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Abstract #4742

An fully automatic prostate segmentation based on generative adversarial networks

Yi Zhu1, Rong Wei1, Ge Gao2, Jue Zhang1,3, and Xiaoying Wang2

1Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 2Peking University First Hospital, Beijing, China, 3College of Engineering, Peking University, Beijing, China

Automatic prostate segmentation in MR images is essential in many clinical applications. Generative adversarial networks(GAN) have recently gained interests due to their promising ability in generating images which are difficult to distinguish from real images. In this paper, we propose an automatic and efficient algorithm base on GAN to segment the prostate contour and make the prostate segmentation shape more realistic. Our restult shows that the mean segmentation accuracy in test dataset is 90.3%±5.5. It indicates that the proposed strategy is feasible for segmentation of prostate MR images.

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