XA-Sim2Real: Adaptive Representation Learning for Vessel Segmentation in X-Ray Angiography

Accurate vessel segmentation from X-ray Angiography (XA) is essential for various medical applications, including diagnosis, treatment planning, and image-guided interventions. However, learning-based methods face challenges such as inaccurate or insufficient manual annotations, anatomical variability, and data heterogeneity across different medical institutions. In this paper, we propose XA-Sim2Real, a novel adaptive framework for vessel segmentation in XA image. Our approach leverages Digitally Reconstructed Vascular Radiographs (DRVRs) and a two-stage adaptation process to achieve promising segmentation performance on XA image without the need for manual annotations. The first stage involves an XA simulation module for generating realistic simulated XA images from patients’ CT angiography data, providing more accurate vascular shapes and backgrounds than existing curvilinear-structure simulation methods. In the second stage, a novel adaptive representation alignment module addresses data heterogeneity by performing intra-domain adaptation for the complex and diverse nature of XA data in different settings. This module utilizes self-supervised and contrastive learning mechanisms to learn adaptive representations for unlabeled XA image. We extensively evaluate our method on both public and in-house datasets, demonstrating superior performance compared to state-of-the-art self-supervised methods and competitive performance compared to supervised method.