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Multicenter fine annotated surgical video dataset for minimally invasive glaucoma surgery
Deming Wang1, Yinhang Zhang1,2, Yirui Li3
1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
Scientific Data
|June 2, 2026
Summary
We created a large, annotated surgical video dataset for minimally invasive glaucoma surgery (MIGS). This resource aids in developing AI tools to enhance surgical precision and patient safety.
Area of Science:
- Ophthalmology
- Computer Vision
- Medical Imaging
Background:
- Minimally invasive glaucoma surgery (MIGS) is technically demanding.
- Advanced surgical navigation systems require precise data.
- Existing datasets lack the scale and annotation detail for MIGS.
Purpose of the Study:
- To develop the first large-scale, multicenter, fine-annotated surgical video dataset for MIGS.
- To provide a foundational resource for training computer vision models for surgical assistance.
Main Methods:
- Collected a large dataset of MIGS surgical videos from multiple centers.
- Annotated millions of video frames with surgical instruments and anatomical structures.
- Organized annotations for phase recognition and semantic segmentation tasks.
Main Results:
- Created a comprehensive dataset with extensive annotations.
- Dataset supports two key tasks: surgical phase recognition and semantic segmentation.
- Millions of frames are available for model training.
Conclusions:
- The dataset is a crucial resource for advancing computer-assisted interventions in MIGS.
- Facilitates the development of AI tools to improve surgical precision and safety.
- Aims to enhance outcomes for patients undergoing glaucoma surgery.
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