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Journal of Medical Imaging • new

PSNet: prostate segmentation on MRI based on a convolutional neural network
Author(s): Zhiqiang Tian; Lizhi Liu; Zhenfeng Zhang; Baowei Fei
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Paper Abstract

Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. We proposed 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, which uses prostate MRI and the corresponding ground truths as inputs. The learned CNN model can be used to make an inference for pixel-wise segmentation. Experiments were performed on three data sets, which contain prostate MRI of 140 patients. The proposed CNN model of prostate segmentation (PSNet) obtained a mean Dice similarity coefficient of 85.0 ± 3.8 % as compared to the manually labeled ground truth. Experimental results show that the proposed model could yield satisfactory segmentation of the prostate on MRI.

Paper Details

Date Published: 17 January 2018
PDF: 6 pages
J. Med. Imag. 5(2) 021208 doi: 10.1117/1.JMI.5.2.021208
Published in: Journal of Medical Imaging Volume 5, Issue 2
Show Author Affiliations
Zhiqiang Tian, Xi'an Jiaotong Univ. (China)
Emory Univ. (United States)
Lizhi Liu, Emory Univ. (United States)
Zhenfeng Zhang, The Second Affiliated Hospital of Guangzhou Medical Univ. (China)
Baowei Fei, Georgia Institute of Technology & Emory Univ. School of Medicine (United States)
The Winship Cancer Institute of Emory Univ. (United States)

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