@inproceedings{e39714e9ce59462193ebd81e0f748e80,
title = "Deep convolutional neural network for prostate MR segmentation",
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.",
keywords = "Convolutional neural network, Deep learning, Magnetic resonance imaging (MRI), Prostate segmentation",
author = "Zhiqiang Tian and Lizhi Liu and Baowei Fei",
note = "Funding Information: This research is supported in part by NIH grants (CA176684, R01CA156775 and CA204254). Publisher Copyright: {\textcopyright} 2017 SPIE.; Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 14-02-2017 Through 16-02-2017",
year = "2017",
doi = "10.1117/12.2254621",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Webster, {Robert J.} and Baowei Fei",
booktitle = "Medical Imaging 2017",
}