mlsanityData to 14 Oct 2026

RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation

arXiv 2307.06577 ↗First posted 13 Jul 2023cs.CV cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 92 references that are in the map. First posted on arXiv 13 Jul 2023.

Builds on

30 of 92 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  2. 2023RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentationcited by 5
  3. 2025Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modellingcited by 0
  4. 2026TubeMLLM: A Foundation Model for Topology Knowledge Exploration in Vessel-like Anatomycited by 0