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Automatic MRI prostate segmentation using 3D deeply supervised FCN with concatenated atrous convolution
Author(s): Bo Wang; Yang Lei; Jiwoong Jason Jeong; Tonghe Wang; Yingzi Liu; Sibo Tian; Pretesh Patel; Xiaojun Jiang; Ashesh B. Jani; Hui Mao; Walter J. Curran; Tian Liu; Xiaofeng Yang
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Paper Abstract

Prostate segmentation of MR volumes is a very important task for treatment planning and image-guided brachytherapy and radiotherapy. Manual delineation of prostate in MR image is very time-consuming and depends on the subjective experience of the physicians. On the other hand, automatic prostate segmentation becomes a reasonable and attractive choice for its speed, even though the task is very challenging because of inhomogeneous intensity and variability of prostate appearance and shape. In this paper, we propose a method to automatically segment MR prostate image based on 3D deeply supervised FCN with concatenated atrous convolution (3D DSA-FCN). More discriminative features provide explicit convergence acceleration in training stage using straightforward dense predictions as deep supervision and the concatenated atrous convolution extract more global contextual information for accurate predictions. The presented method was evaluated on the internal dataset comprising 15 T2-weighted prostate MR volumes from Winship Cancer Institute and obtained a mean Dice similarity coefficient (DSC) of 0.852±0.031, 95% Hausdorff distance (95%HD) 7.189±1.953 mm and mean surface distance (MSD) of 1.597±0.360 mm. The experimental results show that our 3D DSA-FCN could yield satisfied MR prostate segmentation, which can be used for image-guided radiotherapy.

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503X (13 March 2019); doi: 10.1117/12.2512551
Show Author Affiliations
Bo Wang, Ningxia Univ. (China)
Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Jiwoong Jason Jeong, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Yingzi Liu, Emory Univ. (United States)
Sibo Tian, Emory Univ. (United States)
Pretesh Patel, Emory Univ. (United States)
Xiaojun Jiang, Emory Univ. (United States)
Ashesh B. Jani, Emory Univ. (United States)
Hui Mao, Georgia Institute of Technology (United States)
Walter J. Curran, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)

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

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