The dataset comprises four individual datasets taken from 500+ gynecologic laparoscopic surgeries for the task of automatic content analysis. The individual collections contain image classes depicting general surgical actions, anatomical structures, conducted actions on specific anatomy as well as examples of differing amounts of visible instruments:
Cutting (high frequency)
Suction and Irrigation
|Surgical Actions||Anatomical Structures||Actions on Anatomy||Instrument Count|
|Suction & Irrigation||3036||Uterus||938||Suturing (Uterus)||232||0 Instr.||5100|
|Suturing||12914||Ovary||1162||Suturing (Ovary)||196||1 Instr.||5206|
|Cutting (C)||1185||Oviduct||195||Suturing (Vagina)||263||2 Instr.||5856|
|Cutting (HF)||3752||Liver||138||Suturing (Other)||338||3 Instr.||5271|
For further information about the dataset's organization see 'Readme.txt' within the download archive below.
The dataset is exclusively provided for scientific research purposes and as such cannot be used commercially or for any other purpose. If any other purpose is intended, you may directly contact the originator of the videos, Prof. Dr. Jörg Keckstein.
In addition, reference must be made to the following publication when this dataset is used in any academic and research reports:
A. Leibetseder, S. Petscharnig, M. J. Primus, S. Kletz, B. Münzer, K. Schoeffmann, J. Keckstein, LapGyn4: A Dataset for 4 Automatic Content Analysis Problems in the Domain of Laparoscopic Gynecology, submitted for ACM Multimedia Systems Conference 2018
* Certain images of the Instrument Count dataset are extracted from the Cholec80 dataset, hence, when utilizing this dataset you are requested to as well refer to Twinada et al. .
LapGyn4 is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0, ) and is created as well as maintained by Distributed Multimedia Systems Group of the Institute of Information Technology (ITEC) at Alpen-Adria Universität in Klagenfurt, Austria.
This license allows users of this dataset to copy, distribute and transmit the work under the following conditions:
 A.P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, N. Padoy, EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos, IEEE Transactions on Medical Imaging (TMI), to appear (arXiv preprint), doi:10.1109/TMI.2016.2593957, 2016.