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Proceedings Paper

Automatic MR kidney segmentation for autosomal dominant polycystic kidney disease
Author(s): Guangrui Mu; Yiyi Ma; Miaofei Han; Yiqiang Zhan; Xiang Zhou; Yaozong Gao
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Paper Abstract

Measurement of total kidney volume (TKV) plays an important role in the early therapeutic stage of autosomal dominant polycystic kidney disease (ADPKD). As a crucial biomarker, an accurate TKV can sensitively reflect the disease progression and be used as an indicator to evaluate the curative effect of the drug. However, manual contouring of kidneys in magnetic resonance (MR) images is time-consuming (40 minutes), which greatly hinders the wide adoption of TKV in clinic. In this paper, we propose a multi-resolution 3D convolutional neural network to automatically segment kidneys of ADPKD patients from MR images. We adopt two resolutions and use a customized V-Net model for both resolutions. The V-Net model is able to integrate both high-level context information with detailed local information for accurate organ segmentation. The V-Net model in the coarse resolution can robustly localize the kidneys, while the VNet model in the fine resolution can accurately refine the kidney boundaries. Validated on 305 subjects with different loss functions and network architectures, our method can achieve over 95% Dice similarity coefficient with the groundtruth labeled by a senior physician. Moreover, the proposed method can dramatically reduce the measurement of kidney volume from 40 minutes to about 1 second, which can greatly accelerate the disease staging of ADPKD patients for large clinical trials, promote the development of related drugs, and reduce the burden of physicians.

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500X (13 March 2019); doi: 10.1117/12.2512372
Show Author Affiliations
Guangrui Mu, Shanghai United Imaging Intelligence (China)
Southern Medical Univ. (China)
Yiyi Ma, Shanghai Changzheng Hospital (China)
Miaofei Han, Shanghai United Imaging Intelligence (China)
Yiqiang Zhan, Shanghai United Imaging Intelligence (China)
Xiang Zhou, Shanghai United Imaging Intelligence (China)
Yaozong Gao, Shanghai United Imaging Intelligence (China)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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