  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.
