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:医疗图像中的船舶分离是诊断血管疾病和治疗规划的重要任务之一。虽然已经广泛研究了基于学习的分解方法,但需要大量地面真实标签,以监督的方法和混乱的背景结构进行监管,这使得神经网络难以以不受监督的方式将船只分离。为此,我们引入了一种新的扩散对抗代表学习模式,利用对抗学习的分解扩散概率模型,并将其应用于船舶分解。特别是,对于自监督的船舶分解,DARL使用一个扩散模块学习背景信号,使一个生成模块能够有效地提供船只的演示。此外,通过基于拟议的可转换空间适应性脱光化的对抗性学习,我们模型估计了合成船只图像以及船舶分解面罩,从而进一步将模型与捕捉船只相关的分解信息用于对抗性学习,并应用到船舶分解模型中。一旦经过培训,该模型将生成一个单步分解面面面面面面罩,并可以应用到共血管结构结构结构中的一般分解器结构,使一个模块能够有效地提供船只的演示。此外,通过基于拟议可转换的空间反射法的模拟和现有自我分析方法,从而展示了各种变制的系统图像。