  Vessel segmentation in medical images is one of the important tasks in the
diagnosis of vascular diseases and therapy planning. Although learning-based
segmentation approaches have been extensively studied, a large amount of
ground-truth labels are required in supervised methods and confusing background
structures make neural networks hard to segment vessels in an unsupervised
manner. To address this, here we introduce a novel diffusion adversarial
representation learning (DARL) model that leverages a denoising diffusion
probabilistic model with adversarial learning, and apply it to vessel
segmentation. In particular, for self-supervised vessel segmentation, DARL
learns the background signal using a diffusion module, which lets a generation
module effectively provide vessel representations. Also, by adversarial
learning based on the proposed switchable spatially-adaptive denormalization,
our model estimates synthetic fake vessel images as well as vessel segmentation
masks, which further makes the model capture vessel-relevant semantic
information. Once the proposed model is trained, the model generates
segmentation masks in a single step and can be applied to general vascular
structure segmentation of coronary angiography and retinal images. Experimental
results on various datasets show that our method significantly outperforms
existing unsupervised and self-supervised vessel segmentation methods.
