Surgical data science for safe cholecystectomy: a protocol for segmentation of hepatocystic anatomy and assessment of the critical view of safety

Understand

Minimally invasive image-guided surgery heavily relies on vision.

• Deep learning models for surgical video analysis could therefore support visual tasks such as assessing the critical view of safety (CVS) in laparoscopic cholecystectomy (LC), potentially contributing to surgical safety and efficiency.
• However, the performance, reliability and reproducibility of such models are deeply dependent on the quality of data and annotations used in their development.
• Here, we present a protocol, checklists, and visual examples to promote consistent annotation of hepatocystic anatomy and CVS criteria.