RVD：一个用于视网膜血管分割的基于手持设备的眼底视频数据集
RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation
AI总结：
本文构建首个基于手持设备采集的眼底视频数据集RVD，提供多粒度空间与时间血管标注，并给出基准评测以支持视网膜血管分割及眼病早诊研究。
摘要：
视网膜血管分割通常基于使用台式设备采集的图像数据集。静态图像自然会丢失视网膜波动的动态特征，导致数据集丰富度下降，而台式设备的使用由于其可及性有限，进一步限制了数据集的可扩展性。考虑到这些局限性，我们推出了首个通过采用手持设备进行数据采集的基于视频的视网膜数据集。该数据集包含从四家不同诊所收集的635个基于智能手机的眼底视频，涉及415名年龄在50至75岁之间的患者。它在空间和时间两个维度上提供全面且精确的视网膜结构标注，旨在推进血管分割领域的发展。具体而言，该数据集提供三个层级的空间标注：用于整体视网膜结构描绘的二值血管掩膜、用于区分静脉和动脉的通用静脉-动脉掩膜，以及用于进一步表征每条动脉和静脉粒度的细粒度静脉-动脉掩膜。此外，该数据集还提供捕捉血管搏动特征的时间标注，以帮助检测需要对血流动力学波动进行细粒度识别的眼部疾病。在应用中，我们的数据集相对于台式设备采集的数据表现出显著的域偏移，从而对现有方法构成巨大挑战。在实验中，我们在该数据集上提供了评估指标和基准结果，反映了其为血管分割任务带来的潜力与挑战。我们希望这一具有挑战性的数据集能够为眼部疾病诊断和早期预防的发展作出重要贡献。
英文摘要：
Retinal vessel segmentation is generally grounded in image-based datasets collected with bench-top devices. The static images naturally lose the dynamic characteristics of retina fluctuation, resulting in diminished dataset richness, and the usage of bench-top devices further restricts dataset scalability due to its limited accessibility. Considering these limitations, we introduce the first video-based retinal dataset by employing handheld devices for data acquisition. The dataset comprises 635 smartphone-based fundus videos collected from four different clinics, involving 415 patients from 50 to 75 years old. It delivers comprehensive and precise annotations of retinal structures in both spatial and temporal dimensions, aiming to advance the landscape of vasculature segmentation. Specifically, the dataset provides three levels of spatial annotations: binary vessel masks for overall retinal structure delineation, general vein-artery masks for distinguishing the vein and artery, and fine-grained vein-artery masks for further characterizing the granularities of each artery and vein. In addition, the dataset offers temporal annotations that capture the vessel pulsation characteristics, assisting in detecting ocular diseases that require fine-grained recognition of hemodynamic fluctuation. In application, our dataset exhibits a significant domain shift with respect to data captured by bench-top devices, thus posing great challenges to existing methods. In the experiments, we provide evaluation metrics and benchmark results on our dataset, reflecting both the potential and challenges it offers for vessel segmentation tasks. We hope this challenging dataset would significantly contribute to the development of eye disease diagnosis and early prevention.
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