GrInAdapt: Scaling Retinal Vessel Structural Map Segmentation Through Grounding, Integrating and Adapting Multi-device, Multi-site, and Multi-modal Fundus Domains
Journal:
arXiv
Published Date:
Mar 8, 2025
Abstract
Retinal vessel segmentation is critical for diagnosing ocular conditions, yet
current deep learning methods are limited by modality-specific challenges and
significant distribution shifts across imaging devices, resolutions, and
anatomical regions. In this paper, we propose GrInAdapt, a novel framework for
source-free multi-target domain adaptation that leverages multi-view images to
refine segmentation labels and enhance model generalizability for optical
coherence tomography angiography (OCTA) of the fundus of the eye. GrInAdapt
follows an intuitive three-step approach: (i) grounding images to a common
anchor space via registration, (ii) integrating predictions from multiple views
to achieve improved label consensus, and (iii) adapting the source model to
diverse target domains. Furthermore, GrInAdapt is flexible enough to
incorporate auxiliary modalities such as color fundus photography, to provide
complementary cues for robust vessel segmentation. Extensive experiments on a
multi-device, multi-site, and multi-modal retinal dataset demonstrate that
GrInAdapt significantly outperforms existing domain adaptation methods,
achieving higher segmentation accuracy and robustness across multiple domains.
These results highlight the potential of GrInAdapt to advance automated retinal
vessel analysis and support robust clinical decision-making.
