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

• URL: http://arxiv.org/abs/2106.10916v1
• Date: Mon, 21 Jun 2021 08:27:38 GMT
• Title: Surgical data science for safe cholecystectomy: a protocol for segmentation of hepatocystic anatomy and assessment of the critical view of safety
• Authors: Pietro Mascagni and Deepak Alapatt, Alain Garcia, Nariaki Okamoto, Armine Vardazaryan, Guido Costamagna, Bernard Dallemagne, Nicolas Padoy
• Abstract summary: We present a protocol, checklists, and visual examples to promote consistent annotation of hepatocystic anatomy and CVS criteria. Deep learning models for surgical video analysis could support visual tasks such as assessing the critical view of safety in laparoscopic cholecystectomy.
• Score: 2.7457279076218666
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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