Self-Supervised Vessel Segmentation via Adversarial Learning

Vessel segmentation is critically essential for diagnosing a series of diseases, e.g., coronary artery disease and retinal disease. However, annotating vessel segmentation maps of medical images is notoriously challenging due to the tiny and complex vessel structures, leading to insufficient available annotated datasets for existing supervised methods and domain adaptation methods. The subtle structures and confusing background of medical images further suppress the efficacy of unsupervised methods. In this paper, we propose a self-supervised vessel segmentation method via adversarial learning. Our method learns vessel representations by training an attention-guided generator and a segmentation generator to simultaneously synthesize fake vessels and segment vessels out of coronary angiograms. To support the research, we also build the first X-ray angiography coronary vessel segmentation dataset, named XCAD. We evaluate our method extensively on multiple vessel segmentation datasets, including the XCAD dataset, the DRIVE dataset, and the STARE dataset. The experimental results show our method suppresses unsupervised methods significantly and achieves competitive performance compared with supervised methods and traditional methods.