Uenet: A Novel Generative Adversarial Network For Angiography Image Segmentation

Convolutional neural networks (CNNs) have been widely used in medical image segmentation. Vessel segmentation in coronary angiography remains a challenging task. It is a great challenge to extract fine features of coronary artery for segmentation due to the poor opacification, numerous overlap of different artery segments and high similarity between artery segments and soft tissues in an angiography image, which results in a sub-optimal segmentation performance. In this paper, we propose an adapted generative adversarial networks (GANs) to complete the conversion from coronary angiography image to semantic segmentation image. We implemented an adapted U-net as the generator, and a novel 3-layer pyramid structure as the discriminator. During the training period, multi-scale inputs were fed into the discriminator to optimize the objective functions, producing high-definition segmentation results. Due to the generative adversarial mechanism, both generator and discriminator can extract fine feature of coronary artery. Our method effectively solves the problems of segmentation discontinuity and intra-class inconsistencies. Experiment shows that our method improves the segmentation accuracy effectively comparing to other vessel segmentation methods.