The Experts below are selected from a list of 225 Experts worldwide ranked by ideXlab platform
Xinjian Chen - One of the best experts on this subject based on the ideXlab platform.
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Cortexpert a model based method for automatic Renal Cortex segmentation
Medical Image Analysis, 2017Co-Authors: Dehui Xiang, Jianhua Yao, Ulas Bagci, Chao Jin, Fei Shi, Weifang Zhu, Milan Sonka, Xinjian ChenAbstract:Abstract This paper introduces a model-based approach for a fully automatic delineation of kidney and Cortex tissue from contrast-enhanced abdominal CT scans. The proposed framework, named Cortexpert , consists of two new strategies for kidney tissue delineation: Cortex model adaptation and non-uniform graph search. Cortexpert was validated on a clinical data set of 58 CT scans using the cross-validation evaluation strategy. The experimental results indicated the state-of-the-art segmentation accuracies (as dice coefficient): 97.86% ± 2.41% and 97.48% ± 3.18% for kidney and Renal Cortex delineations, respectively.
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Renal Cortex Segmentation on Computed Tomography
Abdomen and Thoracic Imaging, 2013Co-Authors: Xinjian Chen, Dehui Xiang, Heming Zhao, Jianhua YaoAbstract:The current procedure of Renal Cortex segmentation is subjective and tedious. This chapter introduces an automated framework for Renal Cortex segmentation on contrast-enhanced abdominal CT images. The framework consists of four parts: first, an active appearance model (AAM) is built using a set of training images; second, the AAM is refined by live wire (LW) method to initialize the shape and location of the kidney; third, an iterative graph cut-oriented active appearance model (IGC-OAAM) method is applied to segment the kidney; Finally, the identified kidney contour is used as shape constraints for Renal Cortex segmentation which is also based on IGC-OAAM. The chapter also discusses several other state-of-art techniques for segmentation and modeling of the kidneys.
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automatic Renal Cortex segmentation using implicit shape registration and novel multiple surfaces graph search
IEEE Transactions on Medical Imaging, 2012Co-Authors: Xinjian Chen, Jianhua Yao, Xing Zhang, Fei Yang, Jian TianAbstract:In this paper, we present an automatic Renal Cortex segmentation approach using the implicit shape registration and novel multiple surfaces graph search. The proposed approach is based on a hierarchy system. First, the whole kidney is roughly initialized using an implicit shape registration method, with the shapes embedded in the space of Euclidean distance functions. Second, the outer and inner surfaces of Renal Cortex are extracted utilizing multiple surfaces graph searching, which is extended to allow for varying sampling distances and physical constraints to better separate the Renal Cortex and Renal column. Third, a Renal Cortex refining procedure is applied to detect and reduce incorrect segmentation pixels around the Renal pelvis, further improving the segmentation accuracy. The method was evaluated on 17 clinical computed tomography scans using the leave-one-out strategy with five metrics: Dice similarity coefficient (DSC), volumetric overlap error (OE), signed relative volume difference (SVD), average symmetric surface distance (Davg), and average symmetric rms surface distance (Drms). The experimental results of DSC, OE, SVD, Davg, and Drms were 90.50%±1.19%, 4.38% ±3.93%, 2.37% ±1.72%, 0.14 mm ±0.09 mm , and 0.80 mm ±0.64 mm, respectively. The results showed the feasibility, efficiency, and robustness of the proposed method.
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an automatic method for Renal Cortex segmentation on ct images evaluation on kidney donors
Academic Radiology, 2012Co-Authors: Xinjian Chen, Ronald M Summers, Monique E Cho, Ulas Bagci, Jianhua YaoAbstract:Rationale and Objectives The aims of this study were to develop and validate an automated method to segment the Renal Cortex on contrast-enhanced abdominal computed tomographic images from kidney donors and to track Cortex volume change after donation. Materials and Methods A three-dimensional fully automated Renal Cortex segmentation method was developed and validated on 37 arterial phase computed tomographic data sets (27 patients, 10 of whom underwent two computed tomographic scans before and after nephrectomy) using leave-one-out strategy. Two expert interpreters manually segmented the Cortex slice by slice, and linear regression analysis and Bland-Altman plots were used to compare automated and manual segmentation. The true-positive and false-positive volume fractions were also calculated to evaluate the accuracy of the proposed method. Cortex volume changes in 10 subjects were also calculated. Results The linear regression analysis results showed that the automated and manual segmentation methods had strong correlations, with Pearson's correlations of 0.9529, 0.9309, 0.9283, and 0.9124 between intraobserver variation, interobserver variation, automated and user 1, and automated and user 2, respectively ( P P t test). The overall true-positive and false-positive volume fractions for Cortex segmentation were 90.15 ± 3.11% and 0.85 ± 0.05%. With the proposed automated method, the time for Cortex segmentation was reduced from 20 minutes for manual segmentation to 2 minutes. Conclusions The proposed method was accurate and efficient and can replace the current subjective and time-consuming manual procedure. The computer measurement confirms the volume of Renal Cortex increases after kidney donation.
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Medical Imaging: Image Processing - Incorporation of physical constraints in optimal surface search for Renal Cortex segmentation
Medical Imaging 2012: Image Processing, 2012Co-Authors: Xinjian Chen, Jianhua Yao, Xing Zhang, Jie TianAbstract:In this paper, we propose a novel approach for multiple surfaces segmentation based on the incorporation of physical constraints in optimal surface searching. We apply our new approach to solve the Renal Cortex segmentation problem, an important but not sufficiently researched issue. In this study, in order to better restrain the intensity proximity of the Renal Cortex and Renal column, we extend the optimal surface search approach to allow for varying sampling distance and physical separation constraints, instead of the traditional fixed sampling distance and numerical separation constraints. The sampling distance of each vertex-column is computed according to the sparsity of the local triangular mesh. Then the physical constraint learned from a priori Renal Cortex thickness is applied to the inter-surface arcs as the separation constraints. Appropriate varying sampling distance and separation constraints were learnt from 6 clinical CT images. After training, the proposed approach was tested on a test set of 10 images. The manual segmentation of Renal Cortex was used as the reference standard. Quantitative analysis of the segmented Renal Cortex indicates that overall segmentation accuracy was increased after introducing the varying sampling distance and physical separation constraints (the average true positive volume fraction (TPVF) and false positive volume fraction (FPVF) were 83.96% and 2.80%, respectively, by using varying sampling distance and physical separation constraints compared to 74.10% and 0.18%, respectively, by using fixed sampling distance and numerical separation constraints). The experimental results demonstrated the effectiveness of the proposed approach.
Jianhua Yao - One of the best experts on this subject based on the ideXlab platform.
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Cortexpert a model based method for automatic Renal Cortex segmentation
Medical Image Analysis, 2017Co-Authors: Dehui Xiang, Jianhua Yao, Ulas Bagci, Chao Jin, Fei Shi, Weifang Zhu, Milan Sonka, Xinjian ChenAbstract:Abstract This paper introduces a model-based approach for a fully automatic delineation of kidney and Cortex tissue from contrast-enhanced abdominal CT scans. The proposed framework, named Cortexpert , consists of two new strategies for kidney tissue delineation: Cortex model adaptation and non-uniform graph search. Cortexpert was validated on a clinical data set of 58 CT scans using the cross-validation evaluation strategy. The experimental results indicated the state-of-the-art segmentation accuracies (as dice coefficient): 97.86% ± 2.41% and 97.48% ± 3.18% for kidney and Renal Cortex delineations, respectively.
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Renal Cortex Segmentation on Computed Tomography
Abdomen and Thoracic Imaging, 2013Co-Authors: Xinjian Chen, Dehui Xiang, Heming Zhao, Jianhua YaoAbstract:The current procedure of Renal Cortex segmentation is subjective and tedious. This chapter introduces an automated framework for Renal Cortex segmentation on contrast-enhanced abdominal CT images. The framework consists of four parts: first, an active appearance model (AAM) is built using a set of training images; second, the AAM is refined by live wire (LW) method to initialize the shape and location of the kidney; third, an iterative graph cut-oriented active appearance model (IGC-OAAM) method is applied to segment the kidney; Finally, the identified kidney contour is used as shape constraints for Renal Cortex segmentation which is also based on IGC-OAAM. The chapter also discusses several other state-of-art techniques for segmentation and modeling of the kidneys.
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automatic Renal Cortex segmentation using implicit shape registration and novel multiple surfaces graph search
IEEE Transactions on Medical Imaging, 2012Co-Authors: Xinjian Chen, Jianhua Yao, Xing Zhang, Fei Yang, Jian TianAbstract:In this paper, we present an automatic Renal Cortex segmentation approach using the implicit shape registration and novel multiple surfaces graph search. The proposed approach is based on a hierarchy system. First, the whole kidney is roughly initialized using an implicit shape registration method, with the shapes embedded in the space of Euclidean distance functions. Second, the outer and inner surfaces of Renal Cortex are extracted utilizing multiple surfaces graph searching, which is extended to allow for varying sampling distances and physical constraints to better separate the Renal Cortex and Renal column. Third, a Renal Cortex refining procedure is applied to detect and reduce incorrect segmentation pixels around the Renal pelvis, further improving the segmentation accuracy. The method was evaluated on 17 clinical computed tomography scans using the leave-one-out strategy with five metrics: Dice similarity coefficient (DSC), volumetric overlap error (OE), signed relative volume difference (SVD), average symmetric surface distance (Davg), and average symmetric rms surface distance (Drms). The experimental results of DSC, OE, SVD, Davg, and Drms were 90.50%±1.19%, 4.38% ±3.93%, 2.37% ±1.72%, 0.14 mm ±0.09 mm , and 0.80 mm ±0.64 mm, respectively. The results showed the feasibility, efficiency, and robustness of the proposed method.
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an automatic method for Renal Cortex segmentation on ct images evaluation on kidney donors
Academic Radiology, 2012Co-Authors: Xinjian Chen, Ronald M Summers, Monique E Cho, Ulas Bagci, Jianhua YaoAbstract:Rationale and Objectives The aims of this study were to develop and validate an automated method to segment the Renal Cortex on contrast-enhanced abdominal computed tomographic images from kidney donors and to track Cortex volume change after donation. Materials and Methods A three-dimensional fully automated Renal Cortex segmentation method was developed and validated on 37 arterial phase computed tomographic data sets (27 patients, 10 of whom underwent two computed tomographic scans before and after nephrectomy) using leave-one-out strategy. Two expert interpreters manually segmented the Cortex slice by slice, and linear regression analysis and Bland-Altman plots were used to compare automated and manual segmentation. The true-positive and false-positive volume fractions were also calculated to evaluate the accuracy of the proposed method. Cortex volume changes in 10 subjects were also calculated. Results The linear regression analysis results showed that the automated and manual segmentation methods had strong correlations, with Pearson's correlations of 0.9529, 0.9309, 0.9283, and 0.9124 between intraobserver variation, interobserver variation, automated and user 1, and automated and user 2, respectively ( P P t test). The overall true-positive and false-positive volume fractions for Cortex segmentation were 90.15 ± 3.11% and 0.85 ± 0.05%. With the proposed automated method, the time for Cortex segmentation was reduced from 20 minutes for manual segmentation to 2 minutes. Conclusions The proposed method was accurate and efficient and can replace the current subjective and time-consuming manual procedure. The computer measurement confirms the volume of Renal Cortex increases after kidney donation.
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Medical Imaging: Image Processing - Incorporation of physical constraints in optimal surface search for Renal Cortex segmentation
Medical Imaging 2012: Image Processing, 2012Co-Authors: Xinjian Chen, Jianhua Yao, Xing Zhang, Jie TianAbstract:In this paper, we propose a novel approach for multiple surfaces segmentation based on the incorporation of physical constraints in optimal surface searching. We apply our new approach to solve the Renal Cortex segmentation problem, an important but not sufficiently researched issue. In this study, in order to better restrain the intensity proximity of the Renal Cortex and Renal column, we extend the optimal surface search approach to allow for varying sampling distance and physical separation constraints, instead of the traditional fixed sampling distance and numerical separation constraints. The sampling distance of each vertex-column is computed according to the sparsity of the local triangular mesh. Then the physical constraint learned from a priori Renal Cortex thickness is applied to the inter-surface arcs as the separation constraints. Appropriate varying sampling distance and separation constraints were learnt from 6 clinical CT images. After training, the proposed approach was tested on a test set of 10 images. The manual segmentation of Renal Cortex was used as the reference standard. Quantitative analysis of the segmented Renal Cortex indicates that overall segmentation accuracy was increased after introducing the varying sampling distance and physical separation constraints (the average true positive volume fraction (TPVF) and false positive volume fraction (FPVF) were 83.96% and 2.80%, respectively, by using varying sampling distance and physical separation constraints compared to 74.10% and 0.18%, respectively, by using fixed sampling distance and numerical separation constraints). The experimental results demonstrated the effectiveness of the proposed approach.
M.n. Recio - One of the best experts on this subject based on the ideXlab platform.
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Effects of lindane on fluidity and lipid composition in rat Renal Cortex membranes.
Biochimica et biophysica acta, 1991Co-Authors: M.a. Pérez-albarsanz, P. López-aparicio, Sergio Senar, M.n. RecioAbstract:The influence of lindane upon dynamic properties of plasma membranes from rat Renal Cortex has been investigated using a fluorescence polarization technique. Preincubation with lindane increased membrane fluidity in a manner that is dose-dependent. This increase was higher in brush border membranes than in basolateral membranes. However, a significant decrease of the membrane fluidity was found in brush border membranes when rats were injected with lindane for 12 days. A possible solution to this difference could involve a resistance to membrane disordering by lindane through a regulatory mechanism that would balance the amount of cholesterol and phospholipid classes in the Renal Cortex membranes of lindane-injected rats.
Olga E. Redina - One of the best experts on this subject based on the ideXlab platform.
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Comparative transcriptional profiling of Renal Cortex in rats with inherited stress-induced arterial hypertension and normotensive Wistar Albino Glaxo rats
BMC Genetics, 2016Co-Authors: Larisa A. Fedoseeva, Marina A. Ryazanova, Nikita I. Ershov, Arcady L. Markel, Olga E. RedinaAbstract:Background The Renal function plays a leading role in long-term control of arterial pressure. The comparative analysis of Renal Cortex transcriptome in ISIAH rats with inherited stress-induced arterial hypertension and normotensive WAG rats was performed using RNA-Seq approach. The goal of the study was to identify the differentially expressed genes (DEGs) related to hypertension and to detect the pathways contributing to the differences in Renal functions in ISIAH and WAG rats. Results The analysis revealed 716 genes differentially expressed in Renal Cortex of ISIAH and WAG rats, 42 of them were associated with arterial hypertension and regulation of blood pressure (BP). Several Gene Ontology (GO) terms significantly enriched with DEGs suggested the existence of the hormone dependent interstrain differences in Renal Cortex function. Multiple DEGs were associated with regulation of blood pressure and blood circulation, with the response to stress (including oxidative stress, hypoxia, and fluid shear stress) and its regulation. Several other processes which may contribute to hypertension development in ISIAH rats were: ion transport, regulation of calcium ion transport, homeostatic process, tissue remodeling, immune system process and regulation of immune response. KEGG analysis marked out several pathways significantly enriched with DEGs related to immune system function, to steroid hormone biosynthesis, tryptophan, glutathione, nitrogen, and drug metabolism. Conclusions The results of the study provide a basis for identification of potential biomarkers of stress-sensitive hypertension and for further investigation of the mechanisms that affect Renal Cortex function and hypertension development.
M.a. Pérez-albarsanz - One of the best experts on this subject based on the ideXlab platform.
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Effects of lindane on fluidity and lipid composition in rat Renal Cortex membranes.
Biochimica et biophysica acta, 1991Co-Authors: M.a. Pérez-albarsanz, P. López-aparicio, Sergio Senar, M.n. RecioAbstract:The influence of lindane upon dynamic properties of plasma membranes from rat Renal Cortex has been investigated using a fluorescence polarization technique. Preincubation with lindane increased membrane fluidity in a manner that is dose-dependent. This increase was higher in brush border membranes than in basolateral membranes. However, a significant decrease of the membrane fluidity was found in brush border membranes when rats were injected with lindane for 12 days. A possible solution to this difference could involve a resistance to membrane disordering by lindane through a regulatory mechanism that would balance the amount of cholesterol and phospholipid classes in the Renal Cortex membranes of lindane-injected rats.