Deep convolutional neural network for prostate MR segmentation

• Type of Material: Article
• Authors: Zhiqiang Tian, Emory University; Lizhi Liu, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2017-01-01
• Publisher: Springer Verlag (Germany)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 SPIE.
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2254621
• Title of Journal or Parent Work: International Journal of Computer Assisted Radiology and Surgery
• ISSN: 1861-6410
• Volume: 10135
• Grant/Funding Information: This research is supported in part by NIH grants (CA176684, R01CA156775 and CA204254).
• Keywords: REGISTRATION; IMAGE; deep learning; Optics; Physical Sciences; prostate segmentation; Magnetic resonance imaging (MRI); Radiology, Nuclear Medicine & Medical Imaging; convolutional neural network; Life Sciences & Biomedicine; Science & Technology
• Research Categories: Health Sciences, Radiology

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.