The ITEC Smoke_Cholec80 Image Dataset

The dataset contains mapping files for specific video segments belonging to the Cholec80 cholecystectomy dataset, published by Twinada et al. [1]. For further information about the dataset's organization see 'Readme.txt' within the download archive below.

In addition, reference must be made to the following publication when this dataset is used in any academic and research reports:

A. Leibetseder, M. J. Primus, S. Petscharnig, K. Schoeffmann, Real-Time Image-based Smoke Detection in Endoscopic Videos, In Proceedings of ThematicWorkshops’17 , ACM Multimedia 2017, doi: 10.1145/3126686.3126690

    author    = {Andreas Leibetseder and
                 Manfred J{\"{u}}rgen Primus and
                 Stefan Petscharnig and
                 Klaus Schoeffmann},
    editor    = {Wanmin Wu and
                 Jianchao Yang and
                 Qi Tian and
                 Roger Zimmermann},
    title     = {Real-Time Image-based Smoke Detection in Endoscopic Videos},
    booktitle = {Proceedings of the on Thematic Workshops of {ACM} Multimedia 2017,
                 Mountain View, CA, USA, October 23 - 27, 2017},
    pages     = {296--304},
    publisher = {{ACM}},
    year      = {2017},
    url       = {},
    doi       = {10.1145/3126686.3126690},
    timestamp = {Tue, 06 Nov 2018 16:58:36 +0100},
    biburl    = {},
    bibsource = {dblp computer science bibliography,}

Smoke_Cholec80 is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0, Creative Commons License) 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:

For further legal details, please read the complete license terms.

If you agree to above conditions, you are free to download Smoke_Cholec80.

Additionally, Python implementations for the saturation histogram based classification methodologies Saturation Analysis (SAN) and Saturation Peak Analysis (SPA) -- as described in above paper -- are provided via following github repository:

[1] 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.