VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation

TL;DR

VesselMorph introduces a shape-aware representation for retinal vessel segmentation that enhances model generalization across diverse imaging domains by leveraging vessel morphology invariant to intensity variations.

Contribution

The paper proposes a novel Hessian-based tensor field to encode vessel shape, improving domain generalization in retinal vessel segmentation.

Findings

Outperforms existing methods on six public datasets.

Achieves superior generalization across different imaging domains.

Effectively captures vessel morphology invariant to intensity changes.

Abstract

Due to the absence of a single standardized imaging protocol, domain shift between data acquired from different sites is an inherent property of medical images and has become a major obstacle for large-scale deployment of learning-based algorithms. For retinal vessel images, domain shift usually presents as the variation of intensity, contrast and resolution, while the basic tubular shape of vessels remains unaffected. Thus, taking advantage of such domain-invariant morphological features can greatly improve the generalizability of deep models. In this study, we propose a method named VesselMorph which generalizes the 2D retinal vessel segmentation task by synthesizing a shape-aware representation. Inspired by the traditional Frangi filter and the diffusion tensor imaging literature, we introduce a Hessian-based bipolar tensor field to depict the morphology of the vessels so that the shape information is taken into account. We map the intensity image and the tensor field to a latent space for feature extraction. Then we fuse the two latent representations via a weight-balancing trick and feed the result to a segmentation network. We evaluate on six public datasets of fundus and OCT angiography images from diverse patient populations. VesselMorph achieves superior generalization performance compared with competing methods in different domain shift scenarios.