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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)257-273
Number of pages17
JournalMedical Image Analysis
StatePublished - Dec 2017
Externally publishedYes


  • Cortex model adaptation
  • Non-uniform graph search
  • Renal cortex

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Health Informatics
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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