面向宽域视网膜血管分割的卷积提示
Convolutional Prompting for Broad-Domain Retinal Vessel Segmentation
AI总结：
本文提出双卷积提示（DCP）模块，通过位置和通道维度的局部提示提取域特定特征，将R2AU-Net转变为适用于多域的统一模型，解决宽域视网膜血管分割任务，并在构建的宽域数据集上表现优于八种基线方法。
摘要：
以往关于视网膜血管分割的研究主要针对特定图像域，多为彩色眼底摄影（CFP）。本文尝试攻克更具挑战性的宽域视网膜血管分割（BD-RVS）任务，旨在开发一个适用于CFP、SLO、UWF、OCTA和FFA等多种域的统一模型。为此，我们提出双卷积提示（DCP），通过沿位置和通道维度的局部提示来学习提取域特定特征。DCP被设计为即插即用模块，能有效将基于R2AU-Net的血管分割网络转变为统一模型，且无需修改其网络结构。为评估，我们使用五个公开的特定域数据集构建了一个宽域数据集，包括ROSSA、FIVES、IOSTAR、PRIME-FP20和VAMPIRE。为在宽域数据集上对BD-RVS进行基准测试，我们重新利用了多个原本在其他背景下开发的现有方法，共生成八种基线方法。大量实验表明，所提方法在BD-RVS任务上表现优于基线方法。
英文摘要：
Previous research on retinal vessel segmentation is targeted at a specific image domain, mostly color fundus photography (CFP). In this paper we make a brave attempt to attack a more challenging task of broad-domain retinal vessel segmentation (BD-RVS), which is to develop a unified model applicable to varied domains including CFP, SLO, UWF, OCTA and FFA. To that end, we propose Dual Convoltuional Prompting (DCP) that learns to extract domain-specific features by localized prompting along both position and channel dimensions. DCP is designed as a plug-in module that can effectively turn a R2AU-Net based vessel segmentation network to a unified model, yet without the need of modifying its network structure. For evaluation we build a broad-domain set using five public domain-specific datasets including ROSSA, FIVES, IOSTAR, PRIME-FP20 and VAMPIRE. In order to benchmark BD-RVS on the broad-domain dataset, we re-purpose a number of existing methods originally developed in other contexts, producing eight baseline methods in total. Extensive experiments show the the proposed method compares favorably against the baselines for BD-RVS.
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。
