Deep convolutional neural network for prostate MR segmentation

Zhiqiang Tian; Lizhi Liu; Baowei Fei

doi:10.1117/12.2254621

3 March 2017 Deep convolutional neural network for prostate MR segmentation

Zhiqiang Tian, Lizhi Liu, Baowei Fei

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.

Conference Presentation

Citation Download Citation

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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