Abstract: The automatic segmentation of prostate MR images has been widely used in the diagnosis and treatment of prostate cancer. However, due to the significant changes in the shape of the prostate and low contrast with adjacent tissues, traditional segmentation methods still have disadvantages such as low accuracy and slow speed. Generative adversarial networks (GAN) have shown superior performance in computer vision tasks, so this paper proposes a method of training segmentation networks using the concept of adversarial learning to achieve end-to-end automatic segmentation of prostate MR images. The model framework is mainly composed of a segmentation network and a discriminant network. The segmentation network generates a segmentation prediction map, and the discrimination network judges whether the input comes from a real label or a segmentation prediction. At the same time, the receptive field block (RFB) is integrated in the segmentation network to acquire and fuse multi-scale information of deep features, improve the recognition rate and robustness of features, and improve the segmentation performance of the network. Through verification on the PROMISE12 data set, the DSC and HD of the model are 89.56% and 7.65 mm, respectively. 

Key words: prostate MR image, image segmentation, adversarial learning, multi-scale feature