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<p>Minimally invasive glaucoma surgery (MIGS) presents substantial challenges due to its intricate procedures and high technical demands, necessitating robust surgical navigation systems. The development of these systems is highly dependent on the availability of precisely annotated datasets. Addressing this need, we have created the first multicenter, large-scale, fine-annotated surgical video dataset for MIGS. This dataset includes millions of frames, extensively annotated with both surgical instruments and anatomical structures. The annotations are organized into two primary tasks: Task I, which involves phase recognition to identify different stages of the surgery, and Task II, which focuses on semantic segmentation of surgical instruments and anatomical details. By providing a foundational resource for training computer vision models, our dataset aims to facilitate the development of advanced computer-assisted interventions that can improve the precision and safety of glaucoma surgeries.</p>

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Multicenter fine annotated surgical video dataset for minimally invasive glaucoma surgery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

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  • Deming Wang,
  • Yinhang Zhang,
  • Yirui Li,
  • Kai Zhou,
  • Zefeng Yang,
  • Zeng You,
  • Dilimulati Xiaokaiti,
  • Fengqi Zhou,
  • Yu Chen,
  • Xiaoyan Li,
  • Xiaowei Yan,
  • Guihua Xu,
  • Zhixuan Wang,
  • Jiaxuan Jiang,
  • Lin Xie,
  • Xiaomin Zhu,
  • Ling Jin,
  • Zhenyu Wang,
  • Ying Han,
  • Guangxian Tang,
  • Sujie Fan,
  • Fei Li,
  • Mingkui Tan,
  • Xiulan Zhang

摘要

Minimally invasive glaucoma surgery (MIGS) presents substantial challenges due to its intricate procedures and high technical demands, necessitating robust surgical navigation systems. The development of these systems is highly dependent on the availability of precisely annotated datasets. Addressing this need, we have created the first multicenter, large-scale, fine-annotated surgical video dataset for MIGS. This dataset includes millions of frames, extensively annotated with both surgical instruments and anatomical structures. The annotations are organized into two primary tasks: Task I, which involves phase recognition to identify different stages of the surgery, and Task II, which focuses on semantic segmentation of surgical instruments and anatomical details. By providing a foundational resource for training computer vision models, our dataset aims to facilitate the development of advanced computer-assisted interventions that can improve the precision and safety of glaucoma surgeries.