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.

翻译：医学图像中的血管分割是血管疾病诊断和治疗规划中的重要任务之一。虽然基于学习的分割方法已被广泛研究，但监督方法需要大量标注标签，且复杂背景结构使得神经网络难以以无监督方式分割血管。为此，我们提出了一种新颖的扩散对抗表示学习（DARL）模型，该模型利用去噪扩散概率模型并结合对抗学习，将其应用于血管分割。具体而言，对于自监督血管分割，DARL通过扩散模块学习背景信号，使生成模块能够有效提供血管表示。此外，基于所提出的可切换空间自适应去归一化的对抗学习，我们的模型不仅估计合成虚假血管图像，还生成血管分割掩模，从而进一步使模型捕获与血管相关的语义信息。一旦所提出的模型训练完成，它能够单步生成分割掩模，并可用于冠状动脉造影和视网膜图像的一般血管结构分割。在多个数据集上的实验结果表明，我们的方法显著优于现有的无监督和自监督血管分割方法。