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PubMed: 40000637 DOI: 10.1038/s41597-025-04642-4
Endoscapes, a critical view of safety and surgical scene segmentation dataset for laparoscopic cholecystectomy.
Sci Data 12(1):331
Authors: Pietro Mascagni, Deepak Alapatt, Aditya Murali, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Guido Costamagna, Didier Mutter, Jacques Marescaux, Bernard Dallemagne, Nicolas Padoy
Affiliations
- IHU Strasbourg, Strasbourg, France.
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
- ICube, University of Strasbourg, CNRS, Strasbourg, France.
- IHU Strasbourg, Strasbourg, France.
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
- IRCAD, Institute for Research against Digestive Cancer, Strasbourg, France.
Abstract
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.
MeSH
Cholecystectomy, Laparoscopic, Safety, Information Storage And Retrieval, Cholecystectomy, Surgeons