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Proceedings Paper

Deep convolutional neural network for prostate MR segmentation
Author(s): Zhiqiang Tian; Lizhi Liu; Baowei Fei
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Paper Abstract

Automatic segmentation of the prostate in magnetic resonance imaging (MRI) has many applications in prostate cancer diagnosis and therapy. We propose a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage based on prostate MR images and the corresponding ground truths, and learns to make inference for pixel-wise segmentation. Experiments were performed on our in-house data set, which contains prostate MR images of 20 patients. The proposed CNN model obtained a mean Dice similarity coefficient of 85.3%±3.2% as compared to the manual segmentation. Experimental results show that our deep CNN model could yield satisfactory segmentation of the prostate.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351L (3 March 2017); doi: 10.1117/12.2254621
Show Author Affiliations
Zhiqiang Tian, Emory Univ. (United States)
Lizhi Liu, Emory Univ. (United States)
Baowei Fei, Winship Cancer Institute, Emory Univ. (United States)
Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10135:
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Baowei Fei, Editor(s)

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