首个实现多模态视网膜血管通用分割的模型，无需微调即可跨模态适用。
Universal Vessel Segmentation for Multi-Modality Retinal Images
- 构建通用模型，一次训练覆盖多种视网膜成像模态。
- 在多模态数据上性能接近专用微调模型，且无需额外标注数据。
- 适合需要跨模态分析的临床研究与多中心数据应用。
现有视网膜血管分割研究存在两大局限：其一，多数方法仅针对彩色眼底图像（CF），而实际诊疗中常使用多种模态图像，其他模态的研究稀缺；其二，少数扩展至多色扫描激光眼底镜（MC）等模态的工作仍需为每种新模态单独微调模型，导致依赖难以获取的标注数据。本文提出一种新型通用视网膜血管分割模型（URVSM），可同时处理多种常用成像模态。该模型不仅覆盖更广的模态范围，且无需为每种模态重新训练，性能媲美最优微调方法。据我们所知，这是首个实现模态无关的视网膜血管分割，并首次系统研究了多个新模态下的分割任务。
原文摘要 · Abstract (English)
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.
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