多模态视网膜图像的通用血管分割
Universal Vessel Segmentation for Multi-Modality Retinal Images
AI总结：
本文提出通用血管分割模型URVSM，实现多模态视网膜图像的血管分割，首次实现模态无关分割并拓展新模态研究。
摘要：
我们识别出现有视网膜血管分割研究中的两个主要局限性：(1) 大多数现有工作局限于单一模态，即彩色视网膜摄影（CF）。然而，多模态视网膜图像每天都在视网膜研究和视网膜疾病诊断中被使用，而对其他模态进行血管分割的研究很少；(2) 尽管一些工作将实验扩展到新模态，如多色扫描激光眼底镜（MC），这些工作仍需要为新模态单独微调模型。微调需要额外的训练数据，这难以获得。在本工作中，我们提出了一种新颖的通用血管分割模型（URVSM）用于多模态视网膜图像。除了在更广泛的图像模态上进行研究外，我们还提出了一种通用模型，用于分割所有这些常用模态中的血管。与现有方法相比，我们的通用模型更加灵活，同时在性能上与最先进的微调方法相当。据我们所知，这是首个实现模态无关视网膜血管分割的工作，也是首个研究几种新模态视网膜血管分割的工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
