Endoscapes2023, A Critical View of Safety and Surgical Scene Segmentation Dataset for Laparoscopic Cholecystectomy

Minimally invasive image-guided surgery heavily relies on vision. Deep
learning models for surgical video analysis can support surgeons in visual
tasks such as assessing the critical view of safety (CVS) in laparoscopic
cholecystectomy, potentially contributing to surgical safety and efficiency.
However, the performance, reliability, and reproducibility of such models are
deeply dependent on the availability of data with high-quality annotations. To
this end, we release Endoscapes2023, a dataset comprising 201 laparoscopic
cholecystectomy videos with regularly spaced frames annotated with
segmentation masks of surgical instruments and hepatocystic anatomy, as well
as assessments of the criteria defining the CVS by three trained surgeons
following a public protocol. Endoscapes2023 enables the development of models
for object detection, semantic and instance segmentation, and CVS prediction,
contributing to safe laparoscopic cholecystectomy.