  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. We
believe that sharing annotation guidelines can help build trustworthy
multicentric datasets for assessing generalizability of performance, thus
accelerating the clinical translation of deep learning models for surgical
video analysis.
