VAMAE: Vessel-Aware Masked Autoencoders for OCT Angiography

TL;DR

VAMAE introduces a vessel-aware masked autoencoder for OCTA images that emphasizes vascular structures and topology, improving vessel segmentation especially with limited labels.

Contribution

The paper presents a novel vessel-aware masking and multi-target reconstruction approach tailored for OCTA images, enhancing self-supervised learning of vascular features.

Findings

Improved vessel segmentation performance over standard methods.

Vessel-aware masking enhances focus on vascular regions.

Multi-target reconstruction captures appearance, structure, and topology.

Abstract

Optical coherence tomography angiography (OCTA) provides non-invasive visualization of retinal microvasculature, but learning robust representations remains challenging due to sparse vessel structures and strong topological constraints. Many existing self-supervised learning approaches, including masked autoencoders, are primarily designed for dense natural images and rely on uniform masking and pixel-level reconstruction, which may inadequately capture vascular geometry. We propose VAMAE, a vessel-aware masked autoencoding framework for self-supervised pretraining on OCTA images. The approach incorporates anatomically informed masking that emphasizes vessel-rich regions using vesselness and skeleton-based cues, encouraging the model to focus on vascular connectivity and branching patterns. In addition, the pretraining objective includes reconstructing multiple complementary targets, enabling the model to capture appearance, structural, and topological information. We evaluate the proposed pretraining strategy on the OCTA-500 benchmark for several vessel segmentation tasks under varying levels of supervision. The results indicate that vessel-aware masking and multi-target reconstruction provide consistent improvements over standard masked autoencoding baselines, particularly in limited-label settings, suggesting the potential of geometry-aware self-supervised learning for OCTA analysis.