Deep convolutional neural network for prostate MR segmentation

Zhiqiang Tian; Lizhi Liu; Baowei Fei

doi:10.1117/12.2254621

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3 March 2017 Deep convolutional neural network for prostate MR segmentation

Zhiqiang Tian, Lizhi Liu, Baowei Fei

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Proceedings Volume 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling; 101351L (2017) https://doi.org/10.1117/12.2254621
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

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Zhiqiang Tian, Lizhi Liu, and Baowei Fei "Deep convolutional neural network for prostate MR segmentation", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351L (3 March 2017); https://doi.org/10.1117/12.2254621

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