TY - JOUR
T1 - CorteXpert
T2 - A model-based method for automatic renal cortex segmentation
AU - Xiang, Dehui
AU - Bagci, Ulas
AU - Jin, Chao
AU - Shi, Fei
AU - Zhu, Weifang
AU - Yao, Jianhua
AU - Sonka, Milan
AU - Chen, Xinjian
N1 - Funding Information:
This work has been supported in part by the National Basic Research Program of China (973 Program) under Grant 2014CB748600, and in part by the National Natural Science Foundation of China ( NSFC ) under Grant 61401293 , 81371629 , 61401294 , 81401451 , 81401472 . The authors thank to Prof. Xiaodong Wu for the great help to demonstrate the optimality of the proposed method.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/12
Y1 - 2017/12
N2 - 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.
AB - 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.
KW - Cortex model adaptation
KW - Non-uniform graph search
KW - Renal cortex
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U2 - 10.1016/j.media.2017.06.010
DO - 10.1016/j.media.2017.06.010
M3 - Article
C2 - 28888170
AN - SCOPUS:85028886049
SN - 1361-8415
VL - 42
SP - 257
EP - 273
JO - Medical Image Analysis
JF - Medical Image Analysis
ER -