VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation

TL;DR

VasoMIM is a novel self-supervised learning framework that enhances vessel segmentation in X-ray angiograms by integrating vascular anatomy knowledge into masked image modeling, addressing class imbalance and improving representation quality.

Contribution

It introduces an anatomy-guided masking strategy and an anatomical consistency loss to improve vascular feature learning in self-supervised pre-training.

Findings

Achieves state-of-the-art results on three datasets.

Effectively captures vascular structures despite class imbalance.

Enhances the discriminability of vascular representations.

Abstract

Accurate vessel segmentation in X-ray angiograms is crucial for numerous clinical applications. However, the scarcity of annotated data presents a significant challenge, which has driven the adoption of self-supervised learning (SSL) methods such as masked image modeling (MIM) to leverage large-scale unlabeled data for learning transferable representations. Unfortunately, conventional MIM often fails to capture vascular anatomy because of the severe class imbalance between vessel and background pixels, leading to weak vascular representations. To address this, we introduce Vascular anatomy-aware Masked Image Modeling (VasoMIM), a novel MIM framework tailored for X-ray angiograms that explicitly integrates anatomical knowledge into the pre-training process. Specifically, it comprises two complementary components: anatomy-guided masking strategy and anatomical consistency loss. The former preferentially masks vessel-containing patches to focus the model on reconstructing vessel-relevant regions. The latter enforces consistency in vascular semantics between the original and reconstructed images, thereby improving the discriminability of vascular representations. Empirically, VasoMIM achieves state-of-the-art performance across three datasets. These findings highlight its potential to facilitate X-ray angiogram analysis.