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Automated prostate segmentation of volumetric CT images using 3D deeply supervised dilated FCN
Author(s): Bo Wang; Yang Lei; Tonghe Wang; Xue Dong; Sibo Tian; Xiaojun Jiang; Ashesh B. Jani; Tian Liu; Walter J. Curran; Pretesh Patel; Xiaofeng Yang
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Paper Abstract

Segmentation of the prostate in 3D CT images is a crucial step in treatment planning and procedure guidance such as brachytherapy and radiotherapy. However, manual segmentation of the prostate is very time-consuming and depends on the experience of the clinician. On the contrary, automated prostate segmentation is more helpful in practice, whereas the task is very challenging due to low soft-tissue contrast in CT images. In this paper, we propose a 3D deeply supervised fully-convolutional-network (FCN) with dilated convolution kernel to automatically segment prostate in CT images. A deep supervision strategy could acquire more powerful discriminative capability and accelerate the optimization convergence in training stage, while concatenating the dilated convolution enlarges the receptive field to extract more global contextual information for accurate prostate segmentation. The presented method was evaluated using 15 prostate CT images and obtained a mean Dice similarity coefficient (DSC) of 0.85±0.04 and mean surface distance (MSD) of 1.92±0.46 mm. The experimental results show that our approach yields accurate CT prostate segmentation, which can be employed for the prostate-cancer treatment planning of brachytherapy and external beam radiotherapy.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492S (15 March 2019); doi: 10.1117/12.2512547
Show Author Affiliations
Bo Wang, Ningxia Univ. (China)
Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Xue Dong, Emory Univ. (United States)
Sibo Tian, Emory Univ. (United States)
Xiaojun Jiang, Emory Univ. (United States)
Ashesh B. Jani, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Pretesh Patel, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)

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

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