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Simultaneous and automatic two surface detection of renal cortex in 3D CT images by enhanced sparse shape composition
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Paper Abstract

Automatic organ localization plays a significant role in medical image segmentation. This paper introduces a novel approach for simultaneous and automatic two surface detection of renal cortex from contrast enhanced abdominal CT scans. The proposed framework is an integrated procedure consisting of three main parts: (i) cortex model training, both two shape variabilities are detected using principal components analysis from the manual annotation, and dual shape dictionaries and appearance dictionaries are constructed; (ii) outer mesh reconstruction, the initialized outer mesh is iteratively deformed to the target boundary; (iii) inner mesh reconstruction, the inner mesh can be reconstructed using the same deformation coefficients and similarity transformation of the outer mesh with the inner mesh shape dictionary. Our method was validated on a clinical data set of 37 CT scans using the leave-one-out cross validation strategy. The proposed method has improved the overall segmentation accuracy of Dice similarity coefficient to 91.95%±3.15% for renal cortex segmentation.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492D (15 March 2019); doi: 10.1117/12.2512029
Show Author Affiliations
Haihong Tian, Soochow Univ. (China)
Geng Chen, Soochow Univ. (China)
Dehui Xiang, Soochow Univ. (China)
Xinjian Chen, Soochow Univ. (China)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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