  Blood vessel segmentation in medical imaging is one of the essential steps
for vascular disease diagnosis and interventional planning in a broad spectrum
of clinical scenarios in image-based medicine and interventional medicine.
Unfortunately, manual annotation of the vessel masks is challenging and
resource-intensive due to subtle branches and complex structures. To overcome
this issue, this paper presents a self-supervised vessel segmentation method,
dubbed the contrastive diffusion adversarial representation learning (C-DARL)
model. Our model is composed of a diffusion module and a generation module that
learns the distribution of multi-domain blood vessel data by generating
synthetic vessel images from diffusion latent. Moreover, we employ contrastive
learning through a mask-based contrastive loss so that the model can learn more
realistic vessel representations. To validate the efficacy, C-DARL is trained
using various vessel datasets, including coronary angiograms, abdominal digital
subtraction angiograms, and retinal imaging. Experimental results confirm that
our model achieves performance improvement over baseline methods with noise
robustness, suggesting the effectiveness of C-DARL for vessel segmentation.
