Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis

TL;DR

This paper introduces VasoMIM, a vascular anatomy-aware self-supervised learning framework for X-ray angiogram analysis, utilizing a large new dataset to improve downstream task performance by integrating domain-specific anatomical knowledge.

Contribution

The paper presents VasoMIM, a novel SSL framework with anatomy-guided masking and consistency loss, along with the largest X-ray angiogram dataset, XA-170K, for improved medical image analysis.

Findings

VasoMIM outperforms existing methods on multiple downstream tasks.

The XA-170K dataset enables effective pre-training for angiogram analysis.

Anatomy-aware masking improves the robustness of learned representations.

Abstract

X-ray angiography is the gold standard imaging modality for cardiovascular diseases. However, current deep learning approaches for X-ray angiogram analysis are severely constrained by the scarcity of annotated data. While large-scale self-supervised learning (SSL) has emerged as a promising solution, its potential in this domain remains largely unexplored, primarily due to the lack of effective SSL frameworks and large-scale datasets. To bridge this gap, we introduce a vascular anatomy-aware masked image modeling (VasoMIM) framework that explicitly integrates domain-specific anatomical knowledge. Specifically, VasoMIM comprises two key designs: an anatomy-guided masking strategy and an anatomical consistency loss. The former strategically masks vessel-containing patches to compel the model to learn robust vascular semantics, while the latter preserves structural consistency of vessels between original and reconstructed images, enhancing the discriminability of the learned representations. In conjunction with VasoMIM, we curate XA-170K, the largest X-ray angiogram pre-training dataset to date. We validate VasoMIM on four downstream tasks across six datasets, where it demonstrates superior transferability and achieves state-of-the-art performance compared to existing methods. These findings highlight the significant potential of VasoMIM as a foundation model for advancing a wide range of X-ray angiogram analysis tasks. VasoMIM and XA-170K will be available at https://github.com/Dxhuang-CASIA/XA-SSL.