Universal Vessel Segmentation for Multi-Modality Retinal Images

TL;DR

This paper introduces a universal, modality-agnostic retinal vessel segmentation model capable of accurately segmenting vessels across multiple retinal imaging modalities without requiring fine-tuning for each modality.

Contribution

The work presents the first universal model for multi-modality retinal vessel segmentation, eliminating the need for separate models or fine-tuning for different imaging modalities.

Findings

Achieves comparable performance to state-of-the-art fine-tuned models.

Demonstrates versatility across multiple retinal imaging modalities.

First to address modality-agnostic retinal vessel segmentation.

Abstract

We identify two major limitations in the existing studies on retinal vessel segmentation: (1) Most existing works are restricted to one modality, i.e., the Color Fundus (CF). However, multi-modality retinal images are used every day in the study of the retina and diagnosis of retinal diseases, and the study of vessel segmentation on other modalities is scarce; (2) Even though a few works extended their experiments to new modalities such as the Multi-Color Scanning Laser Ophthalmoscopy (MC), these works still require fine-tuning a separate model for the new modality. The fine-tuning will require extra training data, which is difficult to acquire. In this work, we present a novel universal vessel segmentation model (URVSM) for multi-modality retinal images. In addition to performing the study on a much wider range of image modalities, we also propose a universal model to segment the vessels in all these commonly used modalities. While being much more versatile compared with existing methods, our universal model also demonstrates comparable performance to the state-of-the-art fine-tuned methods. To the best of our knowledge, this is the first work that achieves modality-agnostic retinal vessel segmentation and the first to study retinal vessel segmentation in several novel modalities.