Renal cortex segmentation using optimal surface search with novel graph construction

Authors:
Xiuli Li;Xinjian Chen;Jianhua Yao;Xing Zhang;Jie Tian
Affiliations:
Institute of Automation, Chinese Academy of Sciences, China;Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health;Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health;Institute of Automation, Chinese Academy of Sciences, China;Institute of Automation, Chinese Academy of Sciences, China
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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Abstract

In this paper, we propose a novel approach to solve the renal cortex segmentation problem, which has rarely been studied. In this study, the renal cortex segmentation problem is handled as a multiple-surfaces extraction problem, which is solved using the optimal surface search method. We propose a novel graph construction scheme in the optimal surface search to better accommodate multiple surfaces. Different surface sub-graphs are constructed according to their properties, and inter-surface relationships are also modeled in the graph. The proposed method was tested on 17 clinical CT datasets. The true positive volume fraction (TPVF) and false positive volume fraction (FPVF) are 74.10% and 0.08%, respectively. The experimental results demonstrate the effectiveness of the proposed method.