Universal Vessel Segmentation for Multi-Modality Retinal Images
This work addresses a practical challenge in medical imaging by enabling more efficient and versatile vessel segmentation for diagnosing retinal diseases, though it is incremental as it builds on existing segmentation techniques.
The authors tackled the problem of retinal vessel segmentation across multiple imaging modalities, which was previously limited to single modalities or required separate fine-tuned models, and developed a universal model that achieves comparable performance to state-of-the-art fine-tuned methods without needing extra training data for new modalities.
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.