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Related Experiment Video

Updated: Nov 2, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

Challenges in surgical video annotation.

Thomas M Ward¹, Danyal M Fer², Yutong Ban^1,3

¹Surgical AI & Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.

Computer Assisted Surgery (Abingdon, England)

|June 14, 2021

Summary

This summary is machine-generated.

Annotating surgical videos is crucial for computer vision in surgical data science. This review identifies annotation challenges and suggests improvements for clinical translation.

Keywords:

Annotation Image classification Object detection Semantic segmentation Surgical video inter-rater reliability temporal annotation

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Area of Science:

Surgical data science
Computer vision
Medical imaging analysis

Background:

Surgical video annotation is essential for creating ground truth data.
The field of surgical data science has grown significantly over the last decade.
Existing annotation methods face challenges in spatial, temporal, and clinical element identification.

Purpose of the Study:

To review current challenges in surgical video annotation.
To identify difficulties in selecting appropriate annotators.
To propose improvements for surgical video analysis and clinical translation.

Main Methods:

Literature review of surgical video annotation techniques.
Analysis of identified challenges in spatial, temporal, and clinical data annotation.
Discussion of annotator selection criteria and their impact.

Main Results:

Several key challenges in surgical video annotation were identified.
Difficulties in annotator selection were highlighted.
Opportunities for enhancing annotation accuracy and efficiency were discussed.

Conclusions:

Addressing current annotation challenges is vital for advancing surgical data science.
Implementing suggested improvements can facilitate the clinical application of surgical video analysis.
Future research should focus on developing standardized and efficient annotation protocols.