2021/06/21 by Pietro Mascagni, Deepak Alapatt, Mascagni, Pietro +13 · 3 citations
Medicine · #Colorectal Cancer Screening and Detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Pancreatic and Hepatic Oncology Research #Surgical Simulation and Training #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.10916
openalex publication_date 2021/06/21 · openalex created_date 2021/07/05 · openalex updated_date 2026/07/28
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.