LAUSR

Prostate diseases are very common in men. Accurate segmentation of the prostate plays a significant role in further clinical treatment and diagnosis. There have been some methods that combine the segmentation network and generative adversarial network, using the adversarial training to boost the performance of segmentation network. However, the traditional adversarial training is unstable, which is hard to train. This attribute can easily lead to training failure. In this paper, we propose a segmentation network with self-attention adversarial training based on Wasserstein distance to tackle the problem. First, a segmentation network with residual connection and attention mechanism is deployed to generate the prostate segmentation prediction. Then, a self-attention discriminator network is added to the segmentation network to discriminate the prediction from ground truth. In the discriminator network, we replace the cross-entropy loss function with Wasserstein distance loss function which is better to measure the difference between distributions. The comparative experiments suggest our method is more stable than traditional adversarial training and achieves state-of-the-art performance.