The TUM LapChole dataset for the M2CAI 2016 workflow challenge
R. Stauder, D. Ostler, M. Kranzfelder, S. Koller, H. Feußner, and N. Navab. CoRR, (2016)cite arxiv:1610.09278Comment: 5 pages, 2 figures, preliminary reference for published dataset (until larger comparison study of workshop organizers is published).
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.
Description
[1610.09278] The TUM LapChole dataset for the M2CAI 2016 workflow challenge
cite arxiv:1610.09278Comment: 5 pages, 2 figures, preliminary reference for published dataset (until larger comparison study of workshop organizers is published)
%0 Journal Article
%1 stauder2016lapchole
%A Stauder, Ralf
%A Ostler, Daniel
%A Kranzfelder, Michael
%A Koller, Sebastian
%A Feußner, Hubertus
%A Navab, Nassir
%D 2016
%J CoRR
%K challenge learning m2cai phase surgery
%T The TUM LapChole dataset for the M2CAI 2016 workflow challenge
%U http://arxiv.org/abs/1610.09278
%X 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.
@article{stauder2016lapchole,
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.},
added-at = {2021-08-18T17:23:18.000+0200},
author = {Stauder, Ralf and Ostler, Daniel and Kranzfelder, Michael and Koller, Sebastian and Feußner, Hubertus and Navab, Nassir},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2061b93f405f6ff90ad5f40166a763a87/felixholm},
description = {[1610.09278] The TUM LapChole dataset for the M2CAI 2016 workflow challenge},
interhash = {0bc87763e80344b2358a3113b8db7deb},
intrahash = {061b93f405f6ff90ad5f40166a763a87},
journal = {CoRR},
keywords = {challenge learning m2cai phase surgery},
note = {cite arxiv:1610.09278Comment: 5 pages, 2 figures, preliminary reference for published dataset (until larger comparison study of workshop organizers is published)},
timestamp = {2021-08-18T15:23:18.000+0200},
title = {The TUM LapChole dataset for the M2CAI 2016 workflow challenge},
url = {http://arxiv.org/abs/1610.09278},
year = 2016
}