CorteXpert: A model-based method for automatic renal cortex segmentation

Dehui Xiang; Ulas Bagci; Chao Jin; Fei Shi; Weifang Zhu; Jianhua Yao; Milan Sonka; Xinjian Chen

doi:10.1016/j.media.2017.06.010

Back

CorteXpert: A model-based method for automatic renal cortex segmentation

Journal article

Open access

Peer reviewed

CorteXpert: A model-based method for automatic renal cortex segmentation

Dehui Xiang, Ulas Bagci, Chao Jin, Fei Shi, Weifang Zhu, Jianhua Yao, Milan Sonka and Xinjian Chen

Medical image analysis, Vol.42, pp.257-273

12/2017

DOI: 10.1016/j.media.2017.06.010

PMID: 28888170

Files and links (1)

url

https://stars.library.ucf.edu/scopus2015/5601View

Open Access

Abstract

•A precise clinical definition of renal cortex.•A localization algorithm for the outer and the inner layers of the renal cortex.•A purely delineation-based algorithm, which is not only accurate but also extremely efficient.•A non-uniform graph search method is presented to obtain accurate segmentation. [Display omitted] 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.

Cortex model adaptation

Non-uniform graph search

Renal cortex

Details

Title: Subtitle: CorteXpert: A model-based method for automatic renal cortex segmentation
Creators: Dehui Xiang - School of Electronics and Information Engineering, Soochow University, Jiangsu 215006, China
Ulas Bagci - Center for Research in Computer Vision, University of Central Florida (UCF), Orlando, FL 32816, USA
Chao Jin - School of Electronics and Information Engineering, Soochow University, Jiangsu 215006, China
Fei Shi - School of Electronics and Information Engineering, Soochow University, Jiangsu 215006, China
Weifang Zhu - School of Electronics and Information Engineering, Soochow University, Jiangsu 215006, China
Jianhua Yao - Department of Radiology and Imaging Sciences, National Institute of Health, Bethesda, MD 20892, USA
Milan Sonka - Department of Electrical and Computer Engineering, the University of Iowa, Iowa City, IA 52242, USA
Xinjian Chen - School of Electronics and Information Engineering, Soochow University, Jiangsu 215006, China
Resource Type: Journal article
Publication Details: Medical image analysis, Vol.42, pp.257-273
DOI: 10.1016/j.media.2017.06.010
PMID: 28888170
NLM abbreviation: Med Image Anal
ISSN: 1361-8415
eISSN: 1361-8423
Publisher: Elsevier B.V
Grant note: DOI: 10.13039/501100001809, name: NSFC, award: 61401293, 81371629, 61401294, 81401451, 81401472
Language: English
Date published: 12/2017
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984047678202771

Metrics

22 Record Views

23 Times Cited - Web of Science