The TUM LapChole dataset for the M2CAI 2016 workflow challenge

Abstract

In this technical report we present our collected dataset of laparoscopic cholecystectomies (LapChole). Laparoscopic videos of a total of 20 surgeries were recorded and annotated with surgical phase labels, of which 15 were randomly pre-determined as training data, while the remaining 5 videos are selected as test data. This dataset was later included as part of the M2CAI 2016 workflow detection challenge during MICCAI 2016 in Athens.

5 pages, 2 figures, preliminary reference for published dataset (until larger comparison study of workshop organizers is published)

References in corpus (1)

• EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

Cited by in corpus (8)

• CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
• Towards Holistic Surgical Scene Understanding
• CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
• m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks
• Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features
• Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
• Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review
• Progress Estimation and Phase Detection for Sequential Processes