolution. First, we evaluate the most important structural components of our approach: (1) Orthogonal subspace filtering in comparison to steerable filters that show, qualitatively, similarities to the eigenspace filters learned from local image patches. (2) Standard RF against oblique RF. Second, we compare the overall approach to different state-of-the-art methods for (1) vessel segmentation based on optimally oriented flux (OOF) and the eigenstructure of the Hessian, and (2) centerline extraction based on homotopic skeletonization and geodesic path tracing. Our experiments reveal the benefit of steerable over eigenspace filters as well as the advantage of oblique split directions over univariate orthogonal splits. We further show that the learning-based approach outperforms different state-of-the-art methods and proves highly accurate and robust with regard to both vessel segmentation and centerline extraction in spite of the high level of label noise in the training data. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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

Computerized liver volumetry on MRI by using 3D geodesic active contour segmentation.

PubMed

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

2014-01-01

Our purpose was to develop an accurate automated 3D liver segmentation scheme for measuring liver volumes on MRI. 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. 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). The MRI liver volumetry based on our automated scheme agreed excellently with reference-standard volumetry, and it required substantially less completion time.

Co-option of Liver Vessels and Not Sprouting Angiogenesis Drives Acquired Sorafenib Resistance in Hepatocellular Carcinoma.

PubMed

Kuczynski, Elizabeth A; Yin, Melissa; Bar-Zion, Avinoam; Lee, Christina R; Butz, Henriett; Man, Shan; Daley, Frances; Vermeulen, Peter B; Yousef, George M; Foster, F Stuart; Reynolds, Andrew R; Kerbel, Robert S

2016-08-01

The anti-angiogenic Sorafenib is the only approved systemic therapy for advanced hepatocellular carcinoma (HCC). However, acquired resistance limits its efficacy. An emerging theory to explain intrinsic resistance to other anti-angiogenic drugs is 'vessel co-option,' ie, the ability of tumors to hijack the existing vasculature in organs such as the lungs or liver, thus limiting the need for sprouting angiogenesis. Vessel co-option has not been evaluated as a potential mechanism for acquired resistance to anti-angiogenic agents. To study sorafenib resistance mechanisms, we used an orthotopic human HCC model (n = 4-11 per group), where tumor cells are tagged with a secreted protein biomarker to monitor disease burden and response to therapy. Histopathology, vessel perfusion assessed by contrast-enhanced ultrasound, and miRNA sequencing and quantitative real-time polymerase chain reaction were used to monitor changes in tumor biology. While sorafenib initially inhibited angiogenesis and stabilized tumor growth, no angiogenic 'rebound' effect was observed during development of resistance unless therapy was stopped. Instead, resistant tumors became more locally infiltrative, which facilitated extensive incorporation of liver parenchyma and the co-option of liver-associated vessels. Up to 75% (±10.9%) of total vessels were provided by vessel co-option in resistant tumors relative to 23.3% (±10.3%) in untreated controls. miRNA sequencing implicated pro-invasive signaling and epithelial-to-mesenchymal-like transition during resistance development while functional imaging further supported a shift from angiogenesis to vessel co-option. This is the first documentation of vessel co-option as a mechanism of acquired resistance to anti-angiogenic therapy and could have important implications including the potential therapeutic benefits of targeting vessel co-option in conjunction with vascular endothelial growth factor receptor signaling. © The Author 2016. Published by

Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.

PubMed

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%.

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

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

He, Baochun; Huang, Cheng; Zhou, Shoujun

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-levelmore » 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

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

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.

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.

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.

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.

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.

Retinal blood vessel segmentation in high resolution fundus photographs using automated feature parameter estimation

NASA Astrophysics Data System (ADS)

Orlando, José Ignacio; Fracchia, Marcos; del Río, Valeria; del Fresno, Mariana

2017-11-01

Several ophthalmological and systemic diseases are manifested through pathological changes in the properties and the distribution of the retinal blood vessels. The characterization of such alterations requires the segmentation of the vasculature, which is a tedious and time-consuming task that is infeasible to be performed manually. Numerous attempts have been made to propose automated methods for segmenting the retinal vasculature from fundus photographs, although their application in real clinical scenarios is usually limited by their ability to deal with images taken at different resolutions. This is likely due to the large number of parameters that have to be properly calibrated according to each image scale. In this paper we propose to apply a novel strategy for automated feature parameter estimation, combined with a vessel segmentation method based on fully connected conditional random fields. The estimation model is learned by linear regression from structural properties of the images and known optimal configurations, that were previously obtained for low resolution data sets. Our experiments in high resolution images show that this approach is able to estimate appropriate configurations that are suitable for performing the segmentation task without requiring to re-engineer parameters. Furthermore, our combined approach reported state of the art performance on the benchmark data set HRF, as measured in terms of the F1-score and the Matthews correlation coefficient.

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

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

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

Straub, Adam C.; Stolz, Donna B.; Vin, Harina

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,more » 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.« less

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.

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.

Comparison of MRI segmentation techniques for measuring liver cyst volumes in autosomal dominant polycystic kidney disease.

PubMed

Farooq, Zerwa; Behzadi, Ashkan Heshmatzadeh; Blumenfeld, Jon D; Zhao, Yize; Prince, Martin R

To compare MRI segmentation methods for measuring liver cyst volumes in autosomal dominant polycystic kidney disease (ADPKD). Liver cyst volumes in 42 ADPKD patients were measured using region growing, thresholding and cyst diameter techniques. Manual segmentation was the reference standard. Root mean square deviation was 113, 155, and 500 for cyst diameter, thresholding and region growing respectively. Thresholding error for cyst volumes below 500ml was 550% vs 17% for cyst volumes above 500ml (p<0.001). For measuring volume of a small number of cysts, cyst diameter and manual segmentation methods are recommended. For severe disease with numerous, large hepatic cysts, thresholding is an acceptable alternative. Copyright © 2017 Elsevier Inc. All rights reserved.

Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting.

PubMed

Gao, Shan; van 't Klooster, Ronald; Kitslaar, Pieter H; Coolen, Bram F; van den Berg, Alexandra M; Smits, Loek P; Shahzad, Rahil; Shamonin, Denis P; de Koning, Patrick J H; Nederveen, Aart J; van der Geest, Rob J

2017-10-01

The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI. The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data. In particular, a hybrid segmentation which combines deformable model fitting with boundary classification was applied to extract the lumen surface. The 3D model ensures the correct shape and topology of the carotid artery, while the boundary classification uses combined image information of 3D TOF-MRA and 3D BB-MRI to promote accurate delineation of the lumen boundaries. The proposed algorithm was validated on 25 subjects (48 arteries) including both healthy volunteers and atherosclerotic patients with 30% to 70% carotid stenosis. For both lumen and outer wall border detection, our result shows good agreement between manually and automatically determined contours, with contour-to-contour distance less than 1 pixel as well as Dice overlap greater than 0.87 at all different carotid artery sections. The presented 3D segmentation technique has demonstrated the capability of providing vessel wall delineation for 3D carotid MRI data with high accuracy and limited user interaction. This brings benefits to large-scale patient studies for assessing the effect of pharmacological treatment of atherosclerosis by reducing image analysis time and bias between human observers. © 2017 American Association of Physicists in Medicine.

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

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

Fully automated MR liver volumetry using watershed segmentation coupled with active contouring.

PubMed

Huynh, Hieu Trung; Le-Trong, Ngoc; Bao, Pham The; Oto, Aytek; Suzuki, Kenji

2017-02-01

Our purpose is to develop a fully automated scheme for liver volume measurement in abdominal MR images, without requiring any user input or interaction. The proposed scheme is fully automatic for liver volumetry from 3D abdominal MR images, and it consists of three main stages: preprocessing, rough liver shape generation, and liver extraction. The preprocessing stage reduced noise and enhanced the liver boundaries in 3D abdominal MR images. The rough liver shape was revealed fully automatically by using the watershed segmentation, thresholding transform, morphological operations, and statistical properties of the liver. An active contour model was applied to refine the rough liver shape to precisely obtain the liver boundaries. The liver volumes calculated by the proposed scheme were compared to the "gold standard" references which were estimated by an expert abdominal radiologist. The liver volumes computed by using our developed scheme excellently agreed (Intra-class correlation coefficient was 0.94) with the "gold standard" manual volumes by the radiologist in the evaluation with 27 cases from multiple medical centers. The running time was 8.4 min per case on average. We developed a fully automated liver volumetry scheme in MR, which does not require any interaction by users. It was evaluated with cases from multiple medical centers. The liver volumetry performance of our developed system was comparable to that of the gold standard manual volumetry, and it saved radiologists' time for manual liver volumetry of 24.7 min per case.

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.

Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning

NASA Astrophysics Data System (ADS)

Ibragimov, Bulat; Toesca, Diego; Chang, Daniel; Koong, Albert; Xing, Lei

2017-12-01

Automated segmentation of the portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated segmentation of the PV from computed tomography (CT) images. We apply convolutional neural networks (CNNs) to learn the consistent appearance patterns of the PV using a training set of CT images with reference annotations and then enhance the PV in previously unseen CT images. Markov random fields (MRFs) were further used to smooth the results of the enhancement of the CNN enhancement and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled for liver stereotactic body radiation therapy. The obtained accuracy of the segmentation was DSC= 0.83 and \

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.

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.

WE-AB-303-05: Breathing Motion of Liver Segments From Fiducial Tracking During Robotic Radiosurgery and Comparison with 4D-CT-Derived Fiducial Motion

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

Sutherland, J; Pantarotto, J; Nair, V

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 standardmore » 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

Anatomical variations in the pattern of the right hepatic veins draining the posterior segment of the right lobe of the liver.

PubMed

Shilal, Poonam; Tuli, Anita

2015-03-01

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. 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. Sixty formalin fixed adult human liver specimens were dissected manually. 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. 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.

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.

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

Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI.

PubMed

Gatos, Ilias; Tsantis, Stavros; Karamesini, Maria; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Hazle, John D; Kagadis, George C

2017-07-01

To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis. The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%. Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures. © 2017 American

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

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

Experimental comparison of landmark-based methods for 3D elastic registration of pre- and postoperative liver CT data

NASA Astrophysics Data System (ADS)

Lange, Thomas; Wörz, Stefan; Rohr, Karl; Schlag, Peter M.

2009-02-01

The qualitative and quantitative comparison of pre- and postoperative image data is an important possibility to validate surgical procedures, in particular, if computer assisted planning and/or navigation is performed. Due to deformations after surgery, partially caused by the removal of tissue, a non-rigid registration scheme is a prerequisite for a precise comparison. Interactive landmark-based schemes are a suitable approach, if high accuracy and reliability is difficult to achieve by automatic registration approaches. Incorporation of a priori knowledge about the anatomical structures to be registered may help to reduce interaction time and improve accuracy. Concerning pre- and postoperative CT data of oncological liver resections the intrahepatic vessels are suitable anatomical structures. In addition to using branching landmarks for registration, we here introduce quasi landmarks at vessel segments with high localization precision perpendicular to the vessels and low precision along the vessels. A comparison of interpolating thin-plate splines (TPS), interpolating Gaussian elastic body splines (GEBS) and approximating GEBS on landmarks at vessel branchings as well as approximating GEBS on the introduced vessel segment landmarks is performed. It turns out that the segment landmarks provide registration accuracies as good as branching landmarks and can improve accuracy if combined with branching landmarks. For a low number of landmarks segment landmarks are even superior.

Diffusion MRI with Semi-Automated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis

PubMed Central

Stephen, Renu M.; Jha, Abhinav K.; Roe, Denise J.; Trouard, Theodore P.; Galons, Jean-Philippe; Kupinski, Matthew A.; Frey, Georgette; Cui, Haiyan; Squire, Scott; Pagel, Mark D.; Rodriguez, Jeffrey J.; Gillies, Robert J.; Stopeck, Alison T.

2015-01-01

Purpose To assess the value of semi-automated segmentation applied to diffusion MRI for predicting the therapeutic response of liver metastasis. Methods Conventional diffusion weighted magnetic resonance imaging (MRI) was performed using b-values of 0, 150, 300 and 450 s/mm2 at baseline and days 4, 11 and 39 following initiation of a new chemotherapy regimen in a pilot study with 18 women with 37 liver metastases from primary breast cancer. A semi-automated segmentation approach was used to identify liver metastases. Linear regression analysis was used to assess the relationship between baseline values of the apparent diffusion coefficient (ADC) and change in tumor size by day 39. Results A semi-automated segmentation scheme was critical for obtaining the most reliable ADC measurements. A statistically significant relationship between baseline ADC values and change in tumor size at day 39 was observed for minimally treated patients with metastatic liver lesions measuring 2–5 cm in size (p = 0.002), but not for heavily treated patients with the same tumor size range (p = 0.29), or for tumors of smaller or larger sizes. ROC analysis identified a baseline threshold ADC value of 1.33 μm2/ms as 75% sensitive and 83% specific for identifying non-responding metastases in minimally treated patients with 2–5 cm liver lesions. Conclusion Quantitative imaging can substantially benefit from a semi-automated segmentation scheme. Quantitative diffusion MRI results can be predictive of therapeutic outcome in selected patients with liver metastases, but not for all liver metastases, and therefore should be considered to be a restricted biomarker. PMID:26284600

Diffusion MRI with Semi-Automated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis.

PubMed

Stephen, Renu M; Jha, Abhinav K; Roe, Denise J; Trouard, Theodore P; Galons, Jean-Philippe; Kupinski, Matthew A; Frey, Georgette; Cui, Haiyan; Squire, Scott; Pagel, Mark D; Rodriguez, Jeffrey J; Gillies, Robert J; Stopeck, Alison T

2015-12-01

To assess the value of semi-automated segmentation applied to diffusion MRI for predicting the therapeutic response of liver metastasis. Conventional diffusion weighted magnetic resonance imaging (MRI) was performed using b-values of 0, 150, 300 and 450s/mm(2) at baseline and days 4, 11 and 39 following initiation of a new chemotherapy regimen in a pilot study with 18 women with 37 liver metastases from primary breast cancer. A semi-automated segmentation approach was used to identify liver metastases. Linear regression analysis was used to assess the relationship between baseline values of the apparent diffusion coefficient (ADC) and change in tumor size by day 39. A semi-automated segmentation scheme was critical for obtaining the most reliable ADC measurements. A statistically significant relationship between baseline ADC values and change in tumor size at day 39 was observed for minimally treated patients with metastatic liver lesions measuring 2-5cm in size (p=0.002), but not for heavily treated patients with the same tumor size range (p=0.29), or for tumors of smaller or larger sizes. ROC analysis identified a baseline threshold ADC value of 1.33μm(2)/ms as 75% sensitive and 83% specific for identifying non-responding metastases in minimally treated patients with 2-5cm liver lesions. Quantitative imaging can substantially benefit from a semi-automated segmentation scheme. Quantitative diffusion MRI results can be predictive of therapeutic outcome in selected patients with liver metastases, but not for all liver metastases, and therefore should be considered to be a restricted biomarker. Copyright © 2015 Elsevier Inc. All rights reserved.

The error analysis of Lobular and segmental division of right liver by volume measurement.

PubMed

Zhang, Jianfei; Lin, Weigang; Chi, Yanyan; Zheng, Nan; Xu, Qiang; Zhang, Guowei; Yu, Shengbo; Li, Chan; Wang, Bin; Sui, Hongjin

2017-07-01

The aim of this study is to explore the inconsistencies between right liver volume as measured by imaging and the actual anatomical appearance of the right lobe. Five healthy donated livers were studied. The liver slices were obtained with hepatic segments multicolor-infused through the portal vein. In the slices, the lobes were divided by two methods: radiological landmarks and real anatomical boundaries. The areas of the right anterior lobe (RAL) and right posterior lobe (RPL) on each slice were measured using Photoshop CS5 and AutoCAD, and the volumes of the two lobes were calculated. There was no statistically significant difference between the volumes of the RAL or RPL as measured by the radiological landmarks (RL) and anatomical boundaries (AB) methods. However, the curves of the square error value of the RAL and RPL measured using CT showed that the three lowest points were at the cranial, intermediate, and caudal levels. The U- or V-shaped curves of the square error rate of the RAL and RPL revealed that the lowest value is at the intermediate level and the highest at the cranial and caudal levels. On CT images, less accurate landmarks were used to divide the RAL and RPL at the cranial and caudal layers. The measured volumes of hepatic segments VIII and VI would be less than their true values, and the measured volumes of hepatic segments VII and V would be greater than their true values, according to radiological landmarks. Clin. Anat. 30:585-590, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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.

Efficacy of hydrodynamic interleukin 10 gene transfer in human liver segments with interest in transplantation.

PubMed

Sendra Gisbert, Luis; Miguel Matas, Antonio; Sabater Ortí, Luis; Herrero, María José; Sabater Olivas, Laura; Montalvá Orón, Eva María; Frasson, Matteo; Abargues López, Rafael; López-Andújar, Rafael; García-Granero Ximénez, Eduardo; Aliño Pellicer, Salvador Francisco

2017-01-01

Different diseases lead, during their advanced stages, to chronic or acute liver failure, whose unique treatment consists in organ transplantation. The success of intervention is limited by host immune response and graft rejection. The use of immunosuppressant drugs generally improve organ transplantation, but they cannot completely solve the problem. Also, their management is delicate, especially during the early stages of treatment. Thus, new tools to set an efficient modulation of immune response are required. The local expression of interleukin (IL) 10 protein in transplanted livers mediated by hydrodynamic gene transfer could improve the organ acceptance by the host because it presents the natural ability to modulate the immune response at different levels. In the organ transplantation scenario, IL10 has already demonstrated positive effects on graft tolerance. Hydrodynamic gene transfer has been proven to be safe and therapeutically efficient in animal models and could be easily moved to the clinic. In the present work, we evaluated efficacy of human IL10 gene transfer in human liver segments and the tissue natural barriers for gene entry into the cell, employing gold nanoparticles. In conclusion, the present work shows for the first time that hydrodynamic IL10 gene transfer to human liver segments ex vivo efficiently delivers a human gene into the cells. Indexes of tissue protein expression achieved could mediate local pharmacological effects with interest in controlling the immune response triggered after liver transplantation. On the other hand, the ultrastructural study suggests that the solubilized plasmid could access the hepatocyte in a passive manner mediated by the hydric flow and that an active mechanism of transportation could facilitate its entry into the nucleus. Liver Transplantation 23:50-62 2017 AASLD. © 2016 by the American Association for the Study of Liver Diseases.

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.

Automatic plaque characterization and vessel wall segmentation in magnetic resonance images of atherosclerotic carotid arteries

NASA Astrophysics Data System (ADS)

Adame, Isabel M.; van der Geest, Rob J.; Wasserman, Bruce A.; Mohamed, Mona; Reiber, Johan H. C.; Lelieveldt, Boudewijn P. F.

2004-05-01

Composition and structure of atherosclerotic plaque is a primary focus of cardiovascular research. In vivo MRI provides a meanse to non-invasively image and assess the morphological features of athersclerotic and normal human carotid arteries. To quantitatively assess the vulnerability and the type of plaque, the contours of the lumen, outer boundary of the vessel wall and plaque components, need to be traced. To achieve this goal, we have developed an automated contou detection technique, which consists of three consecutive steps: firstly, the outer boundary of the vessel wall is detected by means of an ellipse-fitting procedure in order to obtain smoothed shapes; secondly, the lumen is segnented using fuzzy clustering. Thre region to be classified is that within the outer vessel wall boundary obtained from the previous step; finally, for plaque detection we follow the same approach as for lumen segmentation: fuzzy clustering. However, plaque is more difficult to segment, as the pixel gray value can differ considerably from one region to another, even when it corresponds to the same type of tissue. That makes further processing necessary. All these three steps might be carried out combining information from different sequences (PD-, T2-, T1-weighted images, pre- and post-contrast), to improve the contour detection. The algorithm has been validated in vivo on 58 high-resolution PD and T1 weighted MR images (19 patients). The results demonstrate excellent correspondence between automatic and manual area measurements: lumen (r=0.94), outer (r=0.92), and acceptable for fibrous cap thickness (r=0.76).

Does acid-base equilibrium correlate with remnant liver volume during stepwise liver resection?

PubMed

Golriz, Mohammad; Abbasi, Sepehr; Fathi, Parham; Majlesara, Ali; Brenner, Thorsten; Mehrabi, Arianeb

2017-10-01

Small for size and flow syndrome (SFSF) is one of the most challenging complications following extended hepatectomy (EH). After EH, hepatic artery flow decreases and portal vein flow increases per 100 g of remnant liver volume (RLV). This causes hypoxia followed by metabolic acidosis. A correlation between acidosis and posthepatectomy liver failure has been postulated but not studied systematically in a large animal model or clinical setting. In our study, we performed stepwise liver resections on nine pigs to defined SFSF limits as follows: step 1: segment II/III resection, step 2: segment IV resection, step 3: segment V/VIII resection (RLV: 75, 50, and 25%, respectively). Blood gas values were measured before and after each step using four catheters inserted into the carotid artery, internal jugular vein, hepatic artery, and portal vein. The pH, [Formula: see text], and base excess (BE) decreased, but [Formula: see text] values increased after 75% resection in the portal and jugular veins. EH correlated with reduced BE in the hepatic artery. Pco 2 values increased after 75% resection in the jugular vein. In contrast, arterial Po 2 increased after every resection, whereas the venous Po 2 decreased slightly. There were differences in venous [Formula: see text], BE in the hepatic artery, and Pco 2 in the jugular vein after 75% liver resection. Because 75% resection is the limit for SFSF, these noninvasive blood evaluations may be used to predict SFSF. Further studies with long-term follow-up are required to validate this correlation. NEW & NOTEWORTHY This is the first study to evaluate acid-base parameters in major central and hepatic vessels during stepwise liver resection. The pH, [Formula: see text], and base excess (BE) decreased, but [Formula: see text] values increased after 75% resection in the portal and jugular veins. Extended hepatectomy correlated with reduced BE in the hepatic artery. Because 75% resection is the limit for small for size and flow

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%.

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.

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

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).

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.

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.

Liver Hemangioma

MedlinePlus

Liver hemangioma Overview A liver hemangioma (he-man-jee-O-muh) is a noncancerous (benign) mass in the liver. A liver hemangioma is made up of a tangle of blood vessels. Other terms for a liver hemangioma are hepatic hemangioma and cavernous hemangioma. Most ...

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.

Roi Detection and Vessel Segmentation in Retinal Image

NASA Astrophysics Data System (ADS)

Sabaz, F.; Atila, U.

2017-11-01

Diabetes disrupts work by affecting the structure of the eye and afterwards leads to loss of vision. Depending on the stage of disease that called diabetic retinopathy, there are sudden loss of vision and blurred vision problems. Automated detection of vessels in retinal images is a useful study to diagnose eye diseases, disease classification and other clinical trials. The shape and structure of the vessels give information about the severity of the disease and the stage of the disease. Automatic and fast detection of vessels allows for a quick diagnosis of the disease and the treatment process to start shortly. ROI detection and vessel extraction methods for retinal image are mentioned in this study. It is shown that the Frangi filter used in image processing can be successfully used in detection and extraction of vessels.

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

Segmental liver incarceration through a recurrent incisional lumbar hernia.

PubMed

Salemis, Nikolaos S; Nisotakis, Konstantinos; Gourgiotis, Stavros; Tsohataridis, Efstathios

2007-08-01

Lumbar hernia is a rare congenital or acquired defect of the posterior abdominal wall. The acquired type is more common and occurs mainly as an incisional defect after flank surgery. Incarceration or strangulation of hernia contents is uncommon. Segmental liver incarceration through a recurrent incisional lumbar defect was diagnosed in a 58 years old woman by magnetic resonance imaging. The patient underwent an open repair of the complicated hernia. An expanded polytetraflouoroethylene (e-PTFE) mesh was fashioned as a sublay prosthesis. She had an uncomplicated postoperative course. Follow-up examinations revealed no evidence of recurrence. Although lumbar hernia rarely results in incarceration or strangulation, early repair is necessary because of the risks of complications and the increasing difficulty in repairment as it enlarges. Surgical repair is often difficult and challenging.

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.

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.

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.

Landmark-Based 3D Elastic Registration of Pre- and Postoperative Liver CT Data

NASA Astrophysics Data System (ADS)

Lange, Thomas; Wörz, Stefan; Rohr, Karl; Schlag, Peter M.

The qualitative and quantitative comparison of pre- and postoperative image data is an important possibility to validate computer assisted surgical procedures. Due to deformations after surgery a non-rigid registration scheme is a prerequisite for a precise comparison. Interactive landmark-based schemes are a suitable approach. Incorporation of a priori knowledge about the anatomical structures to be registered may help to reduce interaction time and improve accuracy. Concerning pre- and postoperative CT data of oncological liver resections the intrahepatic vessels are suitable anatomical structures. In addition to using landmarks at vessel branchings, we here introduce quasi landmarks at vessel segments with anisotropic localization precision. An experimental comparison of interpolating thin-plate splines (TPS) and Gaussian elastic body splines (GEBS) as well as approximating GEBS on both types of landmarks is performed.

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.

Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM).

PubMed

Wang, Xuehu; Zheng, Yongchang; Gan, Lan; Wang, Xuan; Sang, Xinting; Kong, Xiangfeng; Zhao, Jie

2017-01-01

This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively.

SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

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

Gou, S; Rapacchi, S; Hu, P

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 propagatedmore » 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

Generalized pixel profiling and comparative segmentation with application to arteriovenous malformation segmentation.

PubMed

Babin, D; Pižurica, A; Bellens, R; De Bock, J; Shang, Y; Goossens, B; Vansteenkiste, E; Philips, W

2012-07-01

Extraction of structural and geometric information from 3-D images of blood vessels is a well known and widely addressed segmentation problem. The segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, with a special application in diagnostics and surgery on arteriovenous malformations (AVM). However, the techniques addressing the problem of the AVM inner structure segmentation are rare. In this work we present a novel method of pixel profiling with the application to segmentation of the 3-D angiography AVM images. Our algorithm stands out in situations with low resolution images and high variability of pixel intensity. Another advantage of our method is that the parameters are set automatically, which yields little manual user intervention. The results on phantoms and real data demonstrate its effectiveness and potentials for fine delineation of AVM structure. Copyright © 2012 Elsevier B.V. All rights reserved.

Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies.

PubMed

Sanz-Requena, Roberto; Moratal, David; García-Sánchez, Diego Ramón; Bodí, Vicente; Rieta, José Joaquín; Sanchis, Juan Manuel

2007-03-01

Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study. The methodology described in this work has four steps: a pre-processing of IVUS images, a segmentation of media-adventitia contour, a detection of intima and plaque and a 3D reconstruction of the vessel. Preprocessing is intended to remove noise from the images without blurring the edges. Segmentation of media-adventitia contour is achieved using active contours (snakes). In particular, we use the gradient vector flow (GVF) as external force for the snakes. The detection of lumen border is obtained taking into account gray-level information of the inner part of the previously detected contours. A knowledge-based approach is used to determine which level of gray corresponds statistically to the different regions of interest: intima, plaque and lumen. The catheter region is automatically discarded. An estimate of plaque type is also given. Finally, 3D reconstruction of all detected regions is made. The suitability of this methodology has been verified for the analysis and visualization of plaque length, stenosis severity, automatic detection of the most problematic regions, calculus of plaque volumes and a preliminary estimation of plaque type obtaining for automatic measures of lumen and vessel area an average error smaller than 1mm(2) (equivalent aproximately to 10% of the average measure), for calculus of plaque and lumen volume errors smaller than 0.5mm(3) (equivalent approximately to 20% of the average measure) and for plaque type estimates a mismatch of less than 8% in the analysed frames.

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.

SU-E-I-87: Automated Liver Segmentation Method for CBCT Dataset by Combining Sparse Shape Composition and Probabilistic Atlas Construction

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

Li, Dengwang; Liu, Li; Chen, Jinhu

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 withmore » 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

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.

A new venous conduit utilizing the recipient portal vein branches for segment V in adult partial liver transplantation.

PubMed

Moon, I S; Kim, D G; Lee, M D; Hong, S K; Park, S C; Oh, D Y; Ahn, S T; Lee, Y J

2005-03-01

Right anterior-medial lobe congestion due to temporary clamping of segment V and/or VIII is common in the operative theater during adult donor right lobe liver transplantation, the most common procedure in our institute. We have used an autogenous saphenous vein conduit to recipient portal vein tributaries in 15 cases, as a "Y-to-I venoplasty" since January 2004. The recipient portal vein is transected 5 mm proximal to its bifurcation and extended to both sides with partial hepatic dissection. The "Y-to-I venoplasty" is made by suture closure of the portal vein transversely to form a tube. The average length is 7.5 cm with a 1.3 cm width. One end of "Y-to-I venoplasty" conduit is anastomosed to the donor segment V branch on the back table. And the other end is anastomosed directly to the IVC via a new window or the middle hepatic vein stump in recipient. The phase distension of the conduit with respiration is noted in the operative field. A 6/15 (40%) patency rate, was observed by CT angiography at the second postoperative week. All-patient conduits showed good flow on serial examinations at the 60th postoperative day. This new venous graft, made of recipient portal vein is a good conduit for segment V decongestion in adult right lobe partial liver transplantation.

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.

Influence of large intrahepatic blood vessels on the gross and histological characteristics of lesions produced by radiofrequency ablation in a pig liver model.

PubMed

Tamaki, Katsuyoshi; Shimizu, Ichiro; Oshio, Atsuo; Fukuno, Hiroshi; Inoue, Hiroshi; Tsutsui, Akemi; Shibata, Hiroshi; Sano, Nobuya; Ito, Susumu

2004-12-01

To determine whether the presence of large intrahepatic blood vessels (>/=3 mm) affect radiofrequency (RF)-induced coagulation necrosis, the gross and histological characteristics of RF-ablated areas proximal to or around vessels were examined in normal pig livers. An RF ablation treatment using a two-stepwise extension technique produced 12 lesions: six contained vessels (Group A), and the other six were localized around vessels (Group B). Gross examination revealed that the longest and shortest diameters of the ablated lesions were significantly larger in Group B than in Group A. In Group A, patent vessels contiguous to the lesion were present in a tongue-shaped area, whereas the lesions in Group B were spherical. Staining with nicotinamide adenine dinucleotide diaphorase was negative within the ablated area; but, if vessels were present in the ablated area, the cells around the vessels in an opposite direction to the ablation were stained blue. Roll-off can be achieved with 100% cellular destruction within a lesion that does not contain large vessels. The ablated area was decreased in lesions that contained large vessels, suggesting that the presence of large vessels in the ablated area further increases the cooling effect and may require repeated RF ablation treatment to achieve complete coagulation necrosis.

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.

Segmental liver resection assisted by HIFU: tissue precauterization using a toroidal-shaped HIFU transducer

NASA Astrophysics Data System (ADS)

N'Djin, W. A.; Melodelima, D.; Schenone, F.; Rivoire, M.; Chapelon, J. Y.

2010-03-01

The development of new cauterization techniques for hepatic resection is critical for improving the safety of the procedure. Previous studies showed the feasibility of using HIFU or radiofrequency precoagulation to limit blood loss during dissection of the organ. Here we report a new therapeutic modality using high intensity focused ultrasound (HIFU) to perform a bloodless hepatic resection that could represent a promising alternative. A comparative study was performed to evaluate the interest of using this complementary tool to improve surgical resection in the liver. This study used a 3 MHz HIFU toroidal-shaped phased array transducer which allows the generation of a single conical lesion of 7 cm3 in 40 seconds. In order to minimize blood loss and dissection time, a barrier of coagulative necrosis was generated with the HIFU device before hepatectomy, by juxtaposing single conical lesions on the line of dissection. Resection assisted by HIFU (RA-HIFU) was compared with classical dissections with clamping (RC) and without clamping (Control). For each technique 14 partial liver resections were performed in seven pigs. The parameters examined were vascular control and times of treatment. Precoagulation allowed the vascular isolation of small vessels and surgical clips were mainly used for the control of vessels>5 mm in diameter. The number of clips used per unit of liver surface dissected in RA-HIFU (0.8±0.3 cm-2) was significantly lower than in the other groups (RC: 1.6±0.4 cm-2, Control: 1.8±0.8 cm-2, p<0.01). In addition, blood loss was lower in RA-HIFU (7.4±6.5 ml.cm-2) than in RC (11.2±4.5 ml.cm-2) and Control (14.0±6.7 ml.cm-2). The time of dissection in RA-HIFU (13±5 min) was shorter than in RC (23±8 minutes) and Control (18±5 minutes). The feasibility and the efficiency of RA-HIFU using a toroidal-shaped HIFU transducer without additional devices were demonstrated. This technique enhances the resection procedure and will be able to be tested in

The prognostic significance of lymphatics in colorectal liver metastases.

PubMed

Muralidharan, Vijayaragavan; Nguyen, Linh; Banting, Jonathan; Christophi, Christopher

2014-01-01

Background. Colorectal Cancer (CRC) is the most common form of cancer diagnosed in Australia across both genders. Approximately, 40%-60% of patients with CRC develop metastasis, the liver being the most common site. Almost 70% of CRC mortality can be attributed to the development of liver metastasis. This study examines the pattern and density of lymphatics in colorectal liver metastases (CLM) as predictors of survival following hepatic resection for CLM. Methods. Patient tissue samples were obtained from the Victorian Cancer Biobank. Immunohistochemistry was used to examine the spatial differences in blood and lymphatic vessel densities between different regions within the tumor (CLM) and surrounding host tissue. Lymphatic vessel density (LVD) was assessed as a potential prognostic marker. Results. Patients with low lymphatic vessel density in the tumor centre, tumor periphery, and adjacent normal liver demonstrated a significant disease-free survival advantage compared to patients with high lymphatic vessel density (P = 0.01, P > 0.01, and P = 0.05, resp.). Lymphatic vessel density in the tumor centre and periphery and adjacent normal liver was an accurate predictive marker of disease-free survival (P = 0.05). Conclusion. Lymphatic vessel density in CLM appears to be an accurate predictor of recurrence and disease-free survival.

An application of cascaded 3D fully convolutional networks for medical image segmentation.

PubMed

Roth, Holger R; Oda, Hirohisa; Zhou, Xiangrong; Shimizu, Natsuki; Yang, Ying; Hayashi, Yuichiro; Oda, Masahiro; Fujiwara, Michitaka; Misawa, Kazunari; Mori, Kensaku

2018-06-01

Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN. This reduces the number of voxels the second FCN has to classify to ∼10% and allows it to focus on more detailed segmentation of the organs and vessels. We utilize training and validation sets consisting of 331 clinical CT images and test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans, targeting three anatomical organs (liver, spleen, and pancreas). In challenging organs such as the pancreas, our cascaded approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset. We compare with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels. Furthermore, we explore fine-tuning our models to different datasets. Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results. 1 . Copyright © 2018 Elsevier Ltd. All rights reserved.

Venous tree separation in the liver: graph partitioning using a non-ising model.

PubMed

O'Donnell, Thomas; Kaftan, Jens N; Schuh, Andreas; Tietjen, Christian; Soza, Grzegorz; Aach, Til

2011-01-01

Entangled tree-like vascular systems are commonly found in the body (e.g., in the peripheries and lungs). Separation of these systems in medical images may be formulated as a graph partitioning problem given an imperfect segmentation and specification of the tree roots. In this work, we show that the ubiquitous Ising-model approaches (e.g., Graph Cuts, Random Walker) are not appropriate for tackling this problem and propose a novel method based on recursive minimal paths for doing so. To motivate our method, we focus on the intertwined portal and hepatic venous systems in the liver. Separation of these systems is critical for liver intervention planning, in particular when resection is involved. We apply our method to 34 clinical datasets, each containing well over a hundred vessel branches, demonstrating its effectiveness.

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.

Adaptation of mesenteric lymphatic vessels to prolonged changes in transmural pressure.

PubMed

Dongaonkar, R M; Nguyen, T L; Quick, C M; Hardy, J; Laine, G A; Wilson, E; Stewart, R H

2013-07-15

In vitro studies have revealed that acute increases in transmural pressure increase lymphatic vessel contractile function. However, adaptive responses to prolonged changes in transmural pressure in vivo have not been reported. Therefore, we developed a novel bovine mesenteric lymphatic partial constriction model to test the hypothesis that lymphatic vessels exposed to higher transmural pressures adapt functionally to become stronger pumps than vessels exposed to lower transmural pressures. Postnodal mesenteric lymphatic vessels were partially constricted for 3 days. On postoperative day 3, constricted vessels were isolated, and divided into upstream (UP) and downstream (DN) segment groups, and instrumented in an isolated bath. Although there were no differences between the passive diameters of the two groups, both diastolic diameter and systolic diameter were significantly larger in the UP group than in the DN group. The pump index of the UP group was also higher than that in the DN group. In conclusion, this is the first work to report how lymphatic vessels adapt to prolonged changes in transmural pressure in vivo. Our results suggest that vessel segments upstream of the constriction adapt to become both better fluid conduits and lymphatic pumps than downstream segments.

Automatic partitioning of head CTA for enabling segmentation

NASA Astrophysics Data System (ADS)

Suryanarayanan, Srikanth; Mullick, Rakesh; Mallya, Yogish; Kamath, Vidya; Nagaraj, Nithin

2004-05-01

Radiologists perform a CT Angiography procedure to examine vascular structures and associated pathologies such as aneurysms. Volume rendering is used to exploit volumetric capabilities of CT that provides complete interactive 3-D visualization. However, bone forms an occluding structure and must be segmented out. The anatomical complexity of the head creates a major challenge in the segmentation of bone and vessel. An analysis of the head volume reveals varying spatial relationships between vessel and bone that can be separated into three sub-volumes: "proximal", "middle", and "distal". The "proximal" and "distal" sub-volumes contain good spatial separation between bone and vessel (carotid referenced here). Bone and vessel appear contiguous in the "middle" partition that remains the most challenging region for segmentation. The partition algorithm is used to automatically identify these partition locations so that different segmentation methods can be developed for each sub-volume. The partition locations are computed using bone, image entropy, and sinus profiles along with a rule-based method. The algorithm is validated on 21 cases (varying volume sizes, resolution, clinical sites, pathologies) using ground truth identified visually. The algorithm is also computationally efficient, processing a 500+ slice volume in 6 seconds (an impressive 0.01 seconds / slice) that makes it an attractive algorithm for pre-processing large volumes. The partition algorithm is integrated into the segmentation workflow. Fast and simple algorithms are implemented for processing the "proximal" and "distal" partitions. Complex methods are restricted to only the "middle" partition. The partitionenabled segmentation has been successfully tested and results are shown from multiple cases.

Automatic multi-organ segmentation using learning-based segmentation and level set optimization.

PubMed

Kohlberger, Timo; Sofka, Michal; Zhang, Jingdan; Birkbeck, Neil; Wetzl, Jens; Kaftan, Jens; Declerck, Jérôme; Zhou, S Kevin

2011-01-01

We present a novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images. Thereby we combine the advantages of learning-based approaches on point cloud-based shape representation, such a speed, robustness, point correspondences, with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps. In a benchmark on 10-100 annotated datasets for the liver, the lungs, and the kidneys we show that the proposed system yields segmentation accuracies of 1.17-2.89 mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40% increase in accuracy.

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.

Appropriate donor size for porcine liver xenotransplant.

PubMed

Soleimani, Mehrdad; Fonouni, Hamidreza; Esmaeilzadeh, Majid; Kashfi, Arash; Fani Yazdi, Seyed Hashem; Golriz, Mohammad; Hafezi, Mohammadreza; Rahbari, Nuh N; Schmidt, Jan; Mehrabi, Arianeb

2012-04-01

Owing to an imbalance between demand and supply, which is more prominent in pediatric transplant, every year more patients lose their lives on waiting lists. In addition to the use of deceased-donor split and living-donor organs, xenotransplant could provide a solution if associated problems, such as immunologic and physiologic ones, are solved. This study sought to analyze the surgical aspects for liver xenotransplant in a porcine model. Landrace pigs (n=22, 23 to 37 kg) underwent a laparotomy under general anesthesia. The hepatic hilum was prepared and the common bile ducts, common hepatic artery, portal vein, supra- and infrahepatic inferior vena cava were identified. The length and diameter of each vessel and bile duct and the weight of the liver were measured. Pearson tests showed a clear correlation between the increase of the pigs' weight and the livers' weight, and the length of the vessels and the bile ducts. We did not find a clear correlation between the increase of the pigs' liver weight and the diameters of the vessels and the bile duct. As the first reporting, this study on xenotransplants from the surgical point of view, we postulate that it could be possible to estimate the size of the liver and the proper length of its vessels and bile duct by weighing only the pigs. It was not feasible to match the diameter of mentioned structures by the livers' weight. However, the weight of pig's liver as well as vascular anatomy of pigs appeared to be suitable alternative for the human liver.

Rotation invariant eigenvessels and auto-context for retinal vessel detection

NASA Astrophysics Data System (ADS)

Montuoro, Alessio; Simader, Christian; Langs, Georg; Schmidt-Erfurth, Ursula

2015-03-01

Retinal vessels are one of the few anatomical landmarks that are clearly visible in various imaging modalities of the eye. As they are also relatively invariant to disease progression, retinal vessel segmentation allows cross-modal and temporal registration enabling exact diagnosing for various eye diseases like diabetic retinopathy, hypertensive retinopathy or age-related macular degeneration (AMD). Due to the clinical significance of retinal vessels many different approaches for segmentation have been published in the literature. In contrast to other segmentation approaches our method is not specifically tailored to the task of retinal vessel segmentation. Instead we utilize a more general image classification approach and show that this can achieve comparable results. In the proposed method we utilize the concepts of eigenfaces and auto-context. Eigenfaces have been described quite extensively in the literature and their performance is well known. They are however quite sensitive to translation and rotation. The former was addressed by computing the eigenvessels in local image windows of different scales, the latter by estimating and correcting the local orientation. Auto-context aims to incorporate automatically generated context information into the training phase of classification approaches. It has been shown to improve the performance of spinal cord segmentation4 and 3D brain image segmentation. The proposed method achieves an area under the receiver operating characteristic (ROC) curve of Az = 0.941 on the DRIVE data set, being comparable to current state-of-the-art approaches.

New algorithm for detecting smaller retinal blood vessels in fundus images

NASA Astrophysics Data System (ADS)

LeAnder, Robert; Bidari, Praveen I.; Mohammed, Tauseef A.; Das, Moumita; Umbaugh, Scott E.

2010-03-01

About 4.1 million Americans suffer from diabetic retinopathy. To help automatically diagnose various stages of the disease, a new blood-vessel-segmentation algorithm based on spatial high-pass filtering was developed to automatically segment blood vessels, including the smaller ones, with low noise. Methods: Image database: Forty, 584 x 565-pixel images were collected from the DRIVE image database. Preprocessing: Green-band extraction was used to obtain better contrast, which facilitated better visualization of retinal blood vessels. A spatial highpass filter of mask-size 11 was applied. A histogram stretch was performed to enhance contrast. A median filter was applied to mitigate noise. At this point, the gray-scale image was converted to a binary image using a binary thresholding operation. Then, a NOT operation was performed by gray-level value inversion between 0 and 255. Postprocessing: The resulting image was AND-ed with its corresponding ring mask to remove the outer-ring (lens-edge) artifact. At this point, the above algorithm steps had extracted most of the major and minor vessels, with some intersections and bifurcations missing. Vessel segments were reintegrated using the Hough transform. Results: After applying the Hough transform, both the average peak SNR and the RMS error improved by 10%. Pratt's Figure of Merit (PFM) was decreased by 6%. Those averages were better than [1] by 10-30%. Conclusions: The new algorithm successfully preserved the details of smaller blood vessels and should prove successful as a segmentation step for automatically identifying diseases that affect retinal blood vessels.

Gap-free segmentation of vascular networks with automatic image processing pipeline.

PubMed

Hsu, Chih-Yang; Ghaffari, Mahsa; Alaraj, Ali; Flannery, Michael; Zhou, Xiaohong Joe; Linninger, Andreas

2017-03-01

Current image processing techniques capture large vessels reliably but often fail to preserve connectivity in bifurcations and small vessels. Imaging artifacts and noise can create gaps and discontinuity of intensity that hinders segmentation of vascular trees. However, topological analysis of vascular trees require proper connectivity without gaps, loops or dangling segments. Proper tree connectivity is also important for high quality rendering of surface meshes for scientific visualization or 3D printing. We present a fully automated vessel enhancement pipeline with automated parameter settings for vessel enhancement of tree-like structures from customary imaging sources, including 3D rotational angiography, magnetic resonance angiography, magnetic resonance venography, and computed tomography angiography. The output of the filter pipeline is a vessel-enhanced image which is ideal for generating anatomical consistent network representations of the cerebral angioarchitecture for further topological or statistical analysis. The filter pipeline combined with computational modeling can potentially improve computer-aided diagnosis of cerebrovascular diseases by delivering biometrics and anatomy of the vasculature. It may serve as the first step in fully automatic epidemiological analysis of large clinical datasets. The automatic analysis would enable rigorous statistical comparison of biometrics in subject-specific vascular trees. The robust and accurate image segmentation using a validated filter pipeline would also eliminate operator dependency that has been observed in manual segmentation. Moreover, manual segmentation is time prohibitive given that vascular trees have more than thousands of segments and bifurcations so that interactive segmentation consumes excessive human resources. Subject-specific trees are a first step toward patient-specific hemodynamic simulations for assessing treatment outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tissues segmentation based on multi spectral medical images

NASA Astrophysics Data System (ADS)

Li, Ya; Wang, Ying

2017-11-01

Each band image contains the most obvious tissue feature according to the optical characteristics of different tissues in different specific bands for multispectral medical images. In this paper, the tissues were segmented by their spectral information at each multispectral medical images. Four Local Binary Patter descriptors were constructed to extract blood vessels based on the gray difference between the blood vessels and their neighbors. The segmented tissue in each band image was merged to a clear image.

An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images.

PubMed

Dash, Jyotiprava; Bhoi, Nilamani

2018-04-26

Pathological disorders may happen due to small changes in retinal blood vessels which may later turn into blindness. Hence, the accurate segmentation of blood vessels is becoming a challenging task for pathological analysis. This paper offers an unsupervised recursive method for extraction of blood vessels from ophthalmoscope images. First, a vessel-enhanced image is generated with the help of gamma correction and contrast-limited adaptive histogram equalization (CLAHE). Next, the vessels are extracted iteratively by applying an adaptive thresholding technique. At last, a final vessel segmented image is produced by applying a morphological cleaning operation. Evaluations are accompanied on the publicly available digital retinal images for vessel extraction (DRIVE) and Child Heart And Health Study in England (CHASE_DB1) databases using nine different measurements. The proposed method achieves average accuracies of 0.957 and 0.952 on DRIVE and CHASE_DB1 databases respectively.

Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.

PubMed

Raja, D Siva Sundhara; Vasuki, S

2015-01-01

Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.

WE-D-18A-05: Construction of Realistic Liver Phantoms From Patient Images and a Commercial 3D Printer

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

Leng, S; Vrieze, T; Kuhlmann, J

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 availablemore » 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.« less

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. Copyright © 2011 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Assessment of liver function in primary biliary cirrhosis using Gd-EOB-DTPA-enhanced liver MRI.

PubMed

Nilsson, Henrik; Blomqvist, Lennart; Douglas, Lena; Nordell, Anders; Jonas, Eduard

2010-10-01

Gd-EOB-DTPA (gadolinium ethoxybenzyl diethylenetriaminepentaacetic acid) is a gadolinium-based hepatocyte-specific contrast agent for magnetic resonance imaging (MRI). The aim of this study was to determine whether the hepatic uptake and excretion of Gd-EOB-DTPA differ between patients with primary biliary cirrhosis (PBC) and healthy controls, and whether differences could be quantified. Gd-EOB-DTPA-enhanced liver MRI was performed in 20 healthy volunteers and 12 patients with PBC. The uptake of Gd-EOB-DTPA was assessed using traditional semi-quantitative parameters (C(max) , T(max) and T(1/2) ), as well as model-free parameters derived after deconvolutional analysis (hepatic extraction fraction [HEF], input-relative blood flow [irBF] and mean transit time [MTT]). In each individual, all parameters were calculated for each liver segment and the median of the segmental values was used to define a global liver median (GLM). Although the PBC patients had relatively mild disease according to their Model for End-stage Liver Disease (MELD), Child-Pugh and Mayo risk scores, they had significantly lower HEF and shorter MTT values compared with the healthy controls. These differences significantly increased with increasing MELD and Child-Pugh scores. Dynamic hepatocyte-specific contrast-enhanced MRI (DHCE-MRI) has a potential role as an imaging-based liver function test. The high spatial resolution of MRI enables hepatic function to be assessed on segmental and sub-segmental levels. © 2010 International Hepato-Pancreato-Biliary Association.

Intrahepatic venous anastomoses with a focus on the middle hepatic vein anastomoses in normal human livers: anatomical study on liver corrosion casts.

PubMed

Hribernik, Marija; Trotovšek, Blaž

2014-04-01

The aim of this study is to present the anatomical data about intrahepatic venous anastomoses found in normal human livers. The focus is on the middle hepatic vein (MHV) anastomoses, because their existence or non-existence could be of crucial importance in tumour resections as well as in split or living donor liver transplantations. The frequency of livers with intrahepatic venous anastomoses was determined on 164 corrosion casts and the diameter of each anastomosis was measured. Additionally, the type of connection and the position within the liver (liver segment) was determined for each MHV anastomosis. Intrahepatic venous anastomoses were found in 46 % (75/164), whereas MHV anastomoses were found in 28 % (44/164) of liver casts. Most commonly (39/44), MHV had anastomotic connections with the right hepatic vein (RHV), and also with the inferior RHV, the left hepatic vein and the short subhepatic vein. In more than three quarters of liver casts, MHV-RHV anastomoses were found in liver segment 8; in 45 % of cases, there was more than one anastomosis in this liver segment. The diameter of MHV-RHV anastomoses found in segment 8 was ≥1 mm in 90.6 % of cases. As MHV anastomoses were present in more than a quarter of all examined liver casts, we believe that detailed anatomical data presented in this article, together with up to date radiologic technics which enable even 3D reconstruction of venous anastomoses in the liver, could contribute to the clinician's decisions when planning surgical procedures.

Angiostatin inhibits experimental liver fibrosis in mice.

PubMed

Vogten, J Mathys; Drixler, Tamas A; te Velde, Elisabeth A; Schipper, Marguerite E; van Vroonhoven, Theo J M V; Voest, Emile E; Borel Rinkes, Inne H M

2004-07-01

Liver fibrosis is a response to chronic hepatic damage, which ultimately leads to liver failure and necessitates liver transplantation. A characteristic of fibrosis is pathological vessel growth. This type of angiogenesis may contribute to the disturbance of hepatocyte perfusion dynamics and lead to aggravation of disease. We hypothesized that angiostatin can inhibit pathological vessel growth and, consequently, the development of hepatic fibrosis. Hepatic fibrosis was induced by injection of carbon tetrachloride for 5 weeks. Angiostatin mice received carbon tetrachloride for 5 weeks and angiostatin during weeks 4 and 5. After 5 weeks, immunohistochemistry for endothelial cell marker von Willebrand factor and for cell proliferation was performed. Angiogenesis was quantified by counting the number of immunopositive microvessels. Also, the relative fibrotic surface was determined using Sirius Red histostaining and computer image analysis. Immunohistochemistry revealed increased expression for von Willebrand factor in fibrotic livers. Immunopositive microvessels were localized in fibrotic areas surrounding larger vessels and in emerging fibrotic septa. Angiostatin reduced the number of immunopositive microvessels by 69% (p<0.001). In addition, angiostatin reduced the relative fibrotic area in the liver by 63+/-0.1% (p<0.001). Finally, angiostatin treatment was not associated with differences in cell proliferation. Angiostatin inhibits the development of pathological angiogenesis and liver fibrosis in mice. These results warrant further evaluation of angiostatin as an antifibrotic agent, potentially contributing to the deferment of liver transplantation and reduced recurrence of fibrotic disease in the transplanted liver.

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.

Cross-sectional and longitudinal evaluation of liver volume and total liver fat burden in adults with nonalcoholic steatohepatitis

PubMed Central

Tang, An; Chen, Joshua; Le, Thuy-Anh; Changchien, Christopher; Hamilton, Gavin; Middleton, Michael S.; Loomba, Rohit; Sirlin, Claude B.

2014-01-01

Purpose To explore the cross-sectional and longitudinal relationships between fractional liver fat content, liver volume, and total liver fat burden. Methods In 43 adults with non-alcoholic steatohepatitis participating in a clinical trial, liver volume was estimated by segmentation of magnitude-based low-flip-angle multiecho GRE images. The liver mean proton density fat fraction (PDFF) was calculated. The total liver fat index (TLFI) was estimated as the product of liver mean PDFF and liver volume. Linear regression analyses were performed. Results Cross-sectional analyses revealed statistically significant relationships between TLFI and liver mean PDFF (R2 = 0.740 baseline/0.791 follow-up, P < 0.001 baseline/P < 0.001 follow-up), and between TLFI and liver volume (R2 = 0.352/0.452, P < 0.001/< 0.001). Longitudinal analyses revealed statistically significant relationships between liver volume change and liver mean PDFF change (R2 = 0.556, P < 0.001), between TLFI change and liver mean PDFF change (R2 = 0.920, P < 0.001), and between TLFI change and liver volume change (R2 = 0.735, P < 0.001). Conclusion Liver segmentation in combination with MRI-based PDFF estimation may be used to monitor liver volume, liver mean PDFF, and TLFI in a clinical trial. PMID:25015398

Histopathological findings in colorectal liver metastases after electrochemotherapy

PubMed Central

Gasljevic, Gorana; Edhemovic, Ibrahim; Cemazar, Maja; Brecelj, Erik; Gadzijev, Eldar M.; Music, Maja M.

2017-01-01

Electrochemotherapy of colorectal liver metastases has been proven to be feasible, safe and effective in a phase I/II study. In that study, a specific group of patients underwent two-stage operation, and the detailed histopathological evaluation of the resected tumors is presented here. Regressive changes in electrochemotherapy-treated liver metastases were evaluated after the second operation (in 8–10 weeks) in 7 patients and 13 metastases when the treated metastases were resected. Macroscopic and microscopic changes were analyzed. Electrochemotherapy induced coagulation necrosis in the treated area encompassing both tumor and a narrow band of normal tissue. The area became necrotic, encapsulated in a fibrous envelope while preserving the functionality of most of the vessels larger than 5 mm in diameter and a large proportion of biliary structures, but the smaller blood vessels displayed various levels of damage. At the time of observation, 8–10 weeks after electrochemotherapy, regenerative changes were already seen in the peripheral parts of the treated area. This study demonstrates regressive changes in the whole electrochemotherapy-treated area of the liver. Further evidence of disruption of vessels less than 5 mm in diameter and preservation of the larger vessels by electrochemotherapy is provided. These findings are important because electrochemotherapy has been indicated for the therapy of metastases near major blood vessels in the liver to provide a safe approach with good antitumor efficacy. PMID:28686650

Histopathological findings in colorectal liver metastases after electrochemotherapy.

PubMed

Gasljevic, Gorana; Edhemovic, Ibrahim; Cemazar, Maja; Brecelj, Erik; Gadzijev, Eldar M; Music, Maja M; Sersa, Gregor

2017-01-01

Electrochemotherapy of colorectal liver metastases has been proven to be feasible, safe and effective in a phase I/II study. In that study, a specific group of patients underwent two-stage operation, and the detailed histopathological evaluation of the resected tumors is presented here. Regressive changes in electrochemotherapy-treated liver metastases were evaluated after the second operation (in 8-10 weeks) in 7 patients and 13 metastases when the treated metastases were resected. Macroscopic and microscopic changes were analyzed. Electrochemotherapy induced coagulation necrosis in the treated area encompassing both tumor and a narrow band of normal tissue. The area became necrotic, encapsulated in a fibrous envelope while preserving the functionality of most of the vessels larger than 5 mm in diameter and a large proportion of biliary structures, but the smaller blood vessels displayed various levels of damage. At the time of observation, 8-10 weeks after electrochemotherapy, regenerative changes were already seen in the peripheral parts of the treated area. This study demonstrates regressive changes in the whole electrochemotherapy-treated area of the liver. Further evidence of disruption of vessels less than 5 mm in diameter and preservation of the larger vessels by electrochemotherapy is provided. These findings are important because electrochemotherapy has been indicated for the therapy of metastases near major blood vessels in the liver to provide a safe approach with good antitumor efficacy.

Segmentation of the heart and major vascular structures in cardiovascular CT images

NASA Astrophysics Data System (ADS)

Peters, J.; Ecabert, O.; Lorenz, C.; von Berg, J.; Walker, M. J.; Ivanc, T. B.; Vembar, M.; Olszewski, M. E.; Weese, J.

2008-03-01

Segmentation of organs in medical images can be successfully performed with shape-constrained deformable models. A surface mesh is attracted to detected image boundaries by an external energy, while an internal energy keeps the mesh similar to expected shapes. Complex organs like the heart with its four chambers can be automatically segmented using a suitable shape variablility model based on piecewise affine degrees of freedom. In this paper, we extend the approach to also segment highly variable vascular structures. We introduce a dedicated framework to adapt an extended mesh model to freely bending vessels. This is achieved by subdividing each vessel into (short) tube-shaped segments ("tubelets"). These are assigned to individual similarity transformations for local orientation and scaling. Proper adaptation is achieved by progressively adapting distal vessel parts to the image only after proximal neighbor tubelets have already converged. In addition, each newly activated tubelet inherits the local orientation and scale of the preceeding one. To arrive at a joint segmentation of chambers and vasculature, we extended a previous model comprising endocardial surfaces of the four chambers, the left ventricular epicardium, and a pulmonary artery trunk. Newly added are the aorta (ascending and descending plus arch), superior and inferior vena cava, coronary sinus, and four pulmonary veins. These vessels are organized as stacks of triangulated rings. This mesh configuration is most suitable to define tubelet segments. On 36 CT data sets reconstructed at several cardiac phases from 17 patients, segmentation accuracies of 0.61-0.80mm are obtained for the cardiac chambers. For the visible parts of the newly added great vessels, surface accuracies of 0.47-1.17mm are obtained (larger errors are asscociated with faintly contrasted venous structures).

Two separate thoroscopic segmentectomies with vessel sealing system.

PubMed

Watanabe, Atsushi; Miyajima, Masahiro; Kawaharada, Nobuyoshi; Higami, Tetsuya

2012-04-01

Total thoracoscopic segmentectomy is an appealing concept in terms of providing a parenchyma-sparing treatment. We describe our technique of two seperate total thoracoscopic segmentectomies by using a vessel sealing system (VSS). A 76-year old female with four gradually enlarging ground glass lesions on the right segment 2 (10 and 3 mm) and segment 6 (8 and 3 mm) was admitted to our institute for surgical diagnosis and treatment. Preoperative three-dimensional computed tomography showed that the A2 is composed of descending A2 and ascending A2, branch of V6 coursed to V2 and B1a originated from B2 and B1b from B3. Two separate segmentectomies were subsequently scheduled. Pulmonary vessel division was performed with VSS after proximal ligation. Intersegmental division by VSS and electrocautery was performed with the use of inflation-deflation demarcation line and the pulmonary veins along the intersegmental plane (V2a and V2c for segment 2 and V6b and V6c for segment 6) as guides to confirm the intersegmental plane after pulmonary artery and bronchial divisions. The intraoperative frozen-section examinations revealed adenocarcinoma in situ. Two separate segmentectomies were successfully completed, with a total operative time of 240 min and blood loss of 30 ml. VSS is a very useful and safe device for intersegmental division and pulmonary vessel division.

Pressure vessels fabricated with high-strength wire and electroformed nickel

NASA Technical Reports Server (NTRS)

Roth, B.

1966-01-01

Metal pressure vessels of various shapes having high strength-to-weight ratios are fabricated by using known techniques of filament winding and electroforming. This eliminates nonuniform wall thickness and unequal wall strength which resulted from welding formed vessel segments together.

A comparison of hepatic segmental anatomy as revealed by cross-sections and MPR CT imaging.

PubMed

Liu, Xue-Jing; Zhang, Jian-Fei; Sui, Hong-Jin; Yu, Sheng-Bo; Gong, Jin; Liu, Jie; Wu, Le-Bin; Liu, Cheng; Bai, Jian; Shi, Bing-Yi

2013-05-01

To compare the areas of human liver horizontal sections with computed tomography (CT) images and to evaluate whether the subsegments determined by CT are consistent with the actual anatomy. Six human cadaver livers were made into horizontal slices with multislice spiral CT three-dimensional (3D) reconstruction was used during infusion process. Each liver segment was displayed using different color, and 3D images of the portal and hepatic vein were reconstructed. Each segmental area was measured on CT-reconstructed images, which were compared with the actual area on the sections of the same liver. The measurements were performed at four key levels namely: (1) the three hepatic veins, (2) the left, and (3) the right branch of portal vein (PV), and (4) caudal to the bifurcation of the PV. By dividing the sum of these areas by the total area of the liver, the authors got the percentage of the incorrectly determined subsegmental areas. In addition to these percentage values, the maximum distances of the radiologically determined intersegmental boundaries from the true anatomic boundaries were measured. On the four key levels, an average of 28.64 ± 10.26% of the hepatic area of CT images was attributed to an incorrect segment. The mean-maximum error between artificial segments on images and actual anatomical segments was 3.81 ± 1.37 cm. The correlation between radiological segmenting method and actual anatomy was poor. The hepatic segments being divided strictly according to the branching point of the PV could be more informative during liver segmental resection. Copyright © 2012 Wiley Periodicals, Inc.

Diagnostic value of Doppler assessment of the hepatic and portal vessels and ultrasound of the spleen in liver disease.

PubMed

O'Donohue, John; Ng, Chaan; Catnach, Susan; Farrant, Patricia; Williams, Roger

2004-02-01

To investigate the clinical utility and the intra-observer and inter-observer variability of Doppler ultrasound assessment of the hepatic and portal vessels along with measurement of spleen size in the diagnosis of chronic liver disease and cirrhosis. Ultrasound measurements of portal vein diameter (PVD), portal vein velocity (PVV), hepatic arterial resistance index (HARI), hepatic vein profile (HVP), and spleen size were obtained in 49 controls and 45 patients with liver disease (23 with primary biliary cirrhosis, 22 with hepatitis C) by two experienced observers, who each performed three blinded measurements of each variable. Control values were derived from normal hospital workers. Percutaneous liver biopsies in 41 of the patients showed cirrhosis (14 patients), moderate/severe fibrosis (13 patients), and early disease (14 patients). Seventy-one percent of cirrhotic patients had splenomegaly (> 13.6 cm). The spleen size was significantly larger in cirrhotics (16.0 cm) than in non-cirrhotics (13.0 cm, P < 0.009) and healthy controls (10.7 cm, P < 0.00005), and was the only independent predictor of cirrhosis, with a threshold of 15 cm predicting cirrhosis with a specificity of 98%, positive predictive value of 93%, sensitivity of 57% and negative predictive value of 80%. HVP was abnormal in 76.9% of cirrhotics, 57.7% of non-cirrhotics and 2.1% of controls (P < 0.04). However, the mean PVV, PVD and HARI were no different between controls and patients or between cirrhotic and non-cirrhotic liver disease. There was significant inter-observer variability for PVV, but intra-observer and inter-observer variability was acceptable for the other measurements. Splenomegaly size and abnormal HVP are useful predictors of chronic liver disease and cirrhosis, and both can be measured reliably and reproducibly. However, Doppler measurements of PVV, PVD and HARI are not useful in distinguishing patients with chronic liver disease from normal controls.

Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline.

PubMed

Gao, Shan; van 't Klooster, Ronald; Brandts, Anne; Roes, Stijntje D; Alizadeh Dehnavi, Reza; de Roos, Albert; Westenberg, Jos J M; van der Geest, Rob J

2017-01-01

To develop and evaluate a method that can fully automatically identify the vessel wall boundaries and quantify the wall thickness for both common carotid artery (CCA) and descending aorta (DAO) from axial magnetic resonance (MR) images. 3T MRI data acquired with T 1 -weighted gradient-echo black-blood imaging sequence from carotid (39 subjects) and aorta (39 subjects) were used to develop and test the algorithm. The vessel wall segmentation was achieved by respectively fitting a 3D cylindrical B-spline surface to the boundaries of lumen and outer wall. The tube-fitting was based on the edge detection performed on the signal intensity (SI) profile along the surface normal. To achieve a fully automated process, Hough Transform (HT) was developed to estimate the lumen centerline and radii for the target vessel. Using the outputs of HT, a tube model for lumen segmentation was initialized and deformed to fit the image data. Finally, lumen segmentation was dilated to initiate the adaptation procedure of outer wall tube. The algorithm was validated by determining: 1) its performance against manual tracing; 2) its interscan reproducibility in quantifying vessel wall thickness (VWT); 3) its capability of detecting VWT difference in hypertensive patients compared with healthy controls. Statistical analysis including Bland-Altman analysis, t-test, and sample size calculation were performed for the purpose of algorithm evaluation. The mean distance between the manual and automatically detected lumen/outer wall contours was 0.00 ± 0.23/0.09 ± 0.21 mm for CCA and 0.12 ± 0.24/0.14 ± 0.35 mm for DAO. No significant difference was observed between the interscan VWT assessment using automated segmentation for both CCA (P = 0.19) and DAO (P = 0.94). Both manual and automated segmentation detected significantly higher carotid (P = 0.016 and P = 0.005) and aortic (P < 0.001 and P = 0.021) wall thickness in the hypertensive patients. A reliable and reproducible pipeline for fully

Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography.

PubMed

Hadjiiski, Lubomir; Liu, Jordan; Chan, Heang-Ping; Zhou, Chuan; Wei, Jun; Chughtai, Aamer; Kuriakose, Jean; Agarwal, Prachi; Kazerooni, Ella

2016-01-01

The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologist's reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases.

Enhancement of optic cup detection through an improved vessel kink detection framework

NASA Astrophysics Data System (ADS)

Wong, Damon W. K.; Liu, Jiang; Tan, Ngan Meng; Zhang, Zhuo; Lu, Shijian; Lim, Joo Hwee; Li, Huiqi; Wong, Tien Yin

2010-03-01

Glaucoma is a leading cause of blindness. The presence and extent of progression of glaucoma can be determined if the optic cup can be accurately segmented from retinal images. In this paper, we present a framework which improves the detection of the optic cup. First, a region of interest is obtained from the retinal fundus image, and a pallor-based preliminary cup contour estimate is determined. Patches are then extracted from the ROI along this contour. To improve the usability of the patches, adaptive methods are introduced to ensure the patches are within the optic disc and to minimize redundant information. The patches are then analyzed for vessels by an edge transform which generates pixel segments of likely vessel candidates. Wavelet, color and gradient information are used as input features for a SVM model to classify the candidates as vessel or non-vessel. Subsequently, a rigourous non-parametric method is adopted in which a bi-stage multi-resolution approach is used to probe and localize the location of kinks along the vessels. Finally, contenxtual information is used to fuse pallor and kink information to obtain an enhanced optic cup segmentation. Using a batch of 21 images obtained from the Singapore Eye Research Institute, the new method results in a 12.64% reduction in the average overlap error against a pallor only cup, indicating viable improvements in the segmentation and supporting the use of kinks for optic cup detection.

Importance Rat Liver Morphology and Vasculature in Surgical Research.

PubMed

Vdoviaková, Katarína; Vdoviaková, Katarína; Petrovová, Eva; Krešáková, Lenka; Maloveská, Marcela; Teleky, Jana; Jenčová, Janka; Živčák, Jozef; Jenča, Andrej

2016-12-02

BACKGROUND The laboratory rat is one of the most popular experimental models for the experimental surgery of the liver. The objective of this study was to investigate the morphometric parameters, physiological data, differences in configuration of liver lobes, biliary system, and vasculature (arteries, veins, and lymphatic vessels) of the liver in laboratory rats. In addition, this study supports the anatomic literature and identified similarities and differences with human and other mammals. MATERIAL AND METHODS Forty laboratory rats were dissected to prepare corrosion casts of vascular system specimens (n=20), determine the lymph vessels and lymph nodes (n=10), and for macroscopic anatomical dissection (n=10) of the rat liver. The results are listed in percentages. The anatomical nomenclature of the liver morphology, its arteries, veins, lymph nodes, and lymphatic vessels are in accordance with Nomina Anatomica Veterinaria. RESULTS We found many variations in origin, direction, and division of the arterial, venous, and lymphatic systems in rat livers, and found differences in morphometric parameters compared to results reported by other authors. The portal vein was formed by 4 tributaries in 23%, by 3 branches in 64%, and by 2 tributaries in 13%. The liver lymph was drained to the 2 different lymph nodes. The nomenclature and morphological characteristics of the rat liver vary among authors. CONCLUSIONS Our results may be useful for the planing of experimental surgery and for cooperation with other investigation methods to help fight liver diseases in human populations.

Importance Rat Liver Morphology and Vasculature in Surgical Research

PubMed Central

Vdoviaková, Katarína; Petrovová, Eva; Krešáková, Lenka; Maloveská, Marcela; Teleky, Jana; Jenčová, Janka; Živčák, Jozef; Jenča, Andrej

2016-01-01

Background The laboratory rat is one of the most popular experimental models for the experimental surgery of the liver. The objective of this study was to investigate the morphometric parameters, physiological data, differences in configuration of liver lobes, biliary system, and vasculature (arteries, veins, and lymphatic vessels) of the liver in laboratory rats. In addition, this study supports the anatomic literature and identified similarities and differences with human and other mammals. Material/Methods Forty laboratory rats were dissected to prepare corrosion casts of vascular system specimens (n=20), determine the lymph vessels and lymph nodes (n=10), and for macroscopic anatomical dissection (n=10) of the rat liver. The results are listed in percentages. The anatomical nomenclature of the liver morphology, its arteries, veins, lymph nodes, and lymphatic vessels are in accordance with Nomina Anatomica Veterinaria. Results We found many variations in origin, direction, and division of the arterial, venous, and lymphatic systems in rat livers, and found differences in morphometric parameters compared to results reported by other authors. The portal vein was formed by 4 tributaries in 23%, by 3 branches in 64%, and by 2 tributaries in 13%. The liver lymph was drained to the 2 different lymph nodes. The nomenclature and morphological characteristics of the rat liver vary among authors. Conclusions Our results may be useful for the planing of experimental surgery and for cooperation with other investigation methods to help fight liver diseases in human populations. PMID:27911356

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.

Haemodynamic changes in hepatocellular carcinoma and liver parenchyma under balloon occlusion of the hepatic artery.

PubMed

Sugihara, Fumie; Murata, Satoru; Ueda, Tatsuo; Yasui, Daisuke; Yamaguchi, Hidenori; Miki, Izumi; Kawamoto, Chiaki; Uchida, Eiji; Kumita, Shin-Ichiro

2017-06-01

To investigate haemodynamic changes in hepatocellular carcinoma (HCC) and liver under hepatic artery occlusion. Thirty-eight HCC nodules in 25 patients were included. Computed tomography (CT) during hepatic arteriography (CTHA) with and without balloon occlusion of the hepatic artery was performed. CT attenuation and enhancement volume of HCC and liver with and without balloon occlusion were measured on CTHA. Influence of balloon position (segmental or subsegmental branch) was evaluated based on differences in HCC-to-liver attenuation ratio (H/L ratio) and enhancement volume of HCC and liver. In the segmental group (n = 20), H/L ratio and enhancement volume of HCC and liver were significantly lower with balloon occlusion than without balloon occlusion. However, in the subsegmental group (n = 18), H/L ratio was significantly higher and liver enhancement volume was significantly lower with balloon occlusion; HCC enhancement volume was similar with and without balloon occlusion. Rate of change in H/L ratio and enhancement volume of HCC and liver were lower in the segmental group than in the subsegmental group. There were significantly more perfusion defects in HCC in the segmental group. Hepatic artery occlusion causes haemodynamic changes in HCC and liver, especially with segmental occlusion. • Hepatic artery occlusion causes haemodynamic changes in hepatocellular carcinoma and liver. • Segmental occlusion decreased rate of change in hepatocellular carcinoma-to-liver attenuation ratio. • Subsegmental occlusion increased rate of change in hepatocellular carcinoma-to-liver attenuation ratio. • Hepatic artery occlusion decreased enhancement volume of hepatocellular carcinoma and liver. • Hepatic artery occlusion causes perfusion defects in hepatocellular carcinoma.

3D deeply supervised network for automated segmentation of volumetric medical images.

PubMed

Dou, Qi; Yu, Lequan; Chen, Hao; Jin, Yueming; Yang, Xin; Qin, Jing; Heng, Pheng-Ann

2017-10-01

While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our proposed 3D DSN. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated retinal vessel type classification in color fundus images

NASA Astrophysics Data System (ADS)

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

2013-02-01

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

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.

Automatic identification of vessel crossovers in retinal images

NASA Astrophysics Data System (ADS)

Sánchez, L.; Barreira, N.; Penedo, M. G.; Cancela, B.

2015-02-01

Crossovers and bifurcations are interest points of the retinal vascular tree useful to diagnose diseases. Specifically, detecting these interest points and identifying which of them are crossings will give us the opportunity to search for arteriovenous nicking, this is, an alteration of the vessel tree where an artery is crossed by a vein and the former compresses the later. These formations are a clear indicative of hypertension, among other medical problems. There are several studies that have attempted to define an accurate and reliable method to detect and classify these relevant points. In this article, we propose a new method to identify crossovers. Our approach is based on segmenting the vascular tree and analyzing the surrounding area of each interest point. The minimal path between vessel points in this area is computed in order to identify the connected vessel segments and, as a result, to distinguish between bifurcations and crossovers. Our method was tested using retinographies from public databases DRIVE and VICAVR, obtaining an accuracy of 90%.

Cerebrovascular plaque segmentation using object class uncertainty snake in MR images

NASA Astrophysics Data System (ADS)

Das, Bipul; Saha, Punam K.; Wolf, Ronald; Song, Hee Kwon; Wright, Alexander C.; Wehrli, Felix W.

2005-04-01

Atherosclerotic cerebrovascular disease leads to formation of lipid-laden plaques that can form emboli when ruptured causing blockage to cerebral vessels. The clinical manifestation of this event sequence is stroke; a leading cause of disability and death. In vivo MR imaging provides detailed image of vascular architecture for the carotid artery making it suitable for analysis of morphological features. Assessing the status of carotid arteries that supplies blood to the brain is of primary interest to such investigations. Reproducible quantification of carotid artery dimensions in MR images is essential for plaque analysis. Manual segmentation being the only method presently makes it time consuming and sensitive to inter and intra observer variability. This paper presents a deformable model for lumen and vessel wall segmentation of carotid artery from MR images. The major challenges of carotid artery segmentation are (a) low signal-to-noise ratio, (b) background intensity inhomogeneity and (c) indistinct inner and/or outer vessel wall. We propose a new, effective object-class uncertainty based deformable model with additional features tailored toward this specific application. Object-class uncertainty optimally utilizes MR intensity characteristics of various anatomic entities that enable the snake to avert leakage through fuzzy boundaries. To strengthen the deformable model for this application, some other properties are attributed to it in the form of (1) fully arc-based deformation using a Gaussian model to maximally exploit vessel wall smoothness, (2) construction of a forbidden region for outer-wall segmentation to reduce interferences by prominent lumen features and (3) arc-based landmark for efficient user interaction. The algorithm has been tested upon T1- and PD- weighted images. Measures of lumen area and vessel wall area are computed from segmented data of 10 patient MR images and their accuracy and reproducibility are examined. These results correspond

Random forest classification of large volume structures for visuo-haptic rendering in CT images

NASA Astrophysics Data System (ADS)

Mastmeyer, Andre; Fortmeier, Dirk; Handels, Heinz

2016-03-01

For patient-specific voxel-based visuo-haptic rendering of CT scans of the liver area, the fully automatic segmentation of large volume structures such as skin, soft tissue, lungs and intestine (risk structures) is important. Using a machine learning based approach, several existing segmentations from 10 segmented gold-standard patients are learned by random decision forests individually and collectively. The core of this paper is feature selection and the application of the learned classifiers to a new patient data set. In a leave-some-out cross-validation, the obtained full volume segmentations are compared to the gold-standard segmentations of the untrained patients. The proposed classifiers use a multi-dimensional feature space to estimate the hidden truth, instead of relying on clinical standard threshold and connectivity based methods. The result of our efficient whole-body section classification are multi-label maps with the considered tissues. For visuo-haptic simulation, other small volume structures would have to be segmented additionally. We also take a look into these structures (liver vessels). For an experimental leave-some-out study consisting of 10 patients, the proposed method performs much more efficiently compared to state of the art methods. In two variants of leave-some-out experiments we obtain best mean DICE ratios of 0.79, 0.97, 0.63 and 0.83 for skin, soft tissue, hard bone and risk structures. Liver structures are segmented with DICE 0.93 for the liver, 0.43 for blood vessels and 0.39 for bile vessels.

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.

Estimation of retinal vessel caliber using model fitting and random forests

NASA Astrophysics Data System (ADS)

Araújo, Teresa; Mendonça, Ana Maria; Campilho, Aurélio

2017-03-01

Retinal vessel caliber changes are associated with several major diseases, such as diabetes and hypertension. These caliber changes can be evaluated using eye fundus images. However, the clinical assessment is tiresome and prone to errors, motivating the development of automatic methods. An automatic method based on vessel crosssection intensity profile model fitting for the estimation of vessel caliber in retinal images is herein proposed. First, vessels are segmented from the image, vessel centerlines are detected and individual segments are extracted and smoothed. Intensity profiles are extracted perpendicularly to the vessel, and the profile lengths are determined. Then, model fitting is applied to the smoothed profiles. A novel parametric model (DoG-L7) is used, consisting on a Difference-of-Gaussians multiplied by a line which is able to describe profile asymmetry. Finally, the parameters of the best-fit model are used for determining the vessel width through regression using ensembles of bagged regression trees with random sampling of the predictors (random forests). The method is evaluated on the REVIEW public dataset. A precision close to the observers is achieved, outperforming other state-of-the-art methods. The method is robust and reliable for width estimation in images with pathologies and artifacts, with performance independent of the range of diameters.

Excluded segmental duct bile leakage: the case for bilio-enteric anastomosis.

PubMed

Patrono, Damiano; Tandoi, Francesco; Romagnoli, Renato; Salizzoni, Mauro

2014-06-01

Excluded segmental duct bile leak is the rarest type of post-hepatectomy bile leak and presents unique diagnostic and management features. Classical management strategies invariably entail a significant loss of functioning hepatic parenchyma. The aim of this study is to report a new liver-sparing technique to handle excluded segmental duct bile leakage. Two cases of excluded segmental duct bile leak occurring after major hepatic resection were managed by a Roux-en-Y hepatico-jejunostomy on the excluded segmental duct, avoiding the sacrifice of the liver parenchyma origin of the fistula. In both cases, classical management strategies would have led to the functional loss of roughly 50 % of the liver remnant. Diagnostic and management implications are thoroughly discussed. Both cases had an uneventful postoperative course. The timing of repair was associated with a different outcome: the patient who underwent surgical repair in the acute phase developed no long-term complications, whereas the patient who underwent delayed repair developed a late stenosis requiring percutaneous dilatation. Roux-en-Y hepatico-jejunostomy on the excluded bile duct is a valuable technique in selected cases of excluded segmental duct bile leakage.

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

Automated segmentation of geographic atrophy using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Hu, Zhihong; Wang, Ziyuan; Sadda, SriniVas R.

2018-02-01

Geographic atrophy (GA) is an end-stage manifestation of the advanced age-related macular degeneration (AMD), the leading cause of blindness and visual impairment in developed nations. Techniques to rapidly and precisely detect and quantify GA would appear to be of critical importance in advancing the understanding of its pathogenesis. In this study, we develop an automated supervised classification system using deep convolutional neural networks (CNNs) for segmenting GA in fundus autofluorescene (FAF) images. More specifically, to enhance the contrast of GA relative to the background, we apply the contrast limited adaptive histogram equalization. Blood vessels may cause GA segmentation errors due to similar intensity level to GA. A tensor-voting technique is performed to identify the blood vessels and a vessel inpainting technique is applied to suppress the GA segmentation errors due to the blood vessels. To handle the large variation of GA lesion sizes, three deep CNNs with three varying sized input image patches are applied. Fifty randomly chosen FAF images are obtained from fifty subjects with GA. The algorithm-defined GA regions are compared with manual delineation by a certified grader. A two-fold cross-validation is applied to evaluate the algorithm performance. The mean segmentation accuracy, true positive rate (i.e. sensitivity), true negative rate (i.e. specificity), positive predictive value, false discovery rate, and overlap ratio, between the algorithm- and manually-defined GA regions are 0.97 +/- 0.02, 0.89 +/- 0.08, 0.98 +/- 0.02, 0.87 +/- 0.12, 0.13 +/- 0.12, and 0.79 +/- 0.12 respectively, demonstrating a high level of agreement.

Coronary artery segmentation in X-ray angiograms using gabor filters and differential evolution.

PubMed

Cervantes-Sanchez, Fernando; Cruz-Aceves, Ivan; Hernandez-Aguirre, Arturo; Solorio-Meza, Sergio; Cordova-Fraga, Teodoro; Aviña-Cervantes, Juan Gabriel

2018-08-01

Segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis, since it can help cardiologists in diagnosing and monitoring vascular abnormalities. Due to the main disadvantages of the X-ray angiograms are the nonuniform illumination, and the weak contrast between blood vessels and image background, different vessel enhancement methods have been introduced. In this paper, a novel method for blood vessel enhancement based on Gabor filters tuned using the optimization strategy of Differential evolution (DE) is proposed. Because the Gabor filters are governed by three different parameters, the optimal selection of those parameters is highly desirable in order to maximize the vessel detection rate while reducing the computational cost of the training stage. To obtain the optimal set of parameters for the Gabor filters, the area (Az) under the receiver operating characteristics curve is used as objective function. In the experimental results, the proposed method achieves an A z =0.9388 in a training set of 40 images, and for a test set of 40 images it obtains the highest performance with an A z =0.9538 compared with six state-of-the-art vessel detection methods. Finally, the proposed method achieves an accuracy of 0.9423 for vessel segmentation using the test set. In addition, the experimental results have also shown that the proposed method can be highly suitable for clinical decision support in terms of computational time and vessel segmentation performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Gallbladder volvulus with segmental right liver lobe hypoplasia/atrophy: a preoperative diagnostic dilemma.

PubMed

Musthafa, Shameel; Aftab, Zia; Ali, Syed Muhammad; Khanna, Maneesh

2018-06-08

Gallbladder volvulus (GBV) due to rotation of the gall bladder (GB) around its own mesentery is a rare surgical emergency and often identified intraoperatively. Typically, cholecystitis is the initial clinical diagnosis, but a high index of suspicion on imaging can alert the physician for the possibility of GBV requiring urgent surgical intervention. We describe a case of a young female patient with hypoplasia/atrophy of the posterior segment of the right liver lobe and a GB with no hepatic attachments but only mesenteric pedicle. She presented with first episode of sudden-onset, severe right subcostal pain. The ultrasonogram and magnetic resonance cholangiopancreatogram findings were suggestive of GBV. She underwent laparoscopic exploration that confirmed GBV of a free-floating GB with a thrombosed cystic artery. The GB was detorted, and cholecystectomy was performed. She had an uneventful postoperative course and was discharged with no complications. Histopathological examination showed intramural haematoma of the GB with wall necrosis. © BMJ Publishing Group Ltd (unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Interactive surface correction for 3D shape based segmentation

NASA Astrophysics Data System (ADS)

Schwarz, Tobias; Heimann, Tobias; Tetzlaff, Ralf; Rau, Anne-Mareike; Wolf, Ivo; Meinzer, Hans-Peter

2008-03-01

Statistical shape models have become a fast and robust method for segmentation of anatomical structures in medical image volumes. In clinical practice, however, pathological cases and image artifacts can lead to local deviations of the detected contour from the true object boundary. These deviations have to be corrected manually. We present an intuitively applicable solution for surface interaction based on Gaussian deformation kernels. The method is evaluated by two radiological experts on segmentations of the liver in contrast-enhanced CT images and of the left heart ventricle (LV) in MRI data. For both applications, five datasets are segmented automatically using deformable shape models, and the resulting surfaces are corrected manually. The interactive correction step improves the average surface distance against ground truth from 2.43mm to 2.17mm for the liver, and from 2.71mm to 1.34mm for the LV. We expect this method to raise the acceptance of automatic segmentation methods in clinical application.

Fully Convolutional Neural Networks Improve Abdominal Organ Segmentation.

PubMed

Bobo, Meg F; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G; Hilmes, Melissa A; Landman, Bennett A

2018-03-01

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities.

Fully convolutional neural networks improve abdominal organ segmentation

NASA Astrophysics Data System (ADS)

Bobo, Meg F.; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J.; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G.; Hilmes, Melissa A.; Landman, Bennett A.

2018-03-01

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1

Development of a new extracorporeal whole-liver perfusion system.

PubMed

Naruse, Katsutoshi; Sakai, Yasuyuki; Guo, Lei; Natori, Takeshi; Shindoh, Junichi; Karasawa, Yasuaki; Iida, Yuhki; Kojima, Kentaro; Michishita, Kazuya; Makuuchi, Masatoshi

2003-01-01

We have developed a new extracorporeal whole-liver accommodation device in which a whole swine liver is placed in a physiological state by modeling the intraabdominal arrangement in the pig body, with the liver supported by a special inferior vena cava tube. Furthermore, we employed a diaphragm-type artificial heart in our system to produce pulsatile blood flow through the hepatic artery, which is considered to be indispensable to dilate peripheral vessels and supply oxygenated whole blood to the peripheral liver tissue. Beneficial effects were demonstrated in visual findings and bile juice secretion. The color of the liver surface in our system remained bright red, indicating that the liver vessels were well drained and free from congestion, and bile juice secretion was maintained at more than 10 ml/h throughout the perfusion period. Our system exhibited excellent ammonia removal and urea nitrogen synthesis, and serum aspartate aminotransferase levels showed no increase, indicating the absence of hepatocyte destruction. Histological findings showed that the liver could expand appropriately and was free from compression caused by its own weight. In conclusion, our original liver accommodation device enabled appropriate expansion of the whole liver and supplied adequate oxygenated blood to peripheral areas by means of a pulsatile pump.

Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms

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

Zhou, Chuan, E-mail: chuan@umich.edu; Chan, Heang-Ping; Chughtai, Aamer

2014-08-15

Purpose: The authors are developing a computer-aided detection system to assist radiologists in analysis of coronary artery disease in coronary CT angiograms (cCTA). This study evaluated the accuracy of the authors’ coronary artery segmentation and tracking method which are the essential steps to define the search space for the detection of atherosclerotic plaques. Methods: The heart region in cCTA is segmented and the vascular structures are enhanced using the authors’ multiscale coronary artery response (MSCAR) method that performed 3D multiscale filtering and analysis of the eigenvalues of Hessian matrices. Starting from seed points at the origins of the left andmore » right coronary arteries, a 3D rolling balloon region growing (RBG) method that adapts to the local vessel size segmented and tracked each of the coronary arteries and identifies the branches along the tracked vessels. The branches are queued and subsequently tracked until the queue is exhausted. With Institutional Review Board approval, 62 cCTA were collected retrospectively from the authors’ patient files. Three experienced cardiothoracic radiologists manually tracked and marked center points of the coronary arteries as reference standard following the 17-segment model that includes clinically significant coronary arteries. Two radiologists visually examined the computer-segmented vessels and marked the mistakenly tracked veins and noisy structures as false positives (FPs). For the 62 cases, the radiologists marked a total of 10191 center points on 865 visible coronary artery segments. Results: The computer-segmented vessels overlapped with 83.6% (8520/10191) of the center points. Relative to the 865 radiologist-marked segments, the sensitivity reached 91.9% (795/865) if a true positive is defined as a computer-segmented vessel that overlapped with at least 10% of the reference center points marked on the segment. When the overlap threshold is increased to 50% and 100%, the sensitivities

Focal confluent fibrosis in cirrhotic liver: natural history studied with serial CT.

PubMed

Brancatelli, Giuseppe; Baron, Richard L; Federle, Michael P; Sparacia, Gianvincenzo; Pealer, Karen

2009-05-01

The objective of this study was to assess the long-term natural history of focal confluent fibrosis in cirrhotic liver with CT. Two radiologists retrospectively reviewed in consensus 118 liver CT examinations in 26 patients (19 men, seven women; age range, 32-68 years; mean age, 50 years) performed over approximately 6 years. Helical CT scans were obtained before and 30-35 and 65-70 seconds after injection of 125-150 mL of contrast medium at a rate of 4-5 mL/s. Proof of cirrhosis was based on liver transplantation (n = 6), biopsy (n = 9), or imaging findings (n = 11). The number, location, and attenuation of fibrotic lesions and presence of trapped vessels were evaluated. Variation of hepatic retraction associated with the development of focal confluent fibrosis lesions was assessed using the ellipsoid volume formula and an arbitrary retraction index. Each radiologist identified 41 focal confluent fibrosis lesions. All lesions were identified by both radiologists. Twelve patients (46%) had a single lesion, 13 (50%) had two lesions, and one (4%) had three lesions. Thirty-four (83%) of 41 lesions were located in segment IV, VII, or VIII. Thirty-two lesions (78%) were hypoattenuating on unenhanced images, 25 lesions (61%) were hypoattenuating on hepatic arterial phase images, and 20 lesions (49%) were isoattenuating on portal venous phase images. Seven lesions (17%) were or became hyperattenuating at follow-up on portal venous phase images. Trapped vessels were found in six lesions (15%). The retraction index showed a significant increase over time (r = 0.423, p < or = 0.0001). The degree of capsule retraction associated with focal confluent fibrosis evolves with time and relates to the natural evolution of cirrhosis.

A method for smoothing segmented lung boundary in chest CT images

NASA Astrophysics Data System (ADS)

Yim, Yeny; Hong, Helen

2007-03-01

To segment low density lung regions in chest CT images, most of methods use the difference in gray-level value of pixels. However, radiodense pulmonary vessels and pleural nodules that contact with the surrounding anatomy are often excluded from the segmentation result. To smooth lung boundary segmented by gray-level processing in chest CT images, we propose a new method using scan line search. Our method consists of three main steps. First, lung boundary is extracted by our automatic segmentation method. Second, segmented lung contour is smoothed in each axial CT slice. We propose a scan line search to track the points on lung contour and find rapidly changing curvature efficiently. Finally, to provide consistent appearance between lung contours in adjacent axial slices, 2D closing in coronal plane is applied within pre-defined subvolume. Our method has been applied for performance evaluation with the aspects of visual inspection, accuracy and processing time. The results of our method show that the smoothness of lung contour was considerably increased by compensating for pulmonary vessels and pleural nodules.

Vessel co-option in primary human tumors and metastases: an obstacle to effective anti-angiogenic treatment?

PubMed

Donnem, Tom; Hu, Jiangting; Ferguson, Mary; Adighibe, Omanma; Snell, Cameron; Harris, Adrian L; Gatter, Kevin C; Pezzella, Francesco

2013-08-01

Angiogenesis has been regarded as essential for tumor growth and progression. Studies of many human tumors, however, suggest that their microcirculation may be provided by nonsprouting vessels and that a variety of tumors can grow and metastasize without angiogenesis. Vessel co-option, where tumor cells migrate along the preexisting vessels of the host organ, is regarded as an alternative tumor blood supply. Vessel co-option may occur in many malignancies, but so far mostly reported in highly vascularized tissues such as brain, lung, and liver. In primary and metastatic lung cancer and liver metastasis from different primary origins, as much as 10-30% of the tumors are reported to use this alternative blood supply. In addition, vessel co-option is introduced as a potential explanation of antiangiogenic drug resistance, although the impact of vessel co-option in this clinical setting is still to be further explored. In this review we discuss tumor vessel co-option with specific examples of vessel co-option in primary and secondary tumors and a consideration of the clinical implications of this alternative tumor blood supply.

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.

Vulnerability of Xylem Vessels to Cavitation in Sugar Maple. Scaling from Individual Vessels to Whole Branches1

PubMed Central

Melcher, Peter J.; Zwieniecki, Maciej A.; Holbrook, N. Michele

2003-01-01

The relation between xylem vessel age and vulnerability to cavitation of sugar maple (Acer saccharum Marsh.) was quantified by measuring the pressure required to force air across bordered pit membranes separating individual xylem vessels. We found that the bordered pit membranes of vessels located in current year xylem could withstand greater applied gas pressures (3.8 MPa) compared with bordered pit membranes in vessels located in older annular rings (2.0 MPa). A longitudinal transect along 6-year-old branches indicated that the pressure required to push gas across bordered pit membranes of current year xylem did not vary with distance from the growing tip. To understand the contribution of age-related changes in vulnerability to the overall resistance to cavitation, we combined data on the pressure thresholds of individual xylem vessels with measurements of the relative flow rate through each annual ring. The annual ring of the current year contributed only 16% of the total flow measured on 10-cm-long segments cut from 6-year-old branches, but it contributed more than 70% of the total flow when measured through 6-year-old branches to the point of leaf attachment. The vulnerability curve calculated using relative flow rates measured on branch segments were similar to vulnerability curves measured on 6-year-old branches (pressure that reduces hydraulic conductance by 50% = 1.6–2.4 MPa), whereas the vulnerability curve calculated using relative flow rates measured on 6-year-old branches were similar to ones measured on the extension growth of the current year (pressure that reduces hydraulic conductance by 50% = 3.8 MPa). These data suggest that, in sugar maple, the xylem of the current year can withstand larger xylem tensions than older wood and dominates water delivery to leaves. PMID:12692336

Preoperative Portal Vein Embolization Tailored to Prepare the Liver for Complex Resections: Initial Experience

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

Baere, T. de, E-mail: debaere@igr.fr; Robinson, J. M.; Deschamps, F.

The purpose of this study was to evaluate the safety and efficacy of preoperative portal vein embolization (PVE) tailored to prepare the liver for complex and extended resections. During the past 5 years, 12 PVEs were performed in noncirrhotic patients with liver metastases from colon cancer (n = 10), choroidal melanoma (n = 1), and leiomyosarcoma (n = 1) to prepare complex anatomical liver resections in patients with small future remnant livers. These liver resections planned to preserve only segment IV in four patients, segments IV, V, and VIII in four patients, segments II, III, VI, and VII in threemore » patients, and segments V and VI in one patient. PVE was performed under general anesthesia with a flow-guided injection of a mixture of cyanoacrylate and Lipiodol using a 5-Fr catheter. All portal branches feeding the liver segments to be resected were successfully embolized with cyanoacrylate except one, which was occluded with coils due to the risk of reflux with cyanoacrylate. After a mean of 32 days, CT volumetry revealed a mean hypertrophy of the unembolized liver of 47 {+-} 25% (range, 21-88%). Liver resections could be performed in 10 patients but were canceled in 2, due to the occurrence of a new hepatic tumor in one and an insufficiently increased volume in the other. Among the 10 patients who underwent the liver resection, 1 died of postoperative sepsis, 3 died 3 to 32 months after surgery, including 1 death unrelated to cancer, and 6 were alive after 6 to 36 months after surgery. In conclusion, in this preliminary report, PVE appears to be feasible and able to induce hypertrophy of the future remnant liver before a complex and extended hepatectomy. Further evaluation is needed in a larger cohort.« less

Using on-site liver 3-D reconstruction and volumetric calculations in split liver transplantation.

PubMed

Reichman, Trevor W; Fiorello, Brittany; Carmody, Ian; Bohorquez, Humberto; Cohen, Ari; Seal, John; Bruce, David; Loss, George E

2016-12-01

Split liver transplantation increases the number of grafts available for transplantation. Pre-recovery assessment of liver graft volume is essential for selecting suitable recipients. The purpose of this study was to determine the ability and feasibility of constructing a 3-D model to aid in surgical planning and to predict graft weight prior to an in situ division of the donor liver. Over 11 months, 3-D volumetric reconstruction of 4 deceased donors was performed using Pathfinder Scout© liver volumetric software. Demographic, laboratory, operative, perioperative and survival data for these patients along with donor demographic data were collected prospectively and analyzed retrospectively. The average predicted weight of the grafts from the adult donors obtained from an in situ split procedure were 1130 g (930-1458 g) for the extended right lobe donors and 312 g (222-396 g) for left lateral segment grafts. Actual adult graft weight was 92% of the predicted weight for both the extended right grafts and the left lateral segment grafts. The predicted and actual graft weights for the pediatric donors were 176 g and 210 g for the left lateral segment grafts and 308 g and 280 g for the extended right lobe grafts, respectively. All grafts were transplanted except for the right lobe from the pediatric donors due to the small graft weight. On-site volumetric assessment of donors provides useful information for the planning of an in situ split and for selection of recipients. This information may expand the donor pool to recipients previously felt to be unsuitable due to donor and/or recipient weight.

Particle filters, a quasi-Monte-Carlo-solution for segmentation of coronaries.

PubMed

Florin, Charles; Paragios, Nikos; Williams, Jim

2005-01-01

In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (Particle Filter). In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.

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.

An approach to localize the retinal blood vessels using bit planes and centerline detection.

PubMed

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

2012-11-01

The change in morphology, diameter, branching pattern or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports an automated method for segmentation of blood vessels in retinal images. A unique combination of techniques for vessel centerlines detection and morphological bit plane slicing is presented to extract the blood vessel tree from the retinal images. The centerlines are extracted by using the first order derivative of a Gaussian filter in four orientations and then evaluation of derivative signs and average derivative values is performed. Mathematical morphology has emerged as a proficient technique for quantifying the blood vessels in the retina. The shape and orientation map of blood vessels is obtained by applying a multidirectional morphological top-hat operator with a linear structuring element followed by bit plane slicing of the vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The methodology is tested on three publicly available databases DRIVE, STARE and MESSIDOR. The results demonstrate that the performance of the proposed algorithm is comparable with state of the art techniques in terms of accuracy, sensitivity and specificity. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

[The clinical study of percutaneous transhepatic radiofrequency ablation combined with tumor edge of percutaneous absolute ethanol injection on liver cancer adjacent to major blood vessels].

PubMed

Du, Jun-dong; Liu, Rong; Jiao, Hua-bo; Xiang, De-dong; Yin, Hui-nan; Li, Zhen-cai; Li, Tao; Zhu, Zi-man; Li, Zhan-liang

2011-05-01

To explore the effects of percutaneous transhepatic radiofrequency ablation (PRFA) combined with tumor edge of percutaneous absolute ethanol injection (PEI) on liver cancer adjacent to major blood vessels. Seventy five patients with liver cancer adjacent to major blood vessels were randomly divided into two groups: PRFA+PEI therapy group (38 cases) and PRFA control group (37 cases). Tumor necrosis rate, AFP levels, local recurrence rate, median for survival time and cum survival were used as the evaluation index to evaluate the efficacies of the two methods. Tumor necrosis rates of the therapy group and the control group were 84.2% and 54.1% (P < 0.01), respectively; AFP levels of therapy group and control group at 1, 3, 6 and 12 months after treatment were (105.0 ± 35.5) μg/L, (28.4 ± 4.3) μg/L, (58.6 ± 6.7) μg/L, (89.5 ± 12.5) μg/L and (137.2 ± 34.6) μg/L, (84.2 ± 18.4) μg/L, (106.6 ± 20.3) μg/L, (173.7 ± 32.0) μg/L, respectively. The rates of therapy group was significantly lower than of control group. Local recurrence rates of the therapy group and control group were 2.6%, 7.9%, 13.2% and 31.6% vs 10.8%, 21.6% , 40.5% and 62.1% (P < 0.05) at 3, 6, 12 and 24 months after treatment, respectively. Median for survival time of the therapy group and control group were 28.0 ± 2.8 months and 19.0 ± 3.6 months, respectively. Cum survival of the therapy group and control group were 84.2%, 78.9%, 60.5% and 31.6% vs 78.4%, 67.6%, 37.8% and 8.1% (P < 0.05) at 6, 12, 24 and 36 months after treatment, respectively. PEI as a supplementary treatment of PRFA can effectively improve the treatment of liver cancer adjacent to major blood vessels and significantly reduce the local recurrence rate and improve long-term survival rates.

Quantification of choroidal neovascularization vessel length using optical coherence tomography angiography

NASA Astrophysics Data System (ADS)

Gao, Simon S.; Liu, Li; Bailey, Steven T.; Flaxel, Christina J.; Huang, David; Li, Dengwang; Jia, Yali

2016-07-01

Quantification of choroidal neovascularization (CNV) as visualized by optical coherence tomography angiography (OCTA) may have importance clinically when diagnosing or tracking disease. Here, we present an automated algorithm to quantify the vessel skeleton of CNV as vessel length. Initial segmentation of the CNV on en face angiograms was achieved using saliency-based detection and thresholding. A level set method was then used to refine vessel edges. Finally, a skeleton algorithm was applied to identify vessel centerlines. The algorithm was tested on nine OCTA scans from participants with CNV and comparisons of the algorithm's output to manual delineation showed good agreement.

Laparoscopic surgery of postero-lateral segments: a comparison between transthoracic and abdominal approach.

PubMed

Fuks, David; Gayet, Brice

2015-06-01

Lesions located in the postero-lateral part of the liver (segments 6 and 7) have been considered as poor candidates for a laparoscopic liver resection due to the limited visualization and difficulty in bleeding control. Although no comparison has been done between transthoracic and abdominal resection of tumors located in the postero-lateral segments, we propose a description of these different strategies, specifying the benefits as well as the disadvantages of the various approaches.

Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation.

PubMed

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

2010-03-01

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 x 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.

Cartwheel projections of segmented pulmonary vasculature for the detection of pulmonary embolism

NASA Astrophysics Data System (ADS)

Kiraly, Atilla P.; Naidich, David P.; Novak, Carol L.

2005-04-01

Pulmonary embolism (PE) detection via contrast-enhanced computed tomography (CT) images is an increasingly important topic of research. Accurate identification of PE is of critical importance in determining the need for further treatment. However, current multi-slice CT scanners provide datasets typically containing 600 or more images per patient, making it desirable to have a visualization method to help radiologists focus directly on potential candidates that might otherwise have been overlooked. This is especially important when assessing the ability of CT to identify smaller, sub-segmental emboli. We propose a cartwheel projection approach to PE visualization that computes slab projections of the original data aided by vessel segmentation. Previous research on slab visualization for PE has utilized the entire volumetric dataset, requiring thin slabs and necessitating the use of maximum intensity projection (MIP). Our use of segmentation within the projection computation allows the use of thicker slabs than previous methods, as well as the ability to employ visualization variations that are only possible with segmentation. Following automatic segmentation of the pulmonary vessels, slabs may be rotated around the X-, Y- or Z-axis. These slabs are rendered by preferentially using voxels within the lung vessels. This effectively eliminates distracting information not relevant to diagnosis, lessening both the chance of overlooking a subtle embolus and minimizing time on spent evaluating false positives. The ability to employ thicker slabs means fewer images need to be evaluated, yielding a more efficient workflow.

A Multi-Anatomical Retinal Structure Segmentation System for Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

PubMed Central

Elleithy, Khaled; Elleithy, Abdelrahman

2018-01-01

Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue for detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc, and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This paper proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc, and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogeneous anatomical structures. PMID:29888146

Medical image segmentation using 3D MRI data

NASA Astrophysics Data System (ADS)

Voronin, V.; Marchuk, V.; Semenishchev, E.; Cen, Yigang; Agaian, S.

2017-05-01

Precise segmentation of three-dimensional (3D) magnetic resonance imaging (MRI) image can be a very useful computer aided diagnosis (CAD) tool in clinical routines. Accurate automatic extraction a 3D component from images obtained by magnetic resonance imaging (MRI) is a challenging segmentation problem due to the small size objects of interest (e.g., blood vessels, bones) in each 2D MRA slice and complex surrounding anatomical structures. Our objective is to develop a specific segmentation scheme for accurately extracting parts of bones from MRI images. In this paper, we use a segmentation algorithm to extract the parts of bones from Magnetic Resonance Imaging (MRI) data sets based on modified active contour method. As a result, the proposed method demonstrates good accuracy in a comparison between the existing segmentation approaches on real MRI data.

Low QRS Voltage on Presenting Electrocardiogram Predicts Multi-vessel Disease in Anterior ST-segment Elevation Myocardial Infarction.

PubMed

Kobayashi, Akihiro; Misumida, Naoki; Aoi, Shunsuke; Kanei, Yumiko

Low QRS voltage was reported to predict adverse outcomes in acute myocardial infarction in the pre-thrombolytic era. However, the association between low voltage and angiographic findings has not been fully addressed. We performed a retrospective analysis of patients with anterior ST-segment elevation myocardial infarction (STEMI). Low QRS voltage was defined as either peak to peak QRS complex voltage <1.0mV in all precordial leads or <0.5mV in all limb leads. Among 190 patients, 37 patients (19%) had low voltage. Patients with low voltage had a higher rate of multi-vessel disease (MVD) (76% vs. 52%, p=0.01). Patients with low voltage were more likely to undergo coronary artery bypass grafting (CABG) during admission (11% vs. 2%, p=0.028). Low voltage was an independent predictor for MVD (OR 2.50; 95% CI 1.12 to 6.03; p=0.032). Low QRS voltage was associated with MVD and in-hospital CABG in anterior STEMI. Copyright © 2017 Elsevier Inc. All rights reserved.

Living Donor Liver Transplantation Using a Liver Graft With Congenital Intrahepatic Portosystemic Shunt

PubMed Central

Kamei, Hideya; Imai, Hisashi; Onishi, Yasuharu; Sugimoto, Hiroyuki; Suzuki, Kojiro; Ogura, Yasuhiro

2016-01-01

Background Despite of recent development of imaging modalities, congenital intrahepatic portosystemic shunt (IPSS) is rarely diagnosed. Therefore, living donor liver transplantation using a liver graft with IPSS has not been previously published. Materials and Methods We report a 28-year-old male patient with end-stage liver disease secondary to Wilson disease. His 26-year-old brother was a potential living donor, who had an IPSS of 25 mm in diameter at segment 6 as shown by computed tomography. Liver function tests were normal, and blood ammonia concentration was in the upper limit of normal. Results Living donor liver transplantation was uneventfully performed. After surgery, a recipient liver function tests showed a quick recovery, and serum ammonia levels were consistently normal. Although thrombosis inside the IPSS was confirmed by computed tomography on postoperative day 21, this thrombosis disappeared at 3 months posttransplant with anticoagulants. Currently (12 months posttransplant), the patient has fully recovered, and the IPSS is still the same size. Conclusions Based on our experience, liver allografts with IPSS can be accepted as potential liver allografts. PMID:27500240

Feasibility of Using the Marginal Blood Vessels as Reference Landmarks for CT Colonography

PubMed Central

Wei, Zhuoshi; Yao, Jianhua; Wang, Shijun; Liu, Jiamin; Dwyer, Andrew J.; Pickhardt, Perry J.; Nowinski, Wieslaw L.; Summers, Ronald M.

2015-01-01

OBJECTIVE The purpose of this study was to show the spatial relationship of the colonic marginal blood vessels and the teniae coli on CT colonography (CTC) and the use of the marginal blood vessels for supine-prone registration of polyps and for determination of proper connectivity of collapsed colonic segments. MATERIALS AND METHODS We manually labeled the marginal blood vessels on 15 CTC examinations. Colon segmentation, centerline extraction, teniae detection, and teniae identification were automatically performed. For assessment of their spatial relationships, the distances from the marginal blood vessels to the three teniae coli and to the colon were measured. Student t tests (paired, two-tailed) were performed to evaluate the differences among these distances. To evaluate the reliability of the marginal vessels as reference points for polyp correlation, we analyzed 20 polyps from 20 additional patients who underwent supine and prone CTC. The average difference of the circumferential polyp position on the supine and prone scans was computed. Student t tests (paired, two-tailed) were performed to evaluate the supine-prone differences of the distance. We performed a study on 10 CTC studies from 10 patients with collapsed colonic segments by manually tracing the marginal blood vessels near the collapsed regions to resolve the ambiguity of the colon path. RESULTS The average distances (± SD) from the marginal blood vessels to the tenia mesocolica, tenia omentalis, and tenia libera were 20.1 ± 3.1 mm (95% CI, 18.5–21.6 mm), 39.5 ± 4.8 mm (37.1–42.0 mm), and 36.9 ± 4.2 mm (34.8–39.1 mm), respectively. Pairwise comparison showed that these distances to the tenia libera and tenia omentalis were significantly different from the distance to the tenia mesocolica (p < 0.001). The average distance from the marginal blood vessels to the colon wall was 15.3 ± 2.0 mm (14.2–16.3 mm). For polyp localization, the average difference of the circumferential polyp

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Yong, Yan Ling; Tan, Li Kuo; McLaughlin, Robert A.; Chee, Kok Han; Liew, Yih Miin

2017-12-01

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.

Laparoscopic liver resection: when to use the laparoscopic stapler device

PubMed Central

Gumbs, Andrew A.; Gayet, Brice

2008-01-01

Minimally invasive hepatic resection was first described by Gagner et al. in the early 1990s and since then has become increasingly adopted by hepatobiliary and liver transplant surgeons. Several techniques exist to transect the hepatic parenchyma laparoscopically and include transection with stapler and/or energy devices, such as ultrasonic shears, radiofrequency ablation and bipolar devices. We believe that coagulative techniques allow for superior anatomic resections and ultimately permit for the performance of more complex hepatic resections. In the stapling technique, Glisson's capsule is usually incised with an energy device until the parenchyma is thinned out and multiple firings of the staplers are then used to transect the remaining parenchyma and larger bridging segmental vessels and ducts. Besides the economic constraints of using multiple stapler firings, the remaining staples have the disadvantage of hindering and even preventing additional hemostasis of the raw liver surface with monopolar and bipolar electrocautery. The laparoscopic stapler device is, however, useful for transection of the main portal branches and hepatic veins during minimally invasive major hepatic resections. Techniques to safely perform major hepatic resection with the above techniques will be described with an emphasis on when and how laparoscopic vascular staplers should be used. PMID:18773113

Grading vascularity from histopathological images based on traveling salesman distance and vessel size

NASA Astrophysics Data System (ADS)

Niazi, M. Khalid Khan; Hemminger, Jessica; Kurt, Habibe; Lozanski, Gerard; Gurcan, Metin

2014-03-01

Vascularity represents an important element of tissue/tumor microenvironment and is implicated in tumor growth, metastatic potential and resistence to therapy. Small blood vessels can be visualized using immunohistochemical stains specific to vascular cells. However, currently used manual methods to assess vascular density are poorly reproducible and are at best semi quantitative. Computer based quantitative and objective methods to measure microvessel density are urgently needed to better understand and clinically utilize microvascular density information. We propose a new method to quantify vascularity from images of bone marrow biopsies stained for CD34 vascular lining cells protein as a model. The method starts by automatically segmenting the blood vessels by methods of maxlink thresholding and minimum graph cuts. The segmentation is followed by morphological post-processing to reduce blast and small spurious objects from the bone marrow images. To classify the images into one of the four grades, we extracted 20 features from the segmented blood vessel images. These features include first four moments of the distribution of the area of blood vessels, first four moments of the distribution of 1) the edge weights in the minimum spanning tree of the blood vessels, 2) the shortest distance between blood vessels, 3) the homogeneity of the shortest distance (absolute difference in distance between consecutive blood vessels along the shortest path) between blood vessels and 5) blood vessel orientation. The method was tested on 26 bone marrow biopsy images stained with CD34 IHC stain, which were evaluated by three pathologists. The pathologists took part in this study by quantifying blood vessel density using gestalt assessment in hematopoietic bone marrow portions of bone marrow core biopsies images. To determine the intra-reader variability, each image was graded twice by each pathologist with two-week interval in between their readings. For each image, the ground

An automated skin segmentation of Breasts in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

PubMed

Lee, Chia-Yen; Chang, Tzu-Fang; Chang, Nai-Yun; Chang, Yeun-Chung

2018-04-18

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to diagnose breast disease. Obtaining anatomical information from DCE-MRI requires the skin be manually removed so that blood vessels and tumors can be clearly observed by physicians and radiologists; this requires considerable manpower and time. We develop an automated skin segmentation algorithm where the surface skin is removed rapidly and correctly. The rough skin area is segmented by the active contour model, and analyzed in segments according to the continuity of the skin thickness for accuracy. Blood vessels and mammary glands are retained, which remedies the defect of removing some blood vessels in active contours. After three-dimensional imaging, the DCE-MRIs without the skin can be used to see internal anatomical information for clinical applications. The research showed the Dice's coefficients of the 3D reconstructed images using the proposed algorithm and the active contour model for removing skins are 93.2% and 61.4%, respectively. The time performance of segmenting skins automatically is about 165 times faster than manually. The texture information of the tumors position with/without the skin is compared by the paired t-test yielded all p < 0.05, which suggested the proposed algorithm may enhance observability of tumors at the significance level of 0.05.

Gadolinium Enhanced MR Coronary Vessel Wall Imaging at 3.0 Tesla.

PubMed

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

2010-10-11

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

Medical Image Segmentation by Combining Graph Cut and Oriented Active Appearance Models

PubMed Central

Chen, Xinjian; Udupa, Jayaram K.; Bağcı, Ulaş; Zhuge, Ying; Yao, Jianhua

2017-01-01

In this paper, we propose a novel 3D segmentation method based on the effective combination of the active appearance model (AAM), live wire (LW), and graph cut (GC). The proposed method consists of three main parts: model building, initialization, and segmentation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the initialization part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW method, resulting in Oriented AAM (OAAM). A multi-object strategy is utilized to help in object initialization. We employ a pseudo-3D initialization strategy, and segment the organs slice by slice via multi-object OAAM method. For the segmentation part, a 3D shape constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT dataset and also tested on the MICCAI 2007 grand challenge for liver segmentation training dataset. The results show the following: (a) An overall segmentation accuracy of true positive volume fraction (TPVF) > 94.3%, false positive volume fraction (FPVF) < 0.2% can be achieved. (b) The initialization performance can be improved by combining AAM and LW. (c) The multi-object strategy greatly facilitates the initialization. (d) Compared to the traditional 3D AAM method, the pseudo 3D OAAM method achieves comparable performance while running 12 times faster. (e) The performance of proposed method is comparable to the state of the art liver segmentation algorithm. The executable version of 3D shape constrained GC with user interface can be downloaded from website http://xinjianchen.wordpress.com/research/. PMID:22311862

Evaluation of Liver Stiffness After Radioembolization by Real-Time ShearWave™ Elastography: Preliminary Study

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

Bas, Ahmet, E-mail: dr.ahmetbas@hotmail.com; Samanci, Cesur, E-mail: cesursamanci@gmail.com; Gulsen, Fatih, E-mail: drfgulsen@yahoo.com

2015-08-15

PurposeTo evaluate the effect of ShearWave™ elastography (SWE) for the assessment of liver fibrosis after radioembolization (RE) in patients with liver malignancies.Materials and MethodsWe prospectively examined the effects of SWE before and after RE in 17 adult patients, from June 2012 to September 2013. All patients underwent SWE within 1 month before and 3 months (96.3 ± 22.9 days) after RE. Measurements were taken in segments III, IV, V, and VI (lateral/medial left lobe and anterior/posterior right lobe, respectively). Liver stiffness was studied in the 39 treated segments.ResultsThe mean stiffness of liver tissue according to the pre-RE SWE measurements was not different from the post-REmore » SWE measurements in the segments that did not undergo RE. Conversely, segments treated with RE were significantly stiffer according to the post-RE SWE measurements (mean SWE 17.4 kPa) than according to the baseline measurements (7.0 kPa) (p < 0.001). Patients with hepatocellular carcinoma and preexisting infection with hepatitis B and C viruses had higher pre-embolization stiffness, and the post-embolization stiffness of the treated segments in these patients was higher than that in the remainder of the study population.ConclusionThese data suggest that SWE measurements of liver stiffness increase as early as the third month after RE. SWE could be used as a noninvasive complementary imaging method for preliminary assessment of liver fibrosis before and after RE.« less

Liver hanging maneuver for right hemiliver in situ donation--anatomical considerations.

PubMed

Trotovsek, B; Gadzijev, E M; Ravnik, D; Hribernik, M

2006-01-01

An anatomical study was carried out to evaluate the safety of the liver hanging maneuver for the right hemiliver in living donor and in situ splitting transplantation. During this procedure a 4-6 cm blind dissection is performed between the inferior vena cava and the liver. Short subhepatic veins entering the inferior vena cava from segments 1 and 9 could be torn with consequent hemorrhage. One hundred corrosive casts of livers were evaluated to establish the position and diameter of short subhepatic veins and the inferior right hepatic vein. The average distance from the right border of the inferior vena cava to the opening of segment 1 veins was 16.7+/-3.4 mm and to the entrance of segment 9 veins was 5.0+/-0.5 mm. The width of the narrowest point on the route of blind dissection was determined, with the average value being 8.7+/-2.3 mm (range 2-15 mm). The results show that the liver hanging maneuver is a safe procedure. A proposed route of dissection minimizes the risk of disrupting short subhepatic veins (7%).

Heat sink phenomenon of bipolar and monopolar radiofrequency ablation observed using polypropylene tubes for vessel simulation.

PubMed

Al-Alem, Ihssan; Pillai, Krishna; Akhter, Javed; Chua, Terence C; Morris, David L

2014-06-01

Radiofrequency ablation (RFA) is widely used for treating liver tumors; recurrence is common owing to proximity to blood vessels possibly due to the heat sink effect. We seek to investigate this phenomenon using unipolar and bipolar RFA on an egg white tumor tissue model and an animal liver model. Temperature profiles during ablation (with and without vessel simulation) were studied, using both bipolar and unipolar RFA probes by 4 strategically placed temperature leads to monitor temperature profile during ablation. The volume of ablated tissue was also measured. The volume ablated during vessel simulation confirmed the impact of the heat sink phenomenon. The heat sink effect of unipolar RFA was greater compared with bipolar RFA (ratio of volume affected 2:1) in both tissue and liver models. The volume ablated using unipolar RFA was less than the bipolar RFA (ratio of volume ablated = 1:4). Unipolar RFA achieved higher ablation temperatures (122°C vs 98°C). Unipolar RFA resulted in tissue damage beyond the vessel, which was not observed using bipolar RFA. Bipolar RFA ablates a larger tumor volume compared with unipolar RFA, with a single ablation. The impact of heat sink phenomenon in tumor ablation is less so with bipolar than unipolar RFA with sparing of adjacent vessel damage. © The Author(s) 2013.

Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound.

PubMed

Hann, Alexander; Bettac, Lucas; Haenle, Mark M; Graeter, Tilmann; Berger, Andreas W; Dreyhaupt, Jens; Schmalstieg, Dieter; Zoller, Wolfram G; Egger, Jan

2017-10-06

Manual segmentation of hepatic metastases in ultrasound images acquired from patients suffering from pancreatic cancer is common practice. Semiautomatic measurements promising assistance in this process are often assessed using a small number of lesions performed by examiners who already know the algorithm. In this work, we present the application of an algorithm for the segmentation of liver metastases due to pancreatic cancer using a set of 105 different images of metastases. The algorithm and the two examiners had never assessed the images before. The examiners first performed a manual segmentation and, after five weeks, a semiautomatic segmentation using the algorithm. They were satisfied in up to 90% of the cases with the semiautomatic segmentation results. Using the algorithm was significantly faster and resulted in a median Dice similarity score of over 80%. Estimation of the inter-operator variability by using the intra class correlation coefficient was good with 0.8. In conclusion, the algorithm facilitates fast and accurate segmentation of liver metastases, comparable to the current gold standard of manual segmentation.

Automated detection and classification of major retinal vessels for determination of diameter ratio of arteries and veins

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Hatanaka, Yuji; Iwase, Tatsuhiko; Hara, Takeshi; Fujita, Hiroshi

2010-03-01

Abnormalities of retinal vasculatures can indicate health conditions in the body, such as the high blood pressure and diabetes. Providing automatically determined width ratio of arteries and veins (A/V ratio) on retinal fundus images may help physicians in the diagnosis of hypertensive retinopathy, which may cause blindness. The purpose of this study was to detect major retinal vessels and classify them into arteries and veins for the determination of A/V ratio. Images used in this study were obtained from DRIVE database, which consists of 20 cases each for training and testing vessel detection algorithms. Starting with the reference standard of vasculature segmentation provided in the database, major arteries and veins each in the upper and lower temporal regions were manually selected for establishing the gold standard. We applied the black top-hat transformation and double-ring filter to detect retinal blood vessels. From the extracted vessels, large vessels extending from the optic disc to temporal regions were selected as target vessels for calculation of A/V ratio. Image features were extracted from the vessel segments from quarter-disc to one disc diameter from the edge of optic discs. The target segments in the training cases were classified into arteries and veins by using the linear discriminant analysis, and the selected parameters were applied to those in the test cases. Out of 40 pairs, 30 pairs (75%) of arteries and veins in the 20 test cases were correctly classified. The result can be used for the automated calculation of A/V ratio.

Hepatic artery embolisation with a novel radiopaque polymer causes extended liver necrosis in pigs due to occlusion of the concomitant portal vein.

PubMed

Maurer, C A; Renzulli, P; Baer, H U; Mettler, D; Uhlschmid, G; Neuenschwander, P; Suter, U W; Triller, J; Zimmermann, A

2000-02-01

In an attempt to overcome some of the problems encountered with the materials available for liver embolisation, we investigated a novel radiopaque polymer of the polyurethane family (Degra-Bloc). Hepatic artery embolisation of one liver lobe using polyurethane was performed in 19 healthy pigs. Microcirculatory changes were assessed by laser Doppler flowmetry. Radiological and pathological examinations of the livers, hearts and lungs removed provided information about the extent and effect of the embolisation. None of the pigs died due to hepatic failure or toxicity of polyurethane. Microcirculation of embolised liver lobes significantly decreased from 106 (+/-15) perfusion units (PU) to 45 (+/-6) PU immediately after embolisation and further to 28 (+/-7) PU before euthanasia. At this time conventional and angiographic X-ray controls demonstrated the radiopaque casts extending up to the peripheral arteries with signs of degradation over time but without formation of collateral vessels. The main pathological findings consisted of destruction of the portal tract structures and also of large areas of liver necrosis. Polyurethane was encountered in arterioles as small as 10-20 microm, but not in liver sinusoids, hearts or lungs. The novel polymer called DegraBloc is a biocompatible, slowly degradable, radiopaque embolic agent. The occlusion of the arterial tree up to the smallest arteriolar diameter combined with concomitant portal vein occlusion leads to sharp segmental necrosis in pig livers without formation of significant collaterals and without systemic embolism. In the treatment of liver tumours polyurethane might provide a promising alternative to conventional embolic materials, provided that it is used with care in patients with advanced liver cirrhosis.

Rosacea assessment by erythema index and principal component analysis segmentation maps

NASA Astrophysics Data System (ADS)

Kuzmina, Ilona; Rubins, Uldis; Saknite, Inga; Spigulis, Janis

2017-12-01

RGB images of rosacea were analyzed using segmentation maps of principal component analysis (PCA) and erythema index (EI). Areas of segmented clusters were compared to Clinician's Erythema Assessment (CEA) values given by two dermatologists. The results show that visible blood vessels are segmented more precisely on maps of the erythema index and the third principal component (PC3). In many cases, a distribution of clusters on EI and PC3 maps are very similar. Mean values of clusters' areas on these maps show a decrease of the area of blood vessels and erythema and an increase of lighter skin area after the therapy for the patients with diagnosis CEA = 2 on the first visit and CEA=1 on the second visit. This study shows that EI and PC3 maps are more useful than the maps of the first (PC1) and second (PC2) principal components for indicating vascular structures and erythema on the skin of rosacea patients and therapy monitoring.

SU-E-J-142: Performance Study of Automatic Image-Segmentation Algorithms in Motion Tracking Via MR-IGRT

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

Feng, Y; Olsen, J.; Parikh, P.

2014-06-01

Purpose: Evaluate commonly used segmentation algorithms on a commercially available real-time MR image guided radiotherapy (MR-IGRT) system (ViewRay), compare the strengths and weaknesses of each method, with the purpose of improving motion tracking for more accurate radiotherapy. Methods: MR motion images of bladder, kidney, duodenum, and liver tumor were acquired for three patients using a commercial on-board MR imaging system and an imaging protocol used during MR-IGRT. A series of 40 frames were selected for each case to cover at least 3 respiratory cycles. Thresholding, Canny edge detection, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE),more » along with the ViewRay treatment planning and delivery system (TPDS) were included in the comparisons. To evaluate the segmentation results, an expert manual contouring of the organs or tumor from a physician was used as a ground-truth. Metrics value of sensitivity, specificity, Jaccard similarity, and Dice coefficient were computed for comparison. Results: In the segmentation of single image frame, all methods successfully segmented the bladder and kidney, but only FKM, KHM and TPDS were able to segment the liver tumor and the duodenum. For segmenting motion image series, the TPDS method had the highest sensitivity, Jarccard, and Dice coefficients in segmenting bladder and kidney, while FKM and KHM had a slightly higher specificity. A similar pattern was observed when segmenting the liver tumor and the duodenum. The Canny method is not suitable for consistently segmenting motion frames in an automated process, while thresholding and RD-LSE cannot consistently segment a liver tumor and the duodenum. Conclusion: The study compared six different segmentation methods and showed the effectiveness of the ViewRay TPDS algorithm in segmenting motion images during MR-IGRT. Future studies include a selection of conformal segmentation methods based on image

Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform.

PubMed

Chen, Shuo-Tsung; Wang, Tzung-Dau; Lee, Wen-Jeng; Huang, Tsai-Wei; Hung, Pei-Kai; Wei, Cheng-Yu; Chen, Chung-Ming; Kung, Woon-Man

2015-01-01

Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

MR elastography in primary sclerosing cholangitis: correlating liver stiffness with bile duct strictures and parenchymal changes.

PubMed

Bookwalter, Candice A; Venkatesh, Sudhakar K; Eaton, John E; Smyrk, Thomas D; Ehman, Richard L

2018-04-07

To determine correlation of liver stiffness measured by MR Elastography (MRE) with biliary abnormalities on MR Cholangiopancreatography (MRCP) and MRI parenchymal features in patients with primary sclerosing cholangitis (PSC). Fifty-five patients with PSC who underwent MRI of the liver with MRCP and MRE were retrospectively evaluated. Two board-certified abdominal radiologists in agreement reviewed the MRI, MRCP, and MRE images. The biliary tree was evaluated for stricture, dilatation, wall enhancement, and thickening at segmental duct, right main duct, left main duct, and common bile duct levels. Liver parenchyma features including signal intensity on T2W and DWI, and hyperenhancement in arterial, portal venous, and delayed phase were evaluated in nine Couinaud liver segments. Atrophy or hypertrophy of segments, cirrhotic morphology, varices, and splenomegaly were scored as present or absent. Regions of interest were placed in each of the nine segments on stiffness maps wherever available and liver stiffness (LS) was recorded. Mean segmental LS, right lobar (V-VIII), left lobar (I-III, and IVA, IVB), and global LS (average of all segments) were calculated. Spearman rank correlation analysis was performed for significant correlation. Features with significant correlation were then analyzed for significant differences in mean LS. Multiple regression analysis of MRI and MRCP features was performed for significant correlation with elevated LS. A total of 439/495 segments were evaluated and 56 segments not included in MRE slices were excluded for correlation analysis. Mean segmental LS correlated with the presence of strictures (r = 0.18, p < 0.001), T2W hyperintensity (r = 0.38, p < 0.001), DWI hyperintensity (r = 0.30, p < 0.001), and hyperenhancement of segment in all three phases. Mean LS of atrophic and hypertrophic segments were significantly higher than normal segments (7.07 ± 3.6 and 6.67 ± 3.26 vs. 5.1 ± 3.6 kPa, p�

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography.

PubMed

Yong, Yan Ling; Tan, Li Kuo; McLaughlin, Robert A; Chee, Kok Han; Liew, Yih Miin

2017-12-01

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Xylem vulnerability to cavitation can be accurately characterised in species with long vessels using a centrifuge method.

PubMed

Tobin, M F; Pratt, R B; Jacobsen, A L; De Guzman, M E

2013-05-01

Vulnerability to cavitation curves describe the decrease in xylem hydraulic conductivity as xylem pressure declines. Several techniques for constructing vulnerability curves use centrifugal force to induce negative xylem pressure in stem or root segments. Centrifuge vulnerability curves constructed for long-vesselled species have been hypothesised to overestimate xylem vulnerability to cavitation due to increased vulnerability of vessels cut open at stem ends that extend to the middle or entirely through segments. We tested two key predictions of this hypothesis: (i) centrifugation induces greater embolism than dehydration in long-vesselled species, and (ii) the proportion of open vessels changes centrifuge vulnerability curves. Centrifuge and dehydration vulnerability curves were compared for a long- and short-vesselled species. The effect of open vessels was tested in four species by comparing centrifuge vulnerability curves for stems of two lengths. Centrifuge and dehydration vulnerability curves agreed well for the long- and short-vesselled species. Centrifuge vulnerability curves constructed using two stem lengths were similar. Also, the distribution of embolism along the length of centrifuged stems matched the theoretical pressure profile induced by centrifugation. We conclude that vulnerability to cavitation can be accurately characterised with vulnerability curves constructed using a centrifuge technique, even in long-vesselled species. © 2012 German Botanical Society and The Royal Botanical Society of the Netherlands.

Segmentation of organs at risk in CT volumes of head, thorax, abdomen, and pelvis

NASA Astrophysics Data System (ADS)

Han, Miaofei; Ma, Jinfeng; Li, Yan; Li, Meiling; Song, Yanli; Li, Qiang

2015-03-01

Accurate segmentation of organs at risk (OARs) is a key step in treatment planning system (TPS) of image guided radiation therapy. We are developing three classes of methods to segment 17 organs at risk throughout the whole body, including brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin. The three classes of segmentation methods include (1) threshold-based methods for organs of large contrast with adjacent structures such as lungs, trachea, and skin; (2) context-driven Generalized Hough Transform-based methods combined with graph cut algorithm for robust localization and segmentation of liver, kidneys and spleen; and (3) atlas and registration-based methods for segmentation of heart and all organs in CT volumes of head and pelvis. The segmentation accuracy for the seventeen organs was subjectively evaluated by two medical experts in three levels of score: 0, poor (unusable in clinical practice); 1, acceptable (minor revision needed); and 2, good (nearly no revision needed). A database was collected from Ruijin Hospital, Huashan Hospital, and Xuhui Central Hospital in Shanghai, China, including 127 head scans, 203 thoracic scans, 154 abdominal scans, and 73 pelvic scans. The percentages of "good" segmentation results were 97.6%, 92.9%, 81.1%, 87.4%, 85.0%, 78.7%, 94.1%, 91.1%, 81.3%, 86.7%, 82.5%, 86.4%, 79.9%, 72.6%, 68.5%, 93.2%, 96.9% for brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin, respectively. Various organs at risk can be reliably segmented from CT scans by use of the three classes of segmentation methods.

Boundary segmentation for fluorescence microscopy using steerable filters

NASA Astrophysics Data System (ADS)

Ho, David Joon; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

2017-02-01

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation.

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

PubMed

Liu, Xiaoming; Guo, Shuxu; Yang, Bingtao; Ma, Shuzhi; Zhang, Huimao; Li, Jing; Sun, Changjian; Jin, Lanyi; Li, Xueyan; Yang, Qi; Fu, Yu

2018-04-20

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

Detection of hypertensive retinopathy using vessel measurements and textural features.

PubMed

Agurto, Carla; Joshi, Vinayak; Nemeth, Sheila; Soliz, Peter; Barriga, Simon

2014-01-01

Features that indicate hypertensive retinopathy have been well described in the medical literature. This paper presents a new system to automatically classify subjects with hypertensive retinopathy (HR) using digital color fundus images. Our method consists of the following steps: 1) normalization and enhancement of the image; 2) determination of regions of interest based on automatic location of the optic disc; 3) segmentation of the retinal vasculature and measurement of vessel width and tortuosity; 4) extraction of color features; 5) classification of vessel segments as arteries or veins; 6) calculation of artery-vein ratios using the six widest (major) vessels for each category; 7) calculation of mean red intensity and saturation values for all arteries; 8) calculation of amplitude-modulation frequency-modulation (AM-FM) features for entire image; and 9) classification of features into HR and non-HR using linear regression. This approach was tested on 74 digital color fundus photographs taken with TOPCON and CANON retinal cameras using leave-one out cross validation. An area under the ROC curve (AUC) of 0.84 was achieved with sensitivity and specificity of 90% and 67%, respectively.

Retinal imaging analysis based on vessel detection.

PubMed

Jamal, Arshad; Hazim Alkawaz, Mohammed; Rehman, Amjad; Saba, Tanzila

2017-07-01

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. © 2017 Wiley Periodicals, Inc.

Lesions of the segmental and lobar hepatic ducts.

PubMed Central

Longmire, W P; Tompkins, R K

1975-01-01

Despite reports to the contrary, unobstructed drainage of 50% of an otherwise normal liver through either the right or left uninfected hepatic duct is adequate to restore normal liver function, even if the obstructed lobe remains in place. An undrained liver lobe, if present, may require no further treatment. As long as it is completely obstructed and uninfected, it will undergo a progressive asymptomatic atrophy. Cholangitis invariably develops behind a partial lobar ductal obstruction, producing jaundice, pruritis, and fever. Unless unobstructed, uninfected biliary flow can be achieved through a segmental or lobar duct, it is better that the duct be completely obstructed and the affected liver parenchyma allowed to atrophy, provided there is normal biliary flow from the residual 50% of liver. This concept is important in the management of injured anomalous segmental or lobar hepatic duct and in the palliative treatment of bile duct carcinoma. Localized intrahepatic infections communicating with abnormal biliary ducts will require hepatic resection of the infected parenchyma and ducts for cure. The abnormality may be saccular dilatation of the intrahepatic ductal system with abscess formation or intrahepatic abscess associated with stenosis of the ductal system from trauma to the duct, to the duct and liver, or to retained intrahepatic stones. Diffusely situated intrahepatic abscesses secondary to ductal abnormalities can be treated with systemic antibiotics, local drainage of a dmoninant abscess, and efforts to improve biliary drainage. Images Fig. 4. Fig. 5. Fig. 6. Fig. 8. Fig. 9. Fig. 10. Figs. 11A and B Figs. 12A and B. Fig. 13. Fig. 14. Fig. 15. Fig. 16. Fig. 17. PMID:1180585

Segmentation methods for breast vasculature in dual-energy contrast-enhanced digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Lau, Kristen C.; Lee, Hyo Min; Singh, Tanushriya; Maidment, Andrew D. A.

2015-03-01

Dual-energy contrast-enhanced digital breast tomosynthesis (DE CE-DBT) uses an iodinated contrast agent to image the three-dimensional breast vasculature. The University of Pennsylvania has an ongoing DE CE-DBT clinical study in patients with known breast cancers. The breast is compressed continuously and imaged at four time points (1 pre-contrast; 3 post-contrast). DE images are obtained by a weighted logarithmic subtraction of the high-energy (HE) and low-energy (LE) image pairs. Temporal subtraction of the post-contrast DE images from the pre-contrast DE image is performed to analyze iodine uptake. Our previous work investigated image registration methods to correct for patient motion, enhancing the evaluation of vascular kinetics. In this project we investigate a segmentation algorithm which identifies blood vessels in the breast from our temporal DE subtraction images. Anisotropic diffusion filtering, Gabor filtering, and morphological filtering are used for the enhancement of vessel features. Vessel labeling methods are then used to distinguish vessel and background features successfully. Statistical and clinical evaluations of segmentation accuracy in DE-CBT images are ongoing.

Small animal magnetic resonance imaging: an efficient tool to assess liver volume and intrahepatic vascular anatomy.

PubMed

Melloul, Emmanuel; Raptis, Dimitri A; Boss, Andreas; Pfammater, Thomas; Tschuor, Christoph; Tian, Yinghua; Graf, Rolf; Clavien, Pierre-Alain; Lesurtel, Mickael

2014-04-01

To develop a noninvasive technique to assess liver volumetry and intrahepatic portal vein anatomy in a mouse model of liver regeneration. Fifty-two C57BL/6 male mice underwent magnetic resonance imaging (MRI) of the liver using a 4.7 T small animal MRI system after no treatment, 70% partial hepatectomy (PH), or selective portal vein embolization. The protocol consisted of the following sequences: three-dimensional-encoded spoiled gradient-echo sequence (repetition time per echo time 15 per 2.7 ms, flip angle 20°) for volumetry, and two-dimensional-encoded time-of-flight angiography sequence (repetition time per echo time 18 per 6.4 ms, flip angle 80°) for vessel visualization. Liver volume and portal vein segmentation was performed using a dedicated postprocessing software. In animals with portal vein embolization, portography served as reference standard. True liver volume was measured after sacrificing the animals. Measurements were carried out by two independent observers with subsequent analysis by the Cohen κ-test for interobserver agreement. MRI liver volumetry highly correlated with the true liver volume measurement using a conventional method in both the untreated liver and the liver remnant after 70% PH with a high interobserver correlation coefficient of 0.94 (95% confidence interval, 0.80-0.98 for untreated liver [P < 0.001] and 0.90-0.97 after 70% PH [P < 0.001]). The diagnostic accuracy of magnetic resonance angiography for the occlusion of one branch of the portal vein was 0.95 (95% confidence interval, 0.84-1). The level of agreement between the two observers for the description of intrahepatic vascular anatomy was excellent (Cohen κ value = 0.925). This protocol may be used for noninvasive liver volumetry and visualization of portal vein anatomy in mice. It will serve the dynamic study of new strategies to enhance liver regeneration in vivo. Copyright © 2014 Elsevier Inc. All rights reserved.

A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising.

PubMed

Khan, Khan Bahadar; Khaliq, Amir A; Jalil, Abdul; Shahid, Muhammad

2018-01-01

The exploration of retinal vessel structure is colossally important on account of numerous diseases including stroke, Diabetic Retinopathy (DR) and coronary heart diseases, which can damage the retinal vessel structure. The retinal vascular network is very hard to be extracted due to its spreading and diminishing geometry and contrast variation in an image. The proposed technique consists of unique parallel processes for denoising and extraction of blood vessels in retinal images. In the preprocessing section, an adaptive histogram equalization enhances dissimilarity between the vessels and the background and morphological top-hat filters are employed to eliminate macula and optic disc, etc. To remove local noise, the difference of images is computed from the top-hat filtered image and the high-boost filtered image. Frangi filter is applied at multi scale for the enhancement of vessels possessing diverse widths. Segmentation is performed by using improved Otsu thresholding on the high-boost filtered image and Frangi's enhanced image, separately. In the postprocessing steps, a Vessel Location Map (VLM) is extracted by using raster to vector transformation. Postprocessing steps are employed in a novel way to reject misclassified vessel pixels. The final segmented image is obtained by using pixel-by-pixel AND operation between VLM and Frangi output image. The method has been rigorously analyzed on the STARE, DRIVE and HRF datasets.

Morphometry of A1 segment of the anterior cerebral artery and its clinical importance.

PubMed

Krishnamurthy, A; Nayak, S R; Bagoji, I B; D'Costa, S; Pai, M M; Jiji, P J; Kumar, C G; Rai, R

2010-01-01

Anterior cerebral artery, one of the terminal branches of the internal carotid artery is an important vessel taking part in the formation of circle of Willis. It supplies a large part of the medial surface of the cerebral hemisphere containing the areas of motor and somatosensory cortices of the lower limb. Aim of this study was the morphometry of A1 segment of the anterior cerebral artery. 93 formalin fixed brain specimen of either sex and of Indian origin were studied. The mean length, mean external diameter and the anomalies present in A1 segment of the vessel were studied in detail and photographed. The mean length of A1 segment of the vessel was 14.49+/-0.28 mm and 14.22+/-0.22 mm on right and left side respectively. The mean external diameter of the vessel on right and left side was 2.12+/-0.07 mm and 2.32+/-0.06 mm respectively. Narrowing, aneurysm formation, buttonhole formation and median anterior cerebral artery were the anomalies seen with an occurrence of 15.05%, 5.37%, 3.22% and 12.9%, respectively. The above anomalies did not have any sex or side predilection. Knowledge of morphometry of the vessel will be of use to neurosurgeons while performing the shunt operation, in assessing the feasibility of such operations and in the choice of patients. From this study we infer that the morphometry of anterior cerebral artery varies in different population and that the neurosurgeons operating should have a thorough knowledge of the possible variations.

Live minimal path for interactive segmentation of medical images

NASA Astrophysics Data System (ADS)

Chartrand, Gabriel; Tang, An; Chav, Ramnada; Cresson, Thierry; Chantrel, Steeve; De Guise, Jacques A.

2015-03-01

Medical image segmentation is nowadays required for medical device development and in a growing number of clinical and research applications. Since dedicated automatic segmentation methods are not always available, generic and efficient interactive tools can alleviate the burden of manual segmentation. In this paper we propose an interactive segmentation tool based on image warping and minimal path segmentation that is efficient for a wide variety of segmentation tasks. While the user roughly delineates the desired organs boundary, a narrow band along the cursors path is straightened, providing an ideal subspace for feature aligned filtering and minimal path algorithm. Once the segmentation is performed on the narrow band, the path is warped back onto the original image, precisely delineating the desired structure. This tool was found to have a highly intuitive dynamic behavior. It is especially efficient against misleading edges and required only coarse interaction from the user to achieve good precision. The proposed segmentation method was tested for 10 difficult liver segmentations on CT and MRI images, and the resulting 2D overlap Dice coefficient was 99% on average..

Accurate detection of blood vessels improves the detection of exudates in color fundus images.

PubMed

Youssef, Doaa; Solouma, Nahed H

2012-12-01

Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the lesions and the anatomic structures of the retina. In this paper, we provide a new method for the detection of blood vessels that improves the detection of exudates in fundus photographs. The method starts with an edge detection algorithm which results in a over segmented image. Then the new feature-based algorithm can be used to accurately detect the blood vessels. This algorithm considers the characteristics of a retinal blood vessel such as its width range, intensities and orientations for the purpose of selective segmentation. Because of its bulb shape and its color similarity with exudates, the optic disc can be detected using the common Hough transform technique. The extracted blood vessel tree and optic disc could be subtracted from the over segmented image to get an initial estimate of exudates. The final estimation of exudates can then be obtained by morphological reconstruction based on the appearance of exudates. This method is shown to be promising since it increases the sensitivity and specificity of exudates detection to 80% and 100% respectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

A study of the sink effect by blood vessels in radiofrequency ablation.

PubMed

Zorbas, George; Samaras, Theodoros

2015-02-01

The objective of the current work was to study the sink effect in radiofrequency ablation (RFA) caused by a blood vessel located close to an electrode in a two-compartment numerical model, consisting of a spherical tumor embedded in healthy liver tissue. Several blood vessels of different sizes were studied at different distances from the electrode. It was found that when a straight blood vessel, cylindrical in shape, is located parallel to the electrode, the minimum distance for a drop of only 10% in the isothermal treatment volume above 50°C, compared to the model without the blood vessel, varies from 4.49 mm (for a vessel of 2mm in diameter) to 20.02 mm (for a vessel 20mm in diameter). The results can be used as a guideline to clinical practitioners, in order to quickly assess the potential impact of existing blood vessels on the resulting treatment volume. Copyright © 2014 Elsevier Ltd. All rights reserved.

New Technique for Liver Resection Using Heat Coagulative Necrosis

PubMed Central

Weber, Jean-Christophe; Navarra, Giuseppe; Jiao, Long R.; Nicholls, Joanna P.; Jensen, Steen Lindkaer; Habib, Nagy A.

2002-01-01

Objective To assess a new bloodless technique using radiofrequency energy for segmental liver resection of hepatic tumors. Summary Background Data Liver resection remains a formidable surgical procedure; safe performance requires a high level of training and skill. Intraoperative blood loss during liver resection remains a major concern because it is associated with a higher rate of postoperative complications and shorter long-term survival. Methods From January 2000 to June 2001, 15 patients with various hepatic tumors were operated on using radiofrequency energy to remove the tumor in its entirety. Radiofrequency energy was applied along the margins of the tumor to create “zones of necrosis” before resection with a scalpel. Results No blood transfusions were required. The mean blood loss during resection was 30 ± 10 mL. No mortality or morbidity was observed. The median postoperative stay was 8 days (range 5–9). No liver recurrence was detected in patients undergoing resection with this technique during follow-up periods ranging from 2 to 20 months. Conclusions Segmental and wedge liver resection assisted by radiofrequency is safe. This novel technique offers a new method for transfusion-free resection. PMID:12409660

Segmented metallic nanostructures, homogeneous metallic nanostructures and methods for producing same

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

Wong, Stanislaus; Koenigsmann, Christopher

The present invention includes a method of producing a segmented 1D nanostructure. The method includes providing a vessel containing a template wherein on one side of the template is a first metal reagent solution and on the other side of the template is a reducing agent solution, wherein the template comprises at least one pore; allowing a first segment of a 1D nanostructure to grow within a pore of the template until a desired length is reached; replacing the first metal reagent solution with a second metal reagent solution; allowing a second segment of a 1D nanostructure to grow frommore » the first segment until a desired length is reached, wherein a segmented 1D nanostructure is produced.« less

3D liver volume reconstructed for palpation training.

PubMed

Tibamoso, Gerardo; Perez-Gutierrez, Byron; Uribe-Quevedo, Alvaro

2013-01-01

Virtual Reality systems for medical procedures such as the palpation of different organs, requires fast, robust, accurate and reliable computational methods for providing realism during interaction with the 3D biological models. This paper presents the segmentation, reconstruction and palpation simulation of a healthy liver volume as a tool for training. The chosen method considers the mechanical characteristics and liver properties for correctly simulating palpation interactions, which results appropriate as a complementary tool for training medical students in familiarizing with the liver anatomy.

Liver regeneration in donors and adult recipients after living donor liver transplantation.

PubMed

Haga, Junko; Shimazu, Motohide; Wakabayashi, Go; Tanabe, Minoru; Kawachi, Shigeyuki; Fuchimoto, Yasushi; Hoshino, Ken; Morikawa, Yasuhide; Kitajima, Masaki; Kitagawa, Yuko

2008-12-01

In living donor liver transplantation, the safety of the donor operation is the highest priority. The introduction of the right lobe graft was late because of concerns about donor safety. We investigated donor liver regeneration by the types of resected segments as well as recipients to assess that appropriate regeneration was occurring. Eighty-seven donors were classified into 3 groups: left lateral section donors, left lobe donors, and right lobe donors. Forty-seven adult recipients were classified as either left or right lobe grafted recipients. Volumetry was retrospectively performed at 1 week, 1, 2, 3, and 6 months, and 1 year after the operation. In the right lobe donor group, the remnant liver volume was 45.4%, and it rapidly increased to 68.9% at 1 month and 89.8% at 6 months. At 6 months, the regeneration ratios were almost the same in all donor groups. The recipient liver volume increased rapidly until 2 months, exceeding the standard liver volume, and then gradually decreased to 90% of the standard liver volume. Livers of the right lobe donor group regenerated fastest in the donor groups, and the recipient liver regenerated faster than the donor liver. Analyzing liver regeneration many times with a large number of donors enabled us to understand the normal liver regeneration pattern. Although the donor livers did not reach their initial volume, the donors showed normal liver function at 1 year. The donors have returned to their normal daily activities. Donor hepatectomy, even right hepatectomy, can be safely performed with accurate preoperative volumetry and careful decision-making concerning graft-type selection.

Liver CT image processing: a short introduction of the technical elements.

PubMed

Masutani, Y; Uozumi, K; Akahane, Masaaki; Ohtomo, Kuni

2006-05-01

In this paper, we describe the technical aspects of image analysis for liver diagnosis and treatment, including the state-of-the-art of liver image analysis and its applications. After discussion on modalities for liver image analysis, various technical elements for liver image analysis such as registration, segmentation, modeling, and computer-assisted detection are covered with examples performed with clinical data sets. Perspective in the imaging technologies is also reviewed and discussed.

Blood vessel classification into arteries and veins in retinal images

NASA Astrophysics Data System (ADS)

Kondermann, Claudia; Kondermann, Daniel; Yan, Michelle

2007-03-01

The prevalence of diabetes is expected to increase dramatically in coming years; already today it accounts for a major proportion of the health care budget in many countries. Diabetic Retinopathy (DR), a micro vascular complication very often seen in diabetes patients, is the most common cause of visual loss in working age population of developed countries today. Since the possibility of slowing or even stopping the progress of this disease depends on the early detection of DR, an automatic analysis of fundus images would be of great help to the ophthalmologist due to the small size of the symptoms and the large number of patients. An important symptom for DR are abnormally wide veins leading to an unusually low ratio of the average diameter of arteries to veins (AVR). There are also other diseases like high blood pressure or diseases of the pancreas with one symptom being an abnormal AVR value. To determine it, a classification of vessels as arteries or veins is indispensable. As to our knowledge despite the importance there have only been two approaches to vessel classification yet. Therefore we propose an improved method. We compare two feature extraction methods and two classification methods based on support vector machines and neural networks. Given a hand-segmentation of vessels our approach achieves 95.32% correctly classified vessel pixels. This value decreases by 10% on average, if the result of a segmentation algorithm is used as basis for the classification.

Selective hypertrophy of the lobus caudatus as a novel approach enabling extended right hepatectomy in the presence of a non-perfused left lateral liver lobe.

PubMed

Atanasov, Georgi; Schmelzle, Moritz; Thelen, Armin; Wiltberger, Georg; Hau, Hans-Michael; Krenzien, Felix; Petersen, Tim-Ole; Moche, Michael; Jonas, Sven

2014-08-01

Portal vein embolization (PVE) is a well-established technique to enhance functional hepatic reserves of segments II and III before curative extended right hepatectomy for tumors of the right liver lobe. However, an adequate hepatopetal flow of the left lateral portal vein branches is required for a sufficient PVE-associated hypertrophy. Here, we report a 65-year old patient suffering from a locally advanced intrahepatic cholangiocarcinoma in the right liver lobe and segment IV. A curative extended right hepatectomy after preoperative PVE of liver segments IV-VIII was initially impossible because of partial thrombosis of the left lateral portal vein branches resulting in an ischemic-type atrophy of segments II and III. However, due to a massive hypertrophy of the caudate lobe following PVE of liver segments IV-VIII, subsequent extended right hepatectomy with intraoperative thrombectomy of segments II and III was made possible. To our knowledge this is the first case in which an extended right hepatectomy for a liver malignancy, in the presence of atrophic left lateral section, was made possible by a massive PVE-associated hypertrophy of the caudate lobe.

Performance evaluation of an automatic segmentation method of cerebral arteries in MRA images by use of a large image database

NASA Astrophysics Data System (ADS)

Uchiyama, Yoshikazu; Asano, Tatsunori; Hara, Takeshi; Fujita, Hiroshi; Kinosada, Yasutomi; Asano, Takahiko; Kato, Hiroki; Kanematsu, Masayuki; Hoshi, Hiroaki; Iwama, Toru

2009-02-01

The detection of cerebrovascular diseases such as unruptured aneurysm, stenosis, and occlusion is a major application of magnetic resonance angiography (MRA). However, their accurate detection is often difficult for radiologists. Therefore, several computer-aided diagnosis (CAD) schemes have been developed in order to assist radiologists with image interpretation. The purpose of this study was to develop a computerized method for segmenting cerebral arteries, which is an essential component of CAD schemes. For the segmentation of vessel regions, we first used a gray level transformation to calibrate voxel values. To adjust for variations in the positioning of patients, registration was subsequently employed to maximize the overlapping of the vessel regions in the target image and reference image. The vessel regions were then segmented from the background using gray-level thresholding and region growing techniques. Finally, rule-based schemes with features such as size, shape, and anatomical location were employed to distinguish between vessel regions and false positives. Our method was applied to 854 clinical cases obtained from two different hospitals. The segmentation of cerebral arteries in 97.1%(829/854) of the MRA studies was attained as an acceptable result. Therefore, our computerized method would be useful in CAD schemes for the detection of cerebrovascular diseases in MRA images.

MRI-based patient-specific human carotid atherosclerotic vessel material property variations in patients, vessel location and long-term follow up

PubMed Central

Wang, Qingyu; Canton, Gador; Guo, Jian; Guo, Xiaoya; Hatsukami, Thomas S.; Billiar, Kristen L.; Yuan, Chun; Wu, Zheyang

2017-01-01

Background Image-based computational models are widely used to determine atherosclerotic plaque stress/strain conditions and investigate their association with plaque progression and rupture. However, patient-specific vessel material properties are in general lacking in those models, limiting the accuracy of their stress/strain measurements. A noninvasive approach of combining in vivo 3D multi-contrast and Cine magnetic resonance imaging (MRI) and computational modeling was introduced to quantify patient-specific carotid plaque material properties for potential plaque model improvements. Vessel material property variation in patients, along vessel segment, and between baseline and follow up were investigated. Methods In vivo 3D multi-contrast and Cine MRI carotid plaque data were acquired from 8 patients with follow-up (18 months) with written informed consent obtained. 3D thin-layer models and an established iterative procedure were used to determine parameter values of the Mooney-Rivlin models for the 81slices from 16 plaque samples. Effective Young’s Modulus (YM) values were calculated for comparison and analysis. Results Average Effective Young’s Modulus (YM) and circumferential shrinkage rate (C-Shrink) value of the 81 slices was 411kPa and 5.62%, respectively. Slice YM value varied from 70 kPa (softest) to 1284 kPa (stiffest), a 1734% difference. Average slice YM values by vessel varied from 109 kPa (softest) to 922 kPa (stiffest), a 746% difference. Location-wise, the maximum slice YM variation rate within a vessel was 311% (149 kPa vs. 613 kPa). The average slice YM variation rate for the 16 vessels was 134%. The average variation of YM values for all patients from baseline to follow up was 61.0%. The range of the variation of YM values was [-28.4%, 215%]. For plaque progression study, YM at follow-up showed negative correlation with plaque progression measured by wall thickness increase (WTI) (r = -0.7764, p = 0.0235). Wall thickness at baseline

Usefulness of free-breathing readout-segmented echo-planar imaging (RESOLVE) for detection of malignant liver tumors: comparison with single-shot echo-planar imaging (SS-EPI).

PubMed

Tokoro, Hirokazu; Fujinaga, Yasunari; Ohya, Ayumi; Ueda, Kazuhiko; Shiobara, Aya; Kitou, Yoshihiro; Ueda, Hitoshi; Kadoya, Masumi

2014-10-01

We aimed to clarify the usefulness of free-breathing readout-segmented echo-planar imaging (RESOLVE), which is multi-shot echo-planar imaging based on a 2D-navigator-based reacquisition technique, for detecting malignant liver tumor. In 77 patients with malignant liver tumors, free-breathing RESOLVE and respiratory-triggered single-shot echo-planar imaging (SS-EPI) at 3-T MR unit were performed. We set a scan time up to approximately 5 min (300s) before examination, measured actual scan time and assessed (1) susceptibility and (2) motion artifacts in the right and left liver lobes (3, no artifact; 1, marked), and (3) detectability of malignant liver tumors (3, good; 1, poor) using a 3-point scale. The median actual scan time of RESOLVE/SS-EPI was 365/423s. The median scores of each factor in RESOLVE/SS-EPI were as following in this order: (1) 3/2 (right lobe); 3/3 (left lobe), (2) 2/3 (right lobe); 1/2 (left lobe), and (3) 3/3, respectively. Significant differences were noted between RESOLVE and SS-EPI in all evaluated factors (P<0.05) except for susceptibility of left lobe and detectability of the lesions. Despite the effect of motion artifacts, RESOLVE provides a comparable detectability of the lesion and the advantage of reducing scanning time compared with SS-EPI. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan.

PubMed

Siri, Sangeeta K; Latte, Mrityunjaya V

2017-11-01

Many different diseases can occur in the liver, including infections such as hepatitis, cirrhosis, cancer and over effect of medication or toxins. The foremost stage for computer-aided diagnosis of liver is the identification of liver region. Liver segmentation algorithms extract liver image from scan images which helps in virtual surgery simulation, speedup the diagnosis, accurate investigation and surgery planning. The existing liver segmentation algorithms try to extort exact liver image from abdominal Computed Tomography (CT) scan images. It is an open problem because of ambiguous boundaries, large variation in intensity distribution, variability of liver geometry from patient to patient and presence of noise. A novel approach is proposed to meet challenges in extracting the exact liver image from abdominal CT scan images. The proposed approach consists of three phases: (1) Pre-processing (2) CT scan image transformation to Neutrosophic Set (NS) and (3) Post-processing. In pre-processing, the noise is removed by median filter. The "new structure" is designed to transform a CT scan image into neutrosophic domain which is expressed using three membership subset: True subset (T), False subset (F) and Indeterminacy subset (I). This transform approximately extracts the liver image structure. In post processing phase, morphological operation is performed on indeterminacy subset (I) and apply Chan-Vese (C-V) model with detection of initial contour within liver without user intervention. This resulted in liver boundary identification with high accuracy. Experiments show that, the proposed method is effective, robust and comparable with existing algorithm for liver segmentation of CT scan images. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of liver volumetry on contrast-enhanced CT images: one semiautomatic and two automatic approaches.

PubMed

Cai, Wei; He, Baochun; Fan, Yingfang; Fang, Chihua; Jia, Fucang

2016-11-08

This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods- one interactive method, an in-house-developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)-based segmentation, and one automatic probabilistic atlas (PA)-guided segmentation method on clinical contrast-enhanced CT images. Forty-two datasets, including 27 normal liver and 15 space-occupying liver lesion patients, were retrospectively included in this study. The three methods - one semiautomatic 3DMIA, one automatic ASM-based, and one automatic PA-based liver volumetry - achieved an accuracy with VD (volume difference) of -1.69%, -2.75%, and 3.06% in the normal group, respectively, and with VD of -3.20%, -3.35%, and 4.14% in the space-occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excel-lent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p < 0.001), as well as between the automatic volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p < 0.001). The semiautomatic interactive 3DMIA, automatic ASM-based, and automatic PA-based liver volum-etry agreed well with manual gold standard in both the normal liver group and the space-occupying lesion group. The ASM- and PA-based automatic segmentation have better efficiency in clinical use. © 2016 The Authors.

Anatomy of the liver: an outline with three levels of complexity--a further step towards tailored territorial liver resections.

PubMed

Majno, Pietro; Mentha, Gilles; Toso, Christian; Morel, Philippe; Peitgen, Heinz O; Fasel, Jean H D

2014-03-01

The vascular anatomy of the liver can be described at three different levels of complexity according to the use that the description has to serve. The first--conventional--level corresponds to the traditional 8-segments scheme of Couinaud and serves as a common language between clinicians from different specialties to describe the location of focal hepatic lesions. The second--surgical--level, to be applied to anatomical liver resections and transplantations, takes into account the real branching of the major portal pedicles and of the hepatic veins. Radiological and surgical techniques exist nowadays to make full use of this anatomy, but this requires accepting that the Couinaud scheme is a simplification, and looking at the vascular architecture with an unprejudiced eye. The third--academic--level of complexity concerns the anatomist, and the need to offer a systematization that resolves the apparent contradictions between anatomical literature, radiological imaging, and surgical practice. Based on the real number of second-order portal branches that, although variable averages 20, we submit a system called the "1-2-20 concept", and suggest that it fits best the number of actual--as opposed to idealized--anatomical liver segments. Copyright © 2013 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Robust Hidden Markov Model based intelligent blood vessel detection of fundus images.

PubMed

Hassan, Mehdi; Amin, Muhammad; Murtza, Iqbal; Khan, Asifullah; Chaudhry, Asmatullah

2017-11-01

In this paper, we consider the challenging problem of detecting retinal vessel networks. Precise detection of retinal vessel networks is vital for accurate eye disease diagnosis. Most of the blood vessel tracking techniques may not properly track vessels in presence of vessels' occlusion. Owing to problem in sensor resolution or acquisition of fundus images, it is possible that some part of vessel may occlude. In this scenario, it becomes a challenging task to accurately trace these vital vessels. For this purpose, we have proposed a new robust and intelligent retinal vessel detection technique on Hidden Markov Model. The proposed model is able to successfully track vessels in the presence of occlusion. The effectiveness of the proposed technique is evaluated on publically available standard DRIVE dataset of the fundus images. The experiments show that the proposed technique not only outperforms the other state of the art methodologies of retinal blood vessels segmentation, but it is also capable of accurate occlusion handling in retinal vessel networks. The proposed technique offers better average classification accuracy, sensitivity, specificity, and area under the curve (AUC) of 95.7%, 81.0%, 97.0%, and 90.0% respectively, which shows the usefulness of the proposed technique. Copyright © 2017 Elsevier B.V. All rights reserved.

Electrochemical lesions in the rat liver support its potential for treatment of liver tumors.

PubMed

Wemyss-Holden, S A; Robertson, G S; Dennison, A R; de la M Hall, P; Fothergill, J C; Jones, B; Maddern, G J

2000-09-01

An effective therapy is needed for patients with surgically unresectable liver tumors who have very limited life expectancy. One possible treatment is electrochemical tumor necrosis. This study investigated the natural history of electrochemical lesions in the normal rat liver. A direct current generator, connected to platinum electrodes, was used to create controlled areas of liver necrosis. Animals were sacrificed 2 days, 2 weeks, 2 months, and 6 months after treatment and the macroscopic and histological appearance of the necrotic lesions was followed. No animal died as a result of electrolysis; postoperatively, all gained weight normally. Liver enzymes were significantly (P < 0.001) elevated after treatment, but returned to normal after a week. Two days after electrolysis, histology confirmed an ellipsoidal area of coagulative necrosis at the site of the electrode tip and commonly a segment of peripheral necrosis. After 2 weeks there was histological evidence of healing. By 6 months, very little necrotic tissue remained within a small fibrous scar. Electrolysis is a safe method for creating defined areas of liver necrosis that heal well with no associated mortality. This study supports the potential of electrolysis for treating patients with unresectable liver tumors. Copyright 2000 Academic Press.

Initial experience with laparoscopic inferior epigastric vessel ligation for delayed transverse rectus abdominus musculocutaneous flap breast reconstruction.

PubMed

Trus, Thadeus L; Collins, E Dale; Demas, Christopher; Kerrigan, Carolyn

2007-04-01

Transverse rectus abdominus musculocutaneous (TRAM) flap breast reconstruction provides excellent cosmetic results. Pedicle flap viability is greatly enhanced by prereconstruction inferior epigastric vessel ligation, which encourages collateral arterial flow (delayed TRAM). We report our initial experience with laparoscopic inferior epigastric vessel ligation. Prospective case series. Tertiary academic center. Female patients with breast cancer who chose pedicle TRAM reconstruction. Vessel ligations were performed 7 to 14 days prior to reconstruction. Abdominal access was achieved with a 3-mm umbilical trocar. A 5-mm trocar was placed lateral to the rectus sheath in the right lower quadrant. Five-millimeter Teflon clips were used to ligate the vessels near their origin. Complications of surgery and subsequent flap viability. From January 2001 to July 2006, 130 patients had laparoscopic inferior epigastric vessel ligation, of whom 123 patients had bilateral ligation. Additional procedures in conjunction with vessel ligation were performed in 38 patients (sentinel node biopsy [27], bilateral oophorectomy [7], liver biopsy [2], breast biopsy [1], and Nissen fundoplication [1]). Median operative time for those patients undergoing ligation only was 32.6 minutes (range, 14-121 minutes). The inferior epigastric vessels were not identified in 2 patients. Metastatic breast cancer involving the liver was found in 1 patient. There were no conversions or complications. Subsequent TRAM flap viability was excellent in most cases, with 1 complete flap necrosis in a high-risk, morbidly obese patient. Laparoscopic inferior epigastric vessel ligation for delayed TRAM flap breast reconstruction is a safe, effective procedure.

Discriminative dictionary learning for abdominal multi-organ segmentation.

PubMed

Tong, Tong; Wolz, Robin; Wang, Zehan; Gao, Qinquan; Misawa, Kazunari; Fujiwara, Michitaka; Mori, Kensaku; Hajnal, Joseph V; Rueckert, Daniel

2015-07-01

An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Effects of large vessel on temperature distribution based on photothermal coupling interaction model

NASA Astrophysics Data System (ADS)

Li, Zhifang; Zhang, Xiyang; Li, Zuoran; Li, Hui

2016-10-01

This paper is based on the finite element analysis method for studying effects of large blood vessel on temperature based on photothermal coupling interaction model, and it couples the physical field of optical transmission with the physical field of heat transfer in biological tissue by using COMSOL Multiphysics 4.4 software. The results demonstrate the cooling effect of large blood vessel, which can be potential application for the treatment of liver tumors.

Vessel network detection using contour evolution and color components

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

Ushizima, Daniela; Medeiros, Fatima; Cuadros, Jorge

2011-06-22

Automated retinal screening relies on vasculature segmentation before the identification of other anatomical structures of the retina. Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration, among other applications. There is an extensive literature related to this problem, often excluding the inherent heterogeneity of ophthalmic clinical images. The contribution of this paper relies on an algorithm using front propagation to segment the vessel network. The algorithm includes a penalty in the wait queue on the fast marching heap to minimize leakage of the evolving interface. The method requires no manual labeling, a minimum numbermore » of parameters and it is capable of segmenting color ocular fundus images in real scenarios, where multi-ethnicity and brightness variations are parts of the problem.« less

Comparative morphometric analysis of 5 interpositional arterial autograft options for adult living donor liver transplantation.

PubMed

Imakuma, E S; Bordini, A L; Millan, L S; Massarollo, P C B; Caldini, E T E G

2014-01-01

In living donor liver transplantation, the right-sided graft presents thin and short vessels, bringing forward a more difficult anastomosis. In these cases, an interpositional arterial autograft can be used to favor the performance of the arterial anastomosis, making the procedure easier and avoiding surgical complications. We compared the inferior mesenteric artery (IMA), the splenic artery (SA), the inferior epigastric artery (IEA), the descending branch of the lateral circumflex femoral artery (LCFA), and the proper hepatic artery (PHA) as options for interpositional autograft in living donor liver transplantation. Segments of at least 3 cm of all 5 arteries were harvested from 16 fresh adult cadavers from both genders through standardized dissection. The analyzed measures were proximal and distal diameter and length. The proximal diameter of the RHA and the distal diameter of the SA, IMA, IEA and the LCFA were compared to the distal diameter of the RHA. The proximal and distal diameters of the SA, IEA and LCFA were compared to study caliber gain of each artery. All arteries except the IMA showed statistical significant difference in relation to the RHA in terms of diameter. Regarding caliber gain, the arteries demonstrated statistical significant difference. All the harvested arteries except PHA were 3 cm in length. The IMA demonstrated the best compatibility with the RHA in terms of diameter and showed sufficient length to be employed as interpositional graft. The PHA, the SA, the IEA and the LCFA presented statistically significant different diameters when compared to the RHA. Among these vessels, only the PHA did not show sufficient mean length. Copyright © 2014 Elsevier Inc. All rights reserved.

Triamcinolone Acetonide Selectively Inhibits Angiogenesis in Small Blood Vessels and Decreases Vessel Diameter within the Vascular Tree

NASA Technical Reports Server (NTRS)

McKay, Terri L.; Gredeon, Dan J.; Vickerman, Mary B.; Hylton, alan G.; Ribita, Daniela; Olar, Harry H.; Kaiser, Peter K.; Parsons-Wingerter, Patricia

2007-01-01

The steroid triamcinolone acetonide (TA) is a potent anti-angiogenesis drug used to treat retinal vascular diseases that include diabetic retinopathy, vascular occlusions and choroidal neovascularization. To quantify the effects of TA on branching morphology within the angiogenic microvascular tree of the chorioallantoic membrane (CAM) of quail embryos. Increasing concentrations of TA (0-16 ng/ml) were applied topically on embryonic day 7 (E7) to the chorioallantoic membrane (CAM) of quail embryos cultured in Petri dishes, and incubated for an additional 24 or 48 hours until fixation. Binary (black/white) microscopic images of arterial end points were quantified by VESGEN software (for Generational Analysis of Vessel Branching) to obtain major vascular parameters that include vessel diameter (Dv), fractal dimension (Df), tortuosity (Tv) and densities of vessel area, length, number and branch point (Av, Lv, Nv and Brv). For assessment of specific changes in vascular morphology induced by TA, the VESGEN software automatically segmented the vascular tree into branching generations (G1...G10) according to changes in vessel diameter and branching. Vessel density decreased significantly up to 34% as the function of increasing concentration of TA according to Av, Lv, Brv, Nv and Df. TA selectively inhibited the growth of new, small vessels, because Lv decreased from 13.14plus or minus 0.61 cm/cm2 for controls to 8.012 plus or minus 0.82 cm/cm2 at 16 ng TA/ml in smaller branching generations (G7-G10), and for Nv from 473.83 plus or minus 29.85 cm(-)2 to 302.32 plus or minus 33.09 cm-()2. In contrast, vessel diameter (Dv) decreased throughout the vascular tree (G1-G10).

Coronary artery analysis: Computer-assisted selection of best-quality segments in multiple-phase coronary CT angiography

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

Zhou, Chuan, E-mail: chuan@umich.edu; Chan, Heang-

Purpose: The authors are developing an automated method to identify the best-quality coronary arterial segment from multiple-phase coronary CT angiography (cCTA) acquisitions, which may be used by either interpreting physicians or computer-aided detection systems to optimally and efficiently utilize the diagnostic information available in multiple-phase cCTA for the detection of coronary artery disease. Methods: After initialization with a manually identified seed point, each coronary artery tree is automatically extracted from multiple cCTA phases using our multiscale coronary artery response enhancement and 3D rolling balloon region growing vessel segmentation and tracking method. The coronary artery trees from multiple phases are thenmore » aligned by a global registration using an affine transformation with quadratic terms and nonlinear simplex optimization, followed by a local registration using a cubic B-spline method with fast localized optimization. The corresponding coronary arteries among the available phases are identified using a recursive coronary segment matching method. Each of the identified vessel segments is transformed by the curved planar reformation (CPR) method. Four features are extracted from each corresponding segment as quality indicators in the original computed tomography volume and the straightened CPR volume, and each quality indicator is used as a voting classifier for the arterial segment. A weighted voting ensemble (WVE) classifier is designed to combine the votes of the four voting classifiers for each corresponding segment. The segment with the highest WVE vote is then selected as the best-quality segment. In this study, the training and test sets consisted of 6 and 20 cCTA cases, respectively, each with 6 phases, containing a total of 156 cCTA volumes and 312 coronary artery trees. An observer preference study was also conducted with one expert cardiothoracic radiologist and four nonradiologist readers to visually rank vessel

Bioengineered transplantable porcine livers with re-endothelialized vasculature.

PubMed

Ko, In Kap; Peng, Li; Peloso, Andrea; Smith, Charesa J; Dhal, Abritee; Deegan, Daniel B; Zimmerman, Cindy; Clouse, Cara; Zhao, Weixin; Shupe, Thomas D; Soker, Shay; Yoo, James J; Atala, Anthony

2015-02-01

Donor shortage remains a continued challenge in liver transplantation. Recent advances in tissue engineering have provided the possibility of creating functional liver tissues as an alternative to donor organ transplantation. Small bioengineered liver constructs have been developed, however a major challenge in achieving functional bioengineered liver in vivo is the establishment of a functional vasculature within the scaffolds. Our overall goal is to bioengineer intact livers, suitable for transplantation, using acellular porcine liver scaffolds. We developed an effective method for reestablishing the vascular network within decellularized liver scaffolds by conjugating anti-endothelial cell antibodies to maximize coverage of the vessel walls with endothelial cells. This procedure resulted in uniform endothelial attachment throughout the liver vasculature extending to the capillary bed of the liver scaffold and greatly reduced platelet adhesion upon blood perfusion in vitro. The re-endothelialized livers, when transplanted to recipient pigs, were able to withstand physiological blood flow and maintained for up to 24 h. This study demonstrates, for the first time, that vascularized bioengineered livers, of clinically relevant size, can be transplanted and maintained in vivo, and represents the first step towards generating engineered livers for transplantation to patients with end-stage liver failure. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identification of cofactors influencing hypertrophy of the future liver remnant after portal vein embolization-the effect of collaterals on embolized liver volume.

PubMed

Zeile, Martin; Bakal, Artur; Volkmer, Jan E; Stavrou, Gregor A; Dautel, Philip; Hoeltje, Jan; Stang, Axel; Oldhafer, Karl J; Brüning, Roland

2016-12-01

The purpose of this retrospective study was to monitor hypertrophy of future liver remnant following portal vein embolization (PVE) before planned extended right hepatectomy. However, because individual responses to PVE are highly variable, our focus was to identify cofactors of successful hypertrophy. 28 patients with primary or secondary liver tumours, mean age 64.1 ± 12.9 years, underwent PVE. Volumetric analysis of hypertrophy before and after PVE (median 39.0 ± 15.7 days) was performed. The embolized liver segments were investigated for occurrence of reperfusion of their portal branches. Blood parameters before PVE were additionally investigated. Patients were divided into responders (21/28) and non-responders (7/28) by post-PVE standardized future liver remnant being above or below 25%, respectively. No significant differences between the groups were found regarding biometric and volumetric parameters before PVE. In the entire group after PVE, the mean absolute increase of Segments 2 and 3 was 196.0 ± 84.7 cm 3 and the median relative increase was 46.6 ± 98.8%. The formation of left to right hepatic portoportal collaterals exhibited a negative correlation to successful hypertrophy (p = 0.004) as well as low plasma total protein (p = 0.019). Successful embolization of Segment IV showed only a trend to significance (p = 0.098). Cofactors associated with a favourable outcome regarding hypertrophy were the absence of collaterals in the control CT scans and high plasma total protein. Advances in knowledge: Portoportal collaterals negatively influence hypertrophy after PVE. On the other hand, plasma total protein is a positive prognostic indicator on hypertrophy of the liver in our cohort.

Identification of cofactors influencing hypertrophy of the future liver remnant after portal vein embolization—the effect of collaterals on embolized liver volume

PubMed Central

Bakal, Artur; Volkmer, Jan E; Stavrou, Gregor A; Dautel, Philip; Hoeltje, Jan; Stang, Axel; Oldhafer, Karl J; Brüning, Roland

2016-01-01

Objective: The purpose of this retrospective study was to monitor hypertrophy of future liver remnant following portal vein embolization (PVE) before planned extended right hepatectomy. However, because individual responses to PVE are highly variable, our focus was to identify cofactors of successful hypertrophy. Methods: 28 patients with primary or secondary liver tumours, mean age 64.1 ± 12.9 years, underwent PVE. Volumetric analysis of hypertrophy before and after PVE (median 39.0 ± 15.7 days) was performed. The embolized liver segments were investigated for occurrence of reperfusion of their portal branches. Blood parameters before PVE were additionally investigated. Results: Patients were divided into responders (21/28) and non-responders (7/28) by post-PVE standardized future liver remnant being above or below 25%, respectively. No significant differences between the groups were found regarding biometric and volumetric parameters before PVE. In the entire group after PVE, the mean absolute increase of Segments 2 and 3 was 196.0 ± 84.7 cm3 and the median relative increase was 46.6 ± 98.8%. The formation of left to right hepatic portoportal collaterals exhibited a negative correlation to successful hypertrophy (p = 0.004) as well as low plasma total protein (p = 0.019). Successful embolization of Segment IV showed only a trend to significance (p = 0.098). Conclusion: Cofactors associated with a favourable outcome regarding hypertrophy were the absence of collaterals in the control CT scans and high plasma total protein. Advances in knowledge: Portoportal collaterals negatively influence hypertrophy after PVE. On the other hand, plasma total protein is a positive prognostic indicator on hypertrophy of the liver in our cohort. PMID:27730840

Quantification and Statistical Analysis Methods for Vessel Wall Components from Stained Images with Masson's Trichrome

PubMed Central

Hernández-Morera, Pablo; Castaño-González, Irene; Travieso-González, Carlos M.; Mompeó-Corredera, Blanca; Ortega-Santana, Francisco

2016-01-01

Purpose To develop a digital image processing method to quantify structural components (smooth muscle fibers and extracellular matrix) in the vessel wall stained with Masson’s trichrome, and a statistical method suitable for small sample sizes to analyze the results previously obtained. Methods The quantification method comprises two stages. The pre-processing stage improves tissue image appearance and the vessel wall area is delimited. In the feature extraction stage, the vessel wall components are segmented by grouping pixels with a similar color. The area of each component is calculated by normalizing the number of pixels of each group by the vessel wall area. Statistical analyses are implemented by permutation tests, based on resampling without replacement from the set of the observed data to obtain a sampling distribution of an estimator. The implementation can be parallelized on a multicore machine to reduce execution time. Results The methods have been tested on 48 vessel wall samples of the internal saphenous vein stained with Masson’s trichrome. The results show that the segmented areas are consistent with the perception of a team of doctors and demonstrate good correlation between the expert judgments and the measured parameters for evaluating vessel wall changes. Conclusion The proposed methodology offers a powerful tool to quantify some components of the vessel wall. It is more objective, sensitive and accurate than the biochemical and qualitative methods traditionally used. The permutation tests are suitable statistical techniques to analyze the numerical measurements obtained when the underlying assumptions of the other statistical techniques are not met. PMID:26761643

Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

PubMed Central

Cai, Wei; He, Baochun; Fang, Chihua

2016-01-01

This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods— one interactive method, an in‐house‐developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)‐based segmentation, and one automatic probabilistic atlas (PA)‐guided segmentation method on clinical contrast‐enhanced CT images. Forty‐two datasets, including 27 normal liver and 15 space‐occupying liver lesion patients, were retrospectively included in this study. The three methods — one semiautomatic 3DMIA, one automatic ASM‐based, and one automatic PA‐based liver volumetry — achieved an accuracy with VD (volume difference) of −1.69%,−2.75%, and 3.06% in the normal group, respectively, and with VD of −3.20%,−3.35%, and 4.14% in the space‐occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excellent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p<0.001), as well as between the automatic volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p<0.001). The semiautomatic interactive 3DMIA, automatic ASM‐based, and automatic PA‐based liver volumetry agreed well with manual gold standard in both the normal liver group and the space‐occupying lesion group. The ASM‐ and PA‐based automatic segmentation have better efficiency in clinical use. PACS number(s): 87.55.‐x PMID:27929487

Liver hanging maneuver for right hemiliver in situ donation – anatomical considerations

PubMed Central

Gadžijev, E.M.; Ravnik, D.; Hribernik, M.

2006-01-01

Background. An anatomical study was carried out to evaluate the safety of the liver hanging maneuver for the right hemiliver in living donor and in situ splitting transplantation. During this procedure a 4–6 cm blind dissection is performed between the inferior vena cava and the liver. Short subhepatic veins entering the inferior vena cava from segments 1 and 9 could be torn with consequent hemorrhage. Materials and methods. One hundred corrosive casts of livers were evaluated to establish the position and diameter of short subhepatic veins and the inferior right hepatic vein. Results. The average distance from the right border of the inferior vena cava to the opening of segment 1 veins was 16.7±3.4 mm and to the entrance of segment 9 veins was 5.0±0.5 mm. The width of the narrowest point on the route of blind dissection was determined, with the average value being 8.7±2.3 mm (range 2–15 mm). Discussion. The results show that the liver hanging maneuver is a safe procedure. A proposed route of dissection minimizes the risk of disrupting short subhepatic veins (7%). PMID:18333236

Automatic retinal blood vessel parameter calculation in spectral domain optical coherence tomography

NASA Astrophysics Data System (ADS)

Wehbe, Hassan; Ruggeri, Marco; Jiao, Shuliang; Gregori, Giovanni; Puliafito, Carmen A.

2007-02-01

Measurement of retinal blood vessel parameters like the blood blow in the vessels may have significant impact on the study and diagnosis of glaucoma, a leading blinding disease worldwide. Optical coherence tomography (OCT) is a noninvasive imaging technique that can provide not only microscopic structural imaging of the retina but also functional information like the blood flow velocity in the retina. The aim of this study is to automatically extract the parameters of retinal blood vessels like the 3D orientation, the vessel diameters, as well as the corresponding absolute blood flow velocity in the vessel. The parameters were extracted from circular OCT scans around the optic disc. By removing the surface reflection through simple segmentation of the circular OCT scans a blood vessel shadowgram can be generated. The lateral coordinates and the diameter of each blood vessel are extracted from the shadowgram through a series of signal processing. Upon determination of the lateral position and the vessel diameter, the coordinate in the depth direction of each blood vessel is calculated in combination with the Doppler information for the vessel. The extraction of the vessel coordinates and diameter makes it possible to calculate the orientation of the vessel in reference to the direction of the incident sample light, which in turn can be used to calculate the absolute blood flow velocity and the flow rate.

Carotid-vertebrobasilar Anastomoses with Reference to Their Segmental Property.

PubMed

Namba, Katsunari

2017-06-15

The primitive carotid-vertebrobasilar anastomoses are primitive embryonic cerebral vessels that temporarily provide arterial supply from the internal carotid artery to the longitudinal neural artery, the future vertebrobasilar artery in the hindbrain. Four types known are the trigeminal, otic, hypoglossal, and proatlantal intersegmental arteries. The arteries are accompanied by their corresponding nerves and resemble an intersegmental pattern. These vessels exist in the very early period of cerebral arterial development and rapidly involute within a week. Occasionally, persistence of the carotid to vertebrobasilar anastomosis is discovered in the adult period, and is considered as the vestige of the corresponding primitive embryonic vessel. The embryonic development and the segmental property of the primitive carotid-vertebrobasilar anastomoses are discussed. This is followed by a brief description of the persisting anastomoses in adults.

Medical image segmentation by combining graph cuts and oriented active appearance models.

PubMed

Chen, Xinjian; Udupa, Jayaram K; Bagci, Ulas; Zhuge, Ying; Yao, Jianhua

2012-04-01

In this paper, we propose a novel method based on a strategic combination of the active appearance model (AAM), live wire (LW), and graph cuts (GCs) for abdominal 3-D organ segmentation. The proposed method consists of three main parts: model building, object recognition, and delineation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the recognition part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW methods, resulting in the oriented AAM (OAAM). A multiobject strategy is utilized to help in object initialization. We employ a pseudo-3-D initialization strategy and segment the organs slice by slice via a multiobject OAAM method. For the object delineation part, a 3-D shape-constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT data set and also on the MICCAI 2007 Grand Challenge liver data set. The results show the following: 1) The overall segmentation accuracy of true positive volume fraction TPVF > 94.3% and false positive volume fraction can be achieved; 2) the initialization performance can be improved by combining the AAM and LW; 3) the multiobject strategy greatly facilitates initialization; 4) compared with the traditional 3-D AAM method, the pseudo-3-D OAAM method achieves comparable performance while running 12 times faster; and 5) the performance of the proposed method is comparable to state-of-the-art liver segmentation algorithm. The executable version of the 3-D shape-constrained GC method with a user interface can be downloaded from http://xinjianchen.wordpress.com/research/.

Laparoscopic liver resection: Experience based guidelines

PubMed Central

Coelho, Fabricio Ferreira; Kruger, Jaime Arthur Pirola; Fonseca, Gilton Marques; Araújo, Raphael Leonardo Cunha; Jeismann, Vagner Birk; Perini, Marcos Vinícius; Lupinacci, Renato Micelli; Cecconello, Ivan; Herman, Paulo

2016-01-01

Laparoscopic liver resection (LLR) has been progressively developed along the past two decades. Despite initial skepticism, improved operative results made laparoscopic approach incorporated to surgical practice and operations increased in frequency and complexity. Evidence supporting LLR comes from case-series, comparative studies and meta-analysis. Despite lack of level 1 evidence, the body of literature is stronger and existing data confirms the safety, feasibility and benefits of laparoscopic approach when compared to open resection. Indications for LLR do not differ from those for open surgery. They include benign and malignant (both primary and metastatic) tumors and living donor liver harvesting. Currently, resection of lesions located on anterolateral segments and left lateral sectionectomy are performed systematically by laparoscopy in hepatobiliary specialized centers. Resection of lesions located on posterosuperior segments (1, 4a, 7, 8) and major liver resections were shown to be feasible but remain technically demanding procedures, which should be reserved to experienced surgeons. Hand-assisted and laparoscopy-assisted procedures appeared to increase the indications of minimally invasive liver surgery and are useful strategies applied to difficult and major resections. LLR proved to be safe for malignant lesions and offers some short-term advantages over open resection. Oncological results including resection margin status and long-term survival were not inferior to open resection. At present, surgical community expects high quality studies to base the already perceived better outcomes achieved by laparoscopy in major centers’ practice. Continuous surgical training, as well as new technologies should augment the application of laparoscopic liver surgery. Future applicability of new technologies such as robot assistance and image-guided surgery is still under investigation. PMID:26843910

Forest representation of vessels in cone-beam computed tomographic angiography.

PubMed

Chen, Zikuan; Ning, Ruola

2005-01-01

Cone-beam computed tomographic angiography (CBCTA) provides a fast three-dimensional (3D) vascular imaging modality, aiming at digitally representing the spatial vascular structure in an angiographic volume. Due to the finite coverage of cone-beam scan, as well as the volume cropping in volumetric image processing, an angiographic volume may fail to contain a whole vascular tree, but rather consist of a multitude of vessel segments or subtrees. As such, it is convenient to represent multitudinal components by a forest. The vessel tracking issue then becomes component characterization/identification in the forest. The forest representation brings several conveniences for vessel tracking: (1) to sort and count the vessels in an angiographic volume, for example, according to spatial occupancy and skeleton pathlength; (2) to single out a vessel and perform in situ 3D measurement and 3D visualization in the support space; (3) to delineate individual vessels from the original angiographic volume; and (4) to cull the forest by getting rid of non-vessels and small vessels. A 3D skeletonization is used to generate component skeletons. For tree construction from skeletons, we suggest a pathlength-based procedure, which lifts the restrictions of unit-width skeleton and root determination. We experimentally demonstrate the forest representation of a dog's carotid arteries in a CBCTA system. In principle, the forest representation is useful for managing vessels in both 2D angiographic images and 3D angiographic volumes.

Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification

PubMed Central

2017-01-01

Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatment of systematic diseases. In this paper, a novel approach is developed to extract retinal blood vessel network. Our method comprises four stages: (1) preprocessing stage in order to prepare dataset for segmentation; (2) an enhancement procedure including Gabor, Frangi, and Gauss filters obtained separately before a top-hat transform; (3) a hard and soft clustering stage which includes K-means and Fuzzy C-means (FCM) in order to get binary vessel map; and (4) a postprocessing step which removes falsely segmented isolated regions. The method is tested on color retinal images obtained from STARE and DRIVE databases which are available online. As a result, Gabor filter followed by K-means clustering method achieves 95.94% and 95.71% of accuracy for STARE and DRIVE databases, respectively, which are acceptable for diagnosis systems. PMID:29065611

Novel Computerized Method for Measurement of Retinal Vessel Diameters

PubMed Central

Guedri, Hichem; Ben Abdallah, Mariem; Echouchene, Fraj; Belmabrouk, Hafedh

2017-01-01

Several clinical studies reveal the relationship between alterations in the topologies of the human retinal blood vessel, the outcrop and the disease evolution, such as diabetic retinopathy, hypertensive retinopathy, and macular degeneration. Indeed, the detection of these vascular changes always has gaps. In addition, the manual steps are slow, which may be subjected to a bias of the perceiver. However, we can overcome these troubles using computer algorithms that are quicker and more accurate. This paper presents and investigates a novel method for measuring the blood vessel diameter in the retinal image. The proposed method is based on a thresholding segmentation and thinning step, followed by the characteristic point determination step by the Douglas-Peucker algorithm. Thereafter, it uses the active contours to detect vessel contour. Finally, Heron’s Formula is applied to assure the calculation of vessel diameter. The obtained results for six sample images showed that the proposed method generated less errors compared to other techniques, which confirms the high performance of the proposed method. PMID:28536355

Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation.

PubMed

Na, Tong; Xie, Jianyang; Zhao, Yitian; Zhao, Yifan; Liu, Yue; Wang, Yongtian; Liu, Jiang

2018-05-09

Automatic methods of analyzing of retinal vascular networks, such as retinal blood vessel detection, vascular network topology estimation, and arteries/veins classification are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide spectrum of diseases. We propose a new framework for precisely segmenting retinal vasculatures, constructing retinal vascular network topology, and separating the arteries and veins. A nonlocal total variation inspired Retinex model is employed to remove the image intensity inhomogeneities and relatively poor contrast. For better generalizability and segmentation performance, a superpixel-based line operator is proposed as to distinguish between lines and the edges, thus allowing more tolerance in the position of the respective contours. The concept of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel network into arteries and veins. The proposed segmentation method yields competitive results on three public data sets (STARE, DRIVE, and IOSTAR), and it has superior performance when compared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964, respectively. The topology estimation approach has been applied to five public databases (DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830, 0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries/veins classification based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and VICAVR) are 0.90.9, 0.910, and 0.907, respectively. The experimental results show that the proposed framework has effectively addressed crossover problem, a bottleneck issue in segmentation and vascular topology reconstruction. The vascular topology information significantly improves the accuracy on arteries/veins classification. © 2018 American Association of Physicists in Medicine.

Comprehensive evaluation of an image segmentation technique for measuring tumor volume from CT images

NASA Astrophysics Data System (ADS)

Deng, Xiang; Huang, Haibin; Zhu, Lei; Du, Guangwei; Xu, Xiaodong; Sun, Yiyong; Xu, Chenyang; Jolly, Marie-Pierre; Chen, Jiuhong; Xiao, Jie; Merges, Reto; Suehling, Michael; Rinck, Daniel; Song, Lan; Jin, Zhengyu; Jiang, Zhaoxia; Wu, Bin; Wang, Xiaohong; Zhang, Shuai; Peng, Weijun

2008-03-01

Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation. In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200 tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can provide complementary information regarding the quality of the segmentation results. The reproducibility was measured by the variation of the volume measurements from 10 independent segmentations. The effect of disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale evaluation of segmentation techniques for other clinical applications.

CT liver volumetry using three-dimensional image data in living donor liver transplantation: Effects of slice thickness on volume calculation

PubMed Central

Hori, Masatoshi; Suzuki, Kenji; Epstein, Mark L.; Baron, Richard L.

2011-01-01

The purpose was to evaluate a relationship between slice thickness and calculated volume on CT liver volumetry by comparing the results for images with various slice thicknesses including three-dimensional images. Twenty adult potential liver donors (12 men, 8 women; mean age, 39 years; range, 24–64) underwent CT with a 64-section multi-detector row CT scanner after intra-venous injection of contrast material. Four image sets with slice thicknesses of 0.625 mm, 2.5 mm, 5 mm, and 10 mm were used. First, a program developed in our laboratory for automated liver extraction was applied to CT images, and the liver boundary was obtained automatically. Then, an abdominal radiologist reviewed all images on which automatically extracted boundaries were superimposed, and edited the boundary on each slice to enhance the accuracy. Liver volumes were determined by counting of the voxels within the liver boundary. Mean whole liver volumes estimated with CT were 1322.5 cm3 on 0.625-mm, 1313.3 cm3 on 2.5-mm, 1310.3 cm3 on 5-mm, and 1268.2 cm3 on 10-mm images. Volumes calculated for three-dimensional (0.625-mm-thick) images were significantly larger than those for thicker images (P<.0001). Partial liver volumes of right lobe, left lobe, and lateral segment were also evaluated in a similar manner. Estimated maximum differences in calculated volumes of lateral segment was −10.9 cm3 (−4.6%) between 0.625-mm and 5-mm images. In conclusion, liver volumes calculated on 2.5-mm or thicker images were significantly smaller than volumes calculated on three-dimensional images. If a maximum error of 5% in the calculated graft volume is within the range of having an insignificant clinical impact, 5-mm thick images are acceptable for CT volumetry. If not, three-dimensional images could be essential. PMID:21850689

Technical aspects of virtual liver resection planning.

PubMed

Glombitza, G; Lamadé, W; Demiris, A M; Göpfert, M R; Mayer, A; Bahner, M L; Meinzer, H P; Richter, G; Lehnert, T; Herfarth, C

1998-01-01

Operability of a liver tumor is depending on its three dimensional relation to the intrahepatic vascular trees which define autonomously functioning liver (sub-)segments. Precise operation planning is complicated by anatomic variability, distortion of the vascular trees by the tumor or preceding liver resections. Because of the missing possibility to track the deformation of the liver during the operation an integration of the resection planning system into an intra-operative navigation system is not feasible. So the main task of an operation planning system in this domain is a quantifiable patient selection by exact prediction of post-operative liver function and a quantifiable resection proposal. The system quantifies the organ structures and resection volumes by means of absolute and relative values. It defines resection planes depending on security margins and the vascular trees and presents the data in visualized form as a 3D movie. The new 3D operation planning system offers quantifiable liver resection proposals based on individualized liver anatomy. The results are visualized in digital movies as well as in quantitative reports.

Comparative study on the performance of textural image features for active contour segmentation.

PubMed

Moraru, Luminita; Moldovanu, Simona

2012-07-01

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

Automated measurement of uptake in cerebellum, liver, and aortic arch in full-body FDG PET/CT scans.

PubMed

Bauer, Christian; Sun, Shanhui; Sun, Wenqing; Otis, Justin; Wallace, Audrey; Smith, Brian J; Sunderland, John J; Graham, Michael M; Sonka, Milan; Buatti, John M; Beichel, Reinhard R

2012-06-01

The purpose of this work was to develop and validate fully automated methods for uptake measurement of cerebellum, liver, and aortic arch in full-body PET/CT scans. Such measurements are of interest in the context of uptake normalization for quantitative assessment of metabolic activity and/or automated image quality control. Cerebellum, liver, and aortic arch regions were segmented with different automated approaches. Cerebella were segmented in PET volumes by means of a robust active shape model (ASM) based method. For liver segmentation, a largest possible hyperellipsoid was fitted to the liver in PET scans. The aortic arch was first segmented in CT images of a PET/CT scan by a tubular structure analysis approach, and the segmented result was then mapped to the corresponding PET scan. For each of the segmented structures, the average standardized uptake value (SUV) was calculated. To generate an independent reference standard for method validation, expert image analysts were asked to segment several cross sections of each of the three structures in 134 F-18 fluorodeoxyglucose (FDG) PET/CT scans. For each case, the true average SUV was estimated by utilizing statistical models and served as the independent reference standard. For automated aorta and liver SUV measurements, no statistically significant scale or shift differences were observed between automated results and the independent standard. In the case of the cerebellum, the scale and shift were not significantly different, if measured in the same cross sections that were utilized for generating the reference. In contrast, automated results were scaled 5% lower on average although not shifted, if FDG uptake was calculated from the whole segmented cerebellum volume. The estimated reduction in total SUV measurement error ranged between 54.7% and 99.2%, and the reduction was found to be statistically significant for cerebellum and aortic arch. With the proposed methods, the authors have demonstrated that

A novel retinal vessel extraction algorithm based on matched filtering and gradient vector flow

NASA Astrophysics Data System (ADS)

Yu, Lei; Xia, Mingliang; Xuan, Li

2013-10-01

The microvasculature network of retina plays an important role in the study and diagnosis of retinal diseases (age-related macular degeneration and diabetic retinopathy for example). Although it is possible to noninvasively acquire high-resolution retinal images with modern retinal imaging technologies, non-uniform illumination, the low contrast of thin vessels and the background noises all make it difficult for diagnosis. In this paper, we introduce a novel retinal vessel extraction algorithm based on gradient vector flow and matched filtering to segment retinal vessels with different likelihood. Firstly, we use isotropic Gaussian kernel and adaptive histogram equalization to smooth and enhance the retinal images respectively. Secondly, a multi-scale matched filtering method is adopted to extract the retinal vessels. Then, the gradient vector flow algorithm is introduced to locate the edge of the retinal vessels. Finally, we combine the results of matched filtering method and gradient vector flow algorithm to extract the vessels at different likelihood levels. The experiments demonstrate that our algorithm is efficient and the intensities of vessel images exactly represent the likelihood of the vessels.

Visualizing blood vessel trees in three dimensions: clinical applications

NASA Astrophysics Data System (ADS)

Bullitt, Elizabeth; Aylward, Stephen

2005-04-01

A connected network of blood vessels surrounds and permeates almost every organ of the human body. The ability to define detailed blood vessel trees enables a variety of clinical applications. This paper discusses four such applications and some of the visualization challenges inherent to each. Guidance of endovascular surgery: 3D vessel trees offer important information unavailable by traditional x-ray projection views. How best to combine the 2- and 3D image information is unknown. Planning/guidance of tumor surgery: During tumor resection it is critical to know which blood vessels can be interrupted safely and which cannot. Providing efficient, clear information to the surgeon together with measures of uncertainty in both segmentation and registration can be a complex problem. Vessel-based registration: Vessel-based registration allows pre-and intraoperative images to be registered rapidly. The approach both provides a potential solution to a difficult clinical dilemma and offers a variety of visualization opportunities. Diagnosis/staging of disease: Almost every disease affects blood vessel morphology. The statistical analysis of vessel shape may thus prove to be an important tool in the noninvasive analysis of disease. A plethora of information is available that must be presented meaningfully to the clinician. As medical image analysis methods increase in sophistication, an increasing amount of useful information of varying types will become available to the clinician. New methods must be developed to present a potentially bewildering amount of complex data to individuals who are often accustomed to viewing only tissue slices or flat projection views.

Pulmonary embolism detection using localized vessel-based features in dual energy CT

NASA Astrophysics Data System (ADS)

Dicente Cid, Yashin; Depeursinge, Adrien; Foncubierta Rodríguez, Antonio; Platon, Alexandra; Poletti, Pierre-Alexandre; Müller, Henning

2015-03-01

Pulmonary embolism (PE) affects up to 600,000 patients and contributes to at least 100,000 deaths every year in the United States alone. Diagnosis of PE can be difficult as most symptoms are unspecific and early diagnosis is essential for successful treatment. Computed Tomography (CT) images can show morphological anomalies that suggest the existence of PE. Various image-based procedures have been proposed for improving computer-aided diagnosis of PE. We propose a novel method for detecting PE based on localized vessel-based features computed in Dual Energy CT (DECT) images. DECT provides 4D data indexed by the three spatial coordinates and the energy level. The proposed features encode the variation of the Hounsfield Units across the different levels and the CT attenuation related to the amount of iodine contrast in each vessel. A local classification of the vessels is obtained through the classification of these features. Moreover, the localization of the vessel in the lung provides better comparison between patients. Results show that the simple features designed are able to classify pulmonary embolism patients with an AUC (area under the receiver operating curve) of 0.71 on a lobe basis. Prior segmentation of the lung lobes is not necessary because an automatic atlas-based segmentation obtains similar AUC levels (0.65) for the same dataset. The automatic atlas reaches 0.80 AUC in a larger dataset with more control cases.

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

PubMed

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

2018-05-01

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

Determination of vessel cross-sectional area by thresholding in Radon space

PubMed Central

Gao, Yu-Rong; Drew, Patrick J

2014-01-01

The cross-sectional area of a blood vessel determines its resistance, and thus is a regulator of local blood flow. However, the cross-sections of penetrating vessels in the cortex can be non-circular, and dilation and constriction can change the shape of the vessels. We show that observed vessel shape changes can introduce large errors in flux calculations when using a single diameter measurement. Because of these shape changes, typical diameter measurement approaches, such as the full-width at half-maximum (FWHM) that depend on a single diameter axis will generate erroneous results, especially when calculating flux. Here, we present an automated method—thresholding in Radon space (TiRS)—for determining the cross-sectional area of a convex object, such as a penetrating vessel observed with two-photon laser scanning microscopy (2PLSM). The thresholded image is transformed back to image space and contiguous pixels are segmented. The TiRS method is analogous to taking the FWHM across multiple axes and is more robust to noise and shape changes than FWHM and thresholding methods. We demonstrate the superior precision of the TiRS method with in vivo 2PLSM measurements of vessel diameter. PMID:24736890

Brain vascular image segmentation based on fuzzy local information C-means clustering

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Liu, Xia; Liang, Xiao; Hui, Hui; Yang, Xin; Tian, Jie

2017-02-01

Light sheet fluorescence microscopy (LSFM) is a powerful optical resolution fluorescence microscopy technique which enables to observe the mouse brain vascular network in cellular resolution. However, micro-vessel structures are intensity inhomogeneity in LSFM images, which make an inconvenience for extracting line structures. In this work, we developed a vascular image segmentation method by enhancing vessel details which should be useful for estimating statistics like micro-vessel density. Since the eigenvalues of hessian matrix and its sign describes different geometric structure in images, which enable to construct vascular similarity function and enhance line signals, the main idea of our method is to cluster the pixel values of the enhanced image. Our method contained three steps: 1) calculate the multiscale gradients and the differences between eigenvalues of Hessian matrix. 2) In order to generate the enhanced microvessels structures, a feed forward neural network was trained by 2.26 million pixels for dealing with the correlations between multi-scale gradients and the differences between eigenvalues. 3) The fuzzy local information c-means clustering (FLICM) was used to cluster the pixel values in enhance line signals. To verify the feasibility and effectiveness of this method, mouse brain vascular images have been acquired by a commercial light-sheet microscope in our lab. The experiment of the segmentation method showed that dice similarity coefficient can reach up to 85%. The results illustrated that our approach extracting line structures of blood vessels dramatically improves the vascular image and enable to accurately extract blood vessels in LSFM images.

Anatomic Peculiarities of Pig and Human Liver.

PubMed

Nykonenko, Andriy; Vávra, Petr; Zonča, Pavel

2017-02-01

Many investigations on surgical methods and medical treatment are currently done on pigs. This is possible because the pig is sufficiently close genetically to humans. In recent years, progress in liver surgery has opened new possibilities in surgical treatment of liver diseases. Because the methods are relatively novel, various improvements are still needed, and it is thus helpful to conduct experimental surgeries on pig livers. We reviewed the literature to compare the anatomic and functional features of pig and human livers, information that will be of great importance for improving surgical techniques. During the literature review, we used various sources, such as PubMed, Scopus, and veterinary journals. Our results were summarized in diagrams to facilitate understanding of the vascular structure and biliary systems. We conclude that, although the shapes of the human and pig livers are quite different, the pig liver is divided into the same number of segments as the human liver, which also shows a common structure of the vascular system. Thus, with the anatomic and structural features of the pig liver taken into account, this animal model can be used in experimental hepatic surgery.

[Application of hepatic segment resection combined with rigid choledochoscope in the treatment of complex hepatolithiasis guided by three-dimensional visualization technology].

PubMed

Xiang, Nan; Fang, Chihua

2015-05-01

To study the value of hepatic segment resection combined with rigid choledochoscope by the three-dimensional (3D) visualization technology in the diagnosis and treatment of complex hepatolithiasis. Enhance computed tomography (CT) data of 46 patients with complex hepatolithiasis who were admitted to the Zhujiang Hospital of the Southern Medical University from July 2010 to June 2014 were collected.All of the CT data were imported into the medical image three-dimensional visualization system (MI-3DVS) for 3D reconstruction and individual 3D types. The optimal scope of liver resection and the remnant liver volume were determined according to the individualized liver segments which were made via the distribution and variation of hepatic vein and portal vein, the distribution of bile duct stones and stricture of the bile duct, which provided guidance for intraoperative hepatic lobectomy and rigid choledochoscope for the remnant calculus lithotripsy. Outcomes of individual 3D types: 10 cases of type I, 11 cases of IIa, 23 cases of IIb, 2 cases of IIc, 19 cases coexisted with history of biliary surgery. The variation of hepatic artery was appeared 6 cases. The variation of portal vein was appeared 8 cases. The remaining liver volume for virtual hepatic lobectomy controlled more than 50%. Eighteen cases underwent left lateral hepatectomy, 8 cases underwent left liver resection, 8 cases underwent right posterior lobe of liver resection, 4 cases underwent the right hepatic resection, 4 cases underwent IV segment liver resection, 2 cases underwent right anterior lobe of liver resection, 2 cases underwent left lateral hepatectomy combined with right posterior lobe of liver resection, 26 cases underwent targeting treatment of rapid choledochoscope and preumatic lithotripsy. The actual surgical procedure was consistent with the preoperative surgical planning. There was no postoperative residual liver ischemia,congestion, liver failure occurred in this study. The intraoperative

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. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Quantification of pulmonary vessel diameter in low-dose CT images

NASA Astrophysics Data System (ADS)

Rudyanto, Rina D.; Ortiz de Solórzano, Carlos; Muñoz-Barrutia, Arrate

2015-03-01

Accurate quantification of vessel diameter in low-dose Computer Tomography (CT) images is important to study pulmonary diseases, in particular for the diagnosis of vascular diseases and the characterization of morphological vascular remodeling in Chronic Obstructive Pulmonary Disease (COPD). In this study, we objectively compare several vessel diameter estimation methods using a physical phantom. Five solid tubes of differing diameters (from 0.898 to 3.980 mm) were embedded in foam, simulating vessels in the lungs. To measure the diameters, we first extracted the vessels using either of two approaches: vessel enhancement using multi-scale Hessian matrix computation, or explicitly segmenting them using intensity threshold. We implemented six methods to quantify the diameter: three estimating diameter as a function of scale used to calculate the Hessian matrix; two calculating equivalent diameter from the crosssection area obtained by thresholding the intensity and vesselness response, respectively; and finally, estimating the diameter of the object using the Full Width Half Maximum (FWHM). We find that the accuracy of frequently used methods estimating vessel diameter from the multi-scale vesselness filter depends on the range and the number of scales used. Moreover, these methods still yield a significant error margin on the challenging estimation of the smallest diameter (on the order or below the size of the CT point spread function). Obviously, the performance of the thresholding-based methods depends on the value of the threshold. Finally, we observe that a simple adaptive thresholding approach can achieve a robust and accurate estimation of the smallest vessels diameter.

A novel pipeline for adrenal tumour segmentation.

PubMed

Koyuncu, Hasan; Ceylan, Rahime; Erdogan, Hasan; Sivri, Mesut

2018-06-01

Adrenal tumours occur on adrenal glands surrounded by organs and osteoid. These tumours can be categorized as either functional, non-functional, malign, or benign. Depending on their appearance in the abdomen, adrenal tumours can arise from one adrenal gland (unilateral) or from both adrenal glands (bilateral) and can connect with other organs, including the liver, spleen, pancreas, etc. This connection phenomenon constitutes the most important handicap against adrenal tumour segmentation. Size change, variety of shape, diverse location, and low contrast (similar grey values between the various tissues) are other disadvantages compounding segmentation difficulty. Few studies have considered adrenal tumour segmentation, and no significant improvement has been achieved for unilateral, bilateral, adherent, or noncohesive tumour segmentation. There is also no recognised segmentation pipeline or method for adrenal tumours including different shape, size, or location information. This study proposes an adrenal tumour segmentation (ATUS) pipeline designed to eliminate the above disadvantages for adrenal tumour segmentation. ATUS incorporates a number of image methods, including contrast limited adaptive histogram equalization, split and merge based on quadtree decomposition, mean shift segmentation, large grey level eliminator, and region growing. Performance assessment of ATUS was realised on 32 arterial and portal phase computed tomography images using six metrics: dice, jaccard, sensitivity, specificity, accuracy, and structural similarity index. ATUS achieved remarkable segmentation performance, and was not affected by the discussed handicaps, on particularly adherence to other organs, with success rates of 83.06%, 71.44%, 86.44%, 99.66%, 99.43%, and 98.51% for the metrics, respectively, for images including sufficient contrast uptake. The proposed ATUS system realises detailed adrenal tumour segmentation, and avoids known disadvantages preventing accurate

Localization of liver tumors in freehand 3D laparoscopic ultrasound

NASA Astrophysics Data System (ADS)

Shahin, O.; Martens, V.; Besirevic, A.; Kleemann, M.; Schlaefer, A.

2012-02-01

The aim of minimally invasive laparoscopic liver interventions is to completely resect or ablate tumors while minimizing the trauma caused by the operation. However, restrictions such as limited field of view and reduced depth perception can hinder the surgeon's capabilities to precisely localize the tumor. Typically, preoperative data is acquired to find the tumor(s) and plan the surgery. Nevertheless, determining the precise position of the tumor is required, not only before but also during the operation. The standard use of ultrasound in hepatic surgery is to explore the liver and identify tumors. Meanwhile, the surgeon mentally builds a 3D context to localize tumors. This work aims to upgrade the use of ultrasound in laparoscopic liver surgery. We propose an approach to segment and localize tumors intra-operatively in 3D ultrasound. We reconstruct a 3D laparoscopic ultrasound volume containing a tumor. The 3D image is then preprocessed and semi-automatically segmented using a level set algorithm. During the surgery, for each subsequent reconstructed volume, a fast update of the tumor position is accomplished via registration using the previously segmented and localized tumor as a prior knowledge. The approach was tested on a liver phantom with artificial tumors. The tumors were localized in approximately two seconds with a mean error of less than 0.5 mm. The strengths of this technique are that it can be performed intra-operatively, it helps the surgeon to accurately determine the location, shape and volume of the tumor, and it is repeatable throughout the operation.

In vivo label-free lymphangiography of cutaneous lymphatic vessels in human burn scars using optical coherence tomography

PubMed Central

Gong, Peijun; Es’haghian, Shaghayegh; Harms, Karl-Anton; Murray, Alexandra; Rea, Suzanne; Wood, Fiona M.; Sampson, David D.; McLaughlin, Robert A.

2016-01-01

We present an automated, label-free method for lymphangiography of cutaneous lymphatic vessels in humans in vivo using optical coherence tomography (OCT). This method corrects for the variation in OCT signal due to the confocal function and sensitivity fall-off of a spectral-domain OCT system and utilizes a single-scattering model to compensate for A-scan signal attenuation to enable reliable thresholding of lymphatic vessels. A segment-joining algorithm is then incorporated into the method to mitigate partial-volume effects with small vessels. The lymphatic vessel images are augmented with images of the blood vessel network, acquired from the speckle decorrelation with additional weighting to differentiate blood vessels from the observed high decorrelation in lymphatic vessels. We demonstrate the method with longitudinal scans of human burn scar patients undergoing ablative fractional laser treatment, showing the visualization of the cutaneous lymphatic and blood vessel networks. PMID:28018713

[Pathophysiological changes of umbilical vessels in intrauterine growth restriction].

PubMed

Jakó, Mária; Surányi, Andrea; Kaiser, László; Domokos, Dóra; Gáspár, Róbert; Bártfai, György

2014-12-14

The prevalence of intrauterine growth restriction is 4-5000/100,000 births, and they give the majority of perinatal morbidity. The aim of the authors was to compare the pathomorphologic data and vasoreactivity of umbilical vessels and placenta of small for date newborns to that of the normal pregnancies. Samples of the umbilical cord and placenta were divided into case and control groups. Two 10 cm long segments were cut of the umbilical cord at placental insertion. Tissue bath experiment was performed on umbilical vessels and pathomorphologic data were collected according to the Royal College of Pathologists' protocol. After the development of basal tone, oxytocin and desmopressin did not enhance the vascular contraction, but the pathomorphological and ultrasonographic data were significantly different in the two groups. The results indicate that umbilical vessels might not have oxytocin or vasopressin receptors. The pathomorphologic and flowmetric differences could be the causes of small birth weight.

[Liver Abscess Secondary to Perforation after Duodenal Endoscopic Resection].

PubMed

Choi, Seung Ho; Kim, Su Jin; Kang, Dae Hwan; Kim, Hyung Wook; Choi, Cheol Woong; Kim, Tae Un; Lee, Jeong Seok; Ko, Ji Hwan

2018-05-25

Duodenal perforation is a complication of endoscopic mucosal resection. Liver abscess secondary to iatrogenic perforation is extremely rare. A 43-year-old female visited the hospital to remove a sub-epithelial tumor on the duodenal bulb. After endoscopic mucosal resection with band ligation, duodenal perforation occurred. Endoscopic closure was performed successfully using a clipping device to manage duodenal perforation. After 4 weeks, the patient visited our outpatient clinic due to abdominal pain and fever. Abdominal computed tomography showed liver abscess that involved segment three. Liver abscess was resolved with a 10-week antibiotic treatment. To the best of our knowledge, no case of liver abscess secondary to duodenal perforation by endoscopic resection was reported to date in Korea. Here, we report a case of liver abscess caused by a duodenal perforation after endoscopic mucosal resection.

[Morphological pathology of vessels in granulomatosis with polyangiitis (Wegener's disease)].

PubMed

Zerbino, D D; Zimba, E A

2015-01-01

to investigate the incidence of injuries in different vascular beds and the morphopathological changes in vessels in granulomatosis with polyangiitis. The morphopathological features of vascular injuries were investigated in 11 dead patients aged 16--74 years with granulomatosis with polyangiitis. Proliferative and destructive angiitis with predominant involvement of microcirculatory vessels and with development of necrosis-prone granulomas in their walls and perivascularly was established to underlie the clinical manifestations of granulomatosis with polyangiitis. The most typical localization of the pathologic process is the vessels of the upper respiratory tract, lungs, and kidneys. Cardiopulmonary and renal failures are causes of death in the majority of cases. It should be noted that the vessels of the heart, liver, and gastrointestinal tract are frequently involved in the pathological process. Vascular changes in these organs determine the clinical features of granulomatosis with polyangiitis and lead to a number of fatal complications. Granulomatosis with polyangiitis is a systemic disease with polymorphism of clinical manifestations, which requires in-depth analysis based on current precision patient examination methods, including a histopathological study.

[The clinical advantage of using three dimensional visualization technology in hepatic surgery].

PubMed

Lau, Y Y; Lau, X X

2016-09-01

The three-dimensional body visible system is a further development of the three-dimensional CT reconstruction system. It has a lot of merits over the latter system. Clinical application of the three-dimensional body visible system in liver surgery showed the system to have the following merits: (1) The system can support the Couinaud classification of liver anatomy into two hemilivers, four sectors and eight segments. As the system can rotate the liver to any angle and it has the ability to make part or whole of the liver transparent thus making the internal blood vessels and bile ducts visible. Learning liver anatomy and liver surgery becomes easier. (2)The system can clearly localize liver tumors within the liver segment(s). (3)It can help clinicians to decide and to plan different operations on an individual. (4)By carrying out simulation partial hepatectomy using this system, it can help clinicians to estimate the difficulty and the risks involved in different options of liver resection and finally.(5)The system helps clinicians to identify anomalies in hepatic artery, portal vein, hepatic vein and bile duct, thus making the operation safer. In conclusion, this system significantly improves on the conventional three-dimensional CT reconstruction system. It is especially useful for inexperienced liver surgeons.

Fractional flow reserve of non-culprit vessel post-myocardial infarction: is it reliable?

PubMed

Leite, Luís; Moura Ferreira, Joana; Silva Marques, João; Jorge, Elisabete; Matos, Vítor; Guardado, Jorge; Calisto, João; Pego, Mariano

2015-10-14

Multi-vessel disease is frequent in patients presenting with myocardial infarction and have an important prognostic impact. The decision to proceed to revascularization in non-culprit vessels can be postponed until ischemia is proven in non-invasive stress tests. On the other hand, there is an increasing evidence to support the role of fractional flow reserve (FFR) in acute coronary syndrome setting. We report a case in which a FFR-guided strategy for non-culprit vessels, 3 weeks after an ST-segment elevation myocardial infarction, was followed by a short-term sub-occlusion of the evaluated vessel. The timing of the coronary microcirculation recovery post-myocardial infarction, avoiding a possible false negative FFR, and the diagnostic gaps between ischemia and plaque vulnerability are under discussion. An FFR-guided strategy in this setting should be interpreted with caution.

Stability, structure and scale: improvements in multi-modal vessel extraction for SEEG trajectory planning.

PubMed

Zuluaga, Maria A; Rodionov, Roman; Nowell, Mark; Achhala, Sufyan; Zombori, Gergely; Mendelson, Alex F; Cardoso, M Jorge; Miserocchi, Anna; McEvoy, Andrew W; Duncan, John S; Ourselin, Sébastien

2015-08-01

Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying significantly associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer-assisted planning systems that can optimise the safety profile of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Twelve paired data sets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coefficient was 0.89 ± 0.04, representing a statistically significantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ± 0.03). Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity.

Effect of Boron Neutron Capture Therapy (BNCT) on Normal Liver Regeneration: Towards a Novel Therapy for Liver Metastases

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

Jorge E. Cardoso; Elisa M. Heber; David W. Nigg

2007-10-01

The “TAORMINA project” developed a new method for Boron Neutron Capture Therapy (BNCT) of human multifocal unresectable liver metastases based on whole liver ex-situ BNCT mediated by boronophenylalanine (BPA), followed by whole liver autograft. This technique involved a high risk, prolonged anhepatic phase. The Roffo Institute liver surgeons (JEC) herein propose a novel technique to pursue ex-situ liver BNCT studies with a drastically lower surgical risk for the patient. The technique would involve, sequentially, ex-situ BNCT of left liver segments II and III, partial liver autograft, and induction of partial atrophy of the untreated right liver. The working hypothesis ismore » that the atrophy of the right, untreated, diseased liver would stimulate regeneration of the left, treated, “cured” liver to yield a healthy liver mass, allowing for the resection of the remaining portion of diseased liver. This technique does not involve an anhepatic phase and would thus pose a drastically lower surgical risk to the patient but requires sine qua non that BNCT should not impair the regenerative capacity of normal hepatocytes. The aim of the present study was to assess the effect of therapeutic doses of BNCT mediated by BPA, GB-10 (Na2 10B10H10) or (GB- 10 + BPA) on normal liver regeneration in the Wistar rat employing partial hepatectomy as a regenerative stimulus. BNCT did not cause alterations in the outcome of normal liver regeneration, regenerated liver function or histology. We provide proof of principle to support the development of a novel, promising BNCT technique for the treatment of liver metastases.« less

Advanced 3D image processing techniques for liver and hepatic tumor location and volumetry

NASA Astrophysics Data System (ADS)

Chemouny, Stephane; Joyeux, Henri; Masson, Bruno; Borne, Frederic; Jaeger, Marc; Monga, Olivier

1999-05-01

To assist radiologists and physicians in diagnosing, and in treatment planning and evaluating in liver oncology, we have developed a fast and accurate segmentation of the liver and its lesions within CT-scan exams. The first step of our method is to reduce spatial resolution of CT images. This will have two effects: obtain near isotropic 3D data space and drastically decrease computational time for further processing. On a second step a 3D non-linear `edge- preserving' smoothing filtering is performed throughout the entire exam. On a third step the 3D regions coming out from the second step are homogeneous enough to allow a quite simple segmentation process, based on morphological operations, under supervisor control, ending up with accurate 3D regions of interest (ROI) of the liver and all the hepatic tumors. On a fourth step the ROIs are eventually set back into the original images, features like volume and location are immediately computed and displayed. The segmentation we get is as precise as a manual one but is much faster.

Brain tissue segmentation in 4D CT using voxel classification

NASA Astrophysics Data System (ADS)

van den Boom, R.; Oei, M. T. H.; Lafebre, S.; Oostveen, L. J.; Meijer, F. J. A.; Steens, S. C. A.; Prokop, M.; van Ginneken, B.; Manniesing, R.

2012-02-01

A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.

Careful treatment planning enables safe ablation of liver tumors adjacent to major blood vessels by percutaneous irreversible electroporation (IRE).

PubMed

Kos, Bor; Voigt, Peter; Miklavcic, Damijan; Moche, Michael

2015-09-01

Irreversible electroporation (IRE) is a tissue ablation method, which relies on the phenomenon of electroporation. When cells are exposed to a sufficiently electric field, the plasma membrane is disrupted and cells undergo an apoptotic or necrotic cell death. Although heating effects are known IRE is considered as non-thermal ablation technique and is currently applied to treat tumors in locations where thermal ablation techniques are contraindicated. The manufacturer of the only commercially available pulse generator for IRE recommends a voltage-to-distance ratio of 1500 to 1700 V/cm for treating tumors in the liver. However, major blood vessels can influence the electric field distribution. We present a method for treatment planning of IRE which takes the influence of blood vessels on the electric field into account; this is illustrated on a treatment of 48-year-old patient with a metastasis near the remaining hepatic vein after a right side hemi-hepatectomy. Output of the numerical treatment planning method shows that a 19.9 cm3 irreversible electroporation lesion was generated and the whole tumor was covered with at least 900 V/cm. This compares well with the volume of the hypodense lesion seen in contrast enhanced CT images taken after the IRE treatment. A significant temperature raise occurs near the electrodes. However, the hepatic vein remains open after the treatment without evidence of tumor recurrence after 6 months. Treatment planning using accurate computer models was recognized as important for electrochemotherapy and irreversible electroporation. An important finding of this study was, that the surface of the electrodes heat up significantly. Therefore the clinical user should generally avoid placing the electrodes less than 4 mm away from risk structures when following recommendations of the manufacturer.

Careful treatment planning enables safe ablation of liver tumors adjacent to major blood vessels by percutaneous irreversible electroporation (IRE)

PubMed Central

Kos, Bor; Voigt, Peter; Miklavcic, Damijan; Moche, Michael

2015-01-01

Background Irreversible electroporation (IRE) is a tissue ablation method, which relies on the phenomenon of electroporation. When cells are exposed to a sufficiently electric field, the plasma membrane is disrupted and cells undergo an apoptotic or necrotic cell death. Although heating effects are known IRE is considered as non-thermal ablation technique and is currently applied to treat tumors in locations where thermal ablation techniques are contraindicated. Materials and methods. The manufacturer of the only commercially available pulse generator for IRE recommends a voltage-to-distance ratio of 1500 to 1700 V/cm for treating tumors in the liver. However, major blood vessels can influence the electric field distribution. We present a method for treatment planning of IRE which takes the influence of blood vessels on the electric field into account; this is illustrated on a treatment of 48-year-old patient with a metastasis near the remaining hepatic vein after a right side hemi-hepatectomy. Results Output of the numerical treatment planning method shows that a 19.9 cm3 irreversible electroporation lesion was generated and the whole tumor was covered with at least 900 V/cm. This compares well with the volume of the hypodense lesion seen in contrast enhanced CT images taken after the IRE treatment. A significant temperature raise occurs near the electrodes. However, the hepatic vein remains open after the treatment without evidence of tumor recurrence after 6 months. Conclusions Treatment planning using accurate computer models was recognized as important for electrochemotherapy and irreversible electroporation. An important finding of this study was, that the surface of the electrodes heat up significantly. Therefore the clinical user should generally avoid placing the electrodes less than 4 mm away from risk structures when following recommendations of the manufacturer. PMID:26401128

[Circulating endothelial cells--markers of blood vessel lesions in patients with diffuse liver disease].

PubMed

Dynnik, O B

2006-01-01

The increased level of the circulating endothelial cells (CEC) is in direct dependance with a degree of an endothelial trauma. We defined CEC in blood (cells x 104/L) of 67 adults and children with acute and chronic viral hepatitis (A and B) and healthy volontiars. The author obtained reliable results of increased CEC in all groups consisting of patients with diffuse liver deseases (17,4+/-6,5 and 19,8+/-8,4) in comparison with control groups (3,8+/-1,9 and 3,8+/-1,2) thus CEC can be of practical value as a marker of microvessel lesions of the liver. Endotheliocytemia testifies to be a factor during endothelial trauma in pathogenesis of diffuse liver disease.

Ridge-branch-based blood vessel detection algorithm for multimodal retinal images

NASA Astrophysics Data System (ADS)

Li, Y.; Hutchings, N.; Knighton, R. W.; Gregori, G.; Lujan, B. J.; Flanagan, J. G.

2009-02-01

Automatic detection of retinal blood vessels is important to medical diagnoses and imaging. With the development of imaging technologies, various modals of retinal images are available. Few of currently published algorithms are applied to multimodal retinal images. Besides, the performance of algorithms with pathologies is expected to be improved. The purpose of this paper is to propose an automatic Ridge-Branch-Based (RBB) detection algorithm of blood vessel centerlines and blood vessels for multimodal retinal images (color fundus photographs, fluorescein angiograms, fundus autofluorescence images, SLO fundus images and OCT fundus images, for example). Ridges, which can be considered as centerlines of vessel-like patterns, are first extracted. The method uses the connective branching information of image ridges: if ridge pixels are connected, they are more likely to be in the same class, vessel ridge pixels or non-vessel ridge pixels. Thanks to the good distinguishing ability of the designed "Segment-Based Ridge Features", the classifier and its parameters can be easily adapted to multimodal retinal images without ground truth training. We present thorough experimental results on SLO images, color fundus photograph database and other multimodal retinal images, as well as comparison between other published algorithms. Results showed that the RBB algorithm achieved a good performance.

Visualisation of distribution of gold nanoparticles in liver tissues ex vivo and in vitro using the method of optical coherence tomography

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

Genina, Elina A; Terentyuk, G S; Khlebtsov, B N

2012-06-30

The possibility of visualising the distribution of gold nanoparticles in liver by means of the method of optical coherence tomography is studied experimentally in model samples of beef liver in vitro and rat liver ex vivo. In the experiments we used the gold nanoparticles in the form of nanocages with resonance absorption in the near-IR spectral region. In the model studies the suspension of nanoparticles was applied to the surface of the sample, which then was treated with ultrasound. In the ex vivo studies the suspension of nanoparticles was injected to the laboratory rats intravenously. The image contrast and themore » optical depth of detection of blood vessels and liver structure components are calculated, as well as the depth of liver optical probing before and after the injection of nanoparticles. It was shown that the administration of the nanoparticle increases significantly the imaging contrast of liver blood vessels owing to the localisation of the nanoparticles therein.« less

Analysis of the efficacy of portal vein embolization for patients with extensive liver malignancy and very low future liver remnant volume, including a comparison with the associating liver partition with portal vein ligation for staged hepatectomy approach.

PubMed

Shindoh, Junichi; Vauthey, Jean-Nicolas; Zimmitti, Giuzeppe; Curley, Steven A; Huang, Steven Y; Mahvash, Armeen; Gupta, Sanjay; Wallace, Michael J; Aloia, Thomas A

2013-07-01

The primary reported indication for the associating liver partition with portal vein ligation for staged hepatectomy (ALPPS) technique is in patients with very low future liver remnant volumes. Given the elevated incidence of major morbidity (40%) and liver-related mortality (12%) with ALPPS, we sought to determine the safety and efficacy of percutaneous portal vein embolization (PVE) in a similar patient population. Tumor resectability and morbidity and mortality rates were reviewed for 144 consecutive liver tumor patients with future liver remnant to body weight ratios (LR/BW) less than 0.5%. All patients were referred for preoperative percutaneous right plus segment IV PVE using embolic microspheres, with planned reassessment of the LR/BW 30 days after PVE. Post-PVE outcomes were compared with reported outcomes for ALPPS. Percutaneous PVE was successfully performed in 141 of the 144 study patients (97.9%). Adequate regeneration was observed in 139 patients (98.5%) with median post-PVE LR/BW rising from 0.33% to 0.52% (p < 0.0001), representing a per-patient median regeneration rate of 62% (range 0.3% to 379%). In total, 104 patients underwent extended right hepatectomy (n = 102) or right hepatectomy (n = 2). The remaining 40 patients (27.8%) were not resectable due to short-interval disease progression (27 patients, 18.5%), insufficient liver regeneration (5 patients, 3.5%), and medical comorbidities (8 patients, 5.6%). After resection, the following outcomes were observed: major morbidity: 33.0% (34 of 104), liver insufficiency: 12.5% (13 of 104), and 90-day liver-related mortality: 5.8% (6 of 104). These oncologic and technical results compare favorably with those of ALPPS. Based on its ability to select oncologically resectable patients and superior safety and efficacy profiles, percutaneous right + segment IV PVE and interval surgery remains the standard of care for patients with very low future liver remnant volumes. Copyright © 2013 American College of

Automated segmentation of the lungs from high resolution CT images for quantitative study of chronic obstructive pulmonary diseases

NASA Astrophysics Data System (ADS)

Garg, Ishita; Karwoski, Ronald A.; Camp, Jon J.; Bartholmai, Brian J.; Robb, Richard A.

2005-04-01

Chronic obstructive pulmonary diseases (COPD) are debilitating conditions of the lung and are the fourth leading cause of death in the United States. Early diagnosis is critical for timely intervention and effective treatment. The ability to quantify particular imaging features of specific pathology and accurately assess progression or response to treatment with current imaging tools is relatively poor. The goal of this project was to develop automated segmentation techniques that would be clinically useful as computer assisted diagnostic tools for COPD. The lungs were segmented using an optimized segmentation threshold and the trachea was segmented using a fixed threshold characteristic of air. The segmented images were smoothed by a morphological close operation using spherical elements of different sizes. The results were compared to other segmentation approaches using an optimized threshold to segment the trachea. Comparison of the segmentation results from 10 datasets showed that the method of trachea segmentation using a fixed air threshold followed by morphological closing with spherical element of size 23x23x5 yielded the best results. Inclusion of greater number of pulmonary vessels in the lung volume is important for the development of computer assisted diagnostic tools because the physiological changes of COPD can result in quantifiable anatomic changes in pulmonary vessels. Using a fixed threshold to segment the trachea removed airways from the lungs to a better extent as compared to using an optimized threshold. Preliminary measurements gathered from patient"s CT scans suggest that segmented images can be used for accurate analysis of total lung volume and volumes of regional lung parenchyma. Additionally, reproducible segmentation allows for quantification of specific pathologic features, such as lower intensity pixels, which are characteristic of abnormal air spaces in diseases like emphysema.

A novel method for segmentation of Infrared Scanning Laser Ophthalmoscope (IR-SLO) images of retina.

PubMed

Ajaz, Aqsa; Aliahmad, Behzad; Kumar, Dinesh K

2017-07-01

Retinal vessel segmentation forms an essential element of automatic retinal disease screening systems. The development of multimodal imaging system with IR-SLO and OCT could help in studying the early stages of retinal disease. The advantages of IR-SLO to examine the alterations in the structure of retina and direct correlation with OCT can be useful for assessment of various diseases. This paper presents an automatic method for segmentation of IR-SLO fundus images based on the combination of morphological filters and image enhancement techniques. As a first step, the retinal vessels are contrasted using morphological filters followed by background exclusion using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Bilateral filtering. The final segmentation is obtained by using Isodata technique. Our approach was tested on a set of 26 IR-SLO images and results were compared to two set of gold standard images. The performance of the proposed method was evaluated in terms of sensitivity, specificity and accuracy. The system has an average accuracy of 0.90 for both the sets.

Automated detection of kinks from blood vessels for optic cup segmentation in retinal images

NASA Astrophysics Data System (ADS)

Wong, D. W. K.; Liu, J.; Lim, J. H.; Li, H.; Wong, T. Y.

2009-02-01

The accurate localization of the optic cup in retinal images is important to assess the cup to disc ratio (CDR) for glaucoma screening and management. Glaucoma is physiologically assessed by the increased excavation of the optic cup within the optic nerve head, also known as the optic disc. The CDR is thus an important indicator of risk and severity of glaucoma. In this paper, we propose a method of determining the cup boundary using non-stereographic retinal images by the automatic detection of a morphological feature within the optic disc known as kinks. Kinks are defined as the bendings of small vessels as they traverse from the disc to the cup, providing physiological validation for the cup boundary. To detect kinks, localized patches are first generated from a preliminary cup boundary obtained via level set. Features obtained using edge detection and wavelet transform are combined using a statistical approach rule to identify likely vessel edges. The kinks are then obtained automatically by analyzing the detected vessel edges for angular changes, and these kinks are subsequently used to obtain the cup boundary. A set of retinal images from the Singapore Eye Research Institute was obtained to assess the performance of the method, with each image being clinically graded for the CDR. From experiments, when kinks were used, the error on the CDR was reduced to less than 0.1 CDR units relative to the clinical CDR, which is within the intra-observer variability of 0.2 CDR units.

Comparison of anterior segment optical coherence tomography angiography and fluorescein angiography for iris vasculature analysis.

PubMed

Zett, Claudio; Stina, Deborah M Rosa; Kato, Renata Tiemi; Novais, Eduardo Amorim; Allemann, Norma

2018-04-01

The aim of this study is to perform imaging of irises of different colors using spectral domain anterior segment optical coherence tomography angiography (AS-OCTA) and iris fluorescein angiography (IFA) and compare their effectiveness in examining iris vasculature. This is a cross-sectional observational clinical study. Patients with no vascular iris alterations and different pigmentation levels were recruited. Participants were imaged using OCTA adapted with an anterior segment lens and IFA with a confocal scanning laser ophthalmoscope (cSLO) adapted with an anterior segment lens. AS-OCTA and IFA images were then compared. Two blinded readers classified iris pigmentation and compared the percentage of visible vessels between OCTA and IFA images. Twenty eyes of 10 patients with different degrees of iris pigmentation were imaged using AS-OCTA and IFA. Significantly more visible iris vessels were observed using OCTA than using FA (W = 5.22; p < 0.001). Iris pigmentation was negatively correlated to the percentage of visible vessels in both imaging methods (OCTA, rho = - 0.73, p < 0.001; IFA, rho = - 0.77, p < 0.001). Unlike FA, AS-OCTA could not detect leakage of dye, delay, or impregnation. Nystagmus and inadequate fixation along with motion artifacts resulted in lower quality images in AS-OCTA than in IFA. AS-OCTA is a new imaging modality which allows analysis of iris vasculature. In both AS-OCTA and IFA, iris pigmentation caused vasculature imaging blockage, but AS-OCTA provided more detailed iris vasculature images than IFA. Additional studies including different iris pathologies are needed to determine the most optimal scanning parameters in OCTA of the anterior segment.

Morphology-based three-dimensional segmentation of coronary artery tree from CTA scans

NASA Astrophysics Data System (ADS)

Banh, Diem Phuc T.; Kyprianou, Iacovos S.; Paquerault, Sophie; Myers, Kyle J.

2007-03-01

We developed an algorithm based on a rule-based threshold framework to segment the coronary arteries from angiographic computed tomography (CTA) data. Computerized segmentation of the coronary arteries is a challenging procedure due to the presence of diverse anatomical structures surrounding the heart on cardiac CTA data. The proposed algorithm incorporates various levels of image processing and organ information including region, connectivity and morphology operations. It consists of three successive stages. The first stage involves the extraction of the three-dimensional scaffold of the heart envelope. This stage is semiautomatic requiring a reader to review the CTA scans and manually select points along the heart envelope in slices. These points are further processed using a surface spline-fitting technique to automatically generate the heart envelope. The second stage consists of segmenting the left heart chambers and coronary arteries using grayscale threshold, size and connectivity criteria. This is followed by applying morphology operations to further detach the left and right coronary arteries from the aorta. In the final stage, the 3D vessel tree is reconstructed and labeled using an Isolated Connected Threshold technique. The algorithm was developed and tested on a patient coronary artery CTA that was graciously shared by the Department of Radiology of the Massachusetts General Hospital. The test showed that our method constantly segmented the vessels above 79% of the maximum gray-level and automatically extracted 55 of the 58 coronary segments that can be seen on the CTA scan by a reader. These results are an encouraging step toward our objective of generating high resolution models of the male and female heart that will be subsequently used as phantoms for medical imaging system optimization studies.

Automated segmentation of multifocal basal ganglia T2*-weighted MRI hypointensities

PubMed Central

Glatz, Andreas; Bastin, Mark E.; Kiker, Alexander J.; Deary, Ian J.; Wardlaw, Joanna M.; Valdés Hernández, Maria C.