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

arXiv: https://arxiv.org/abs/2106.10916 · HTML: https://mlsanity.com/paper/2106.10916/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [Surgical Data Science: Enabling Next-Generation Surgery](https://mlsanity.com/paper/1701.06482/) (2017)

## Built on by (most cited first, 8 of 8)

- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [Latent Graph Representations for Critical View of Safety Assessment](https://mlsanity.com/paper/2212.04155/) (2022)
- [The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark](https://mlsanity.com/paper/2312.12429/) (2023)
- [Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation](https://mlsanity.com/paper/2112.13815/) (2021)
- [The SAGES Critical View of Safety Challenge: A Global Benchmark for AI-Assisted Surgical Quality Assessment](https://mlsanity.com/paper/2509.17100/) (2025)
- [CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching](https://mlsanity.com/paper/2407.06795/) (2024)
- [SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy](https://mlsanity.com/paper/2603.29962/) (2026)
- [RobustSurg: Tackling domain generalisation for out-of-distribution surgical scene segmentation](https://mlsanity.com/paper/2512.02188/) (2025)

## Abstract (arXiv)

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.