The TUM LapChole dataset for the M2CAI 2016 workflow challenge
Ralf Stauder, Daniel Ostler, Michael Kranzfelder, Sebastian Koller,, Hubertus Feu{\ss}ner, Nassir Navab
TL;DR
This paper introduces the TUM LapChole dataset comprising laparoscopic videos of 20 surgeries, annotated with surgical phases, to support the M2CAI 2016 workflow detection challenge.
Contribution
It provides a publicly available, annotated dataset for surgical workflow analysis, facilitating research in automated phase recognition.
Findings
Dataset includes 20 annotated surgeries.
Split into 15 training and 5 test videos.
Supports development of surgical workflow algorithms.
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.
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Taxonomy
TopicsMedical Imaging Techniques and Applications · COVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging