DIFFUSION ADVERSARIAL REPRESENTATION LEARN-ING FOR SELF-SUPERVISED VESSEL SEGMENTATION

Abstract

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 groundtruth 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.

1. INTRODUCTION

In the clinical diagnosis of vascular diseases, vessel segmentation is necessary to analyze the vessel structures and therapy planning. In particular, when diagnosing coronary artery disease, X-ray angiography is taken to enhance vessel visualization by injecting a contrast agent into the blood vessels (Cong et al., 2015) . However, it is challenging to extract vessels accurately due to low contrast, motion artifacts, many tiny branches, structural interference in the backgrounds, etc (Xia et al., 2019; Chen et al., 2014) . To segment vascular structures, various segmentation methods have been explored. Traditional optimization models (Law & Chung, 2009; Taghizadeh Dehkordi et al., 2014) typically require complicated preprocessing steps and manual tuning. Furthermore, they are computationally expensive to process many images. On the other hand, learning-based methods (Nasr-Esfahani et al., 2016; Fan et al., 2018; Chen et al., 2019) generate segmentation maps in real-time once the models are trained. However, supervised methods require a huge amount of labeled data for training, which complicates their use in practical applications. Also, existing unsupervised methods designed on natural images are difficult to apply to medical vessel images due to low contrast subtle branches and confusing background structures. Although a recent self-supervised method (Ma et al., 2021) is presented to learn vessel representations, this requires two different adversarial networks to segment vessels, which leads to increasing training complexity. Recently, diffusion models such as denoising diffusion probabilistic model (DDPM) (Ho et al., 2020) has become one of the main research topics in modeling data distribution and sampling diverse images. By learning the Markov transformation of the reverse diffusion process from Gaussian

