GLENDA: The ITEC Gynecologic Laparoscopy Endometriosis Dataset (v1.5)

GLENDA (Gynecologic Laparoscopy ENdometriosis DAtaset) comprises over 350 annotated endometriosis lesion images taken from 100+ gynecologic laparoscopy surgeries as well as over 13K unannotated non pathological images of 20+ surgeries. The dataset is purposefully created to be utilized for a variety of automatic content analysis problems in the context of Endometriosis recognition.



Endometriosis is a benign but potentially painful condition among women in child bearing age involving the growth of uterine-like tissue in locations outside of the uterus. Corresponding lesions can be found in various positions and severities, often in multiple instances per patient requiring a physician to determine its extent. This most frequently is accomplished by calculating its magnitude via utilizing the combination of two popular classification systems, the revised American Society for Reproductive Medicine (rASRM) and the European Enzian scores. Endometriosis can not reliably identified by laymen, therefore, the dataset has been created with the help of medical experts in the field of endometriosis treatment.



The dataset includes region-based annotations of 4 pathological endometriosis categories as well as non pathological counter example images. Annotations are created for single video frames that may be part of larger sequences comprising several consecutive frames (all showing the annotated condition). Frames can contain multiple annotations, potentially of different categories. Each single annotation is exported as a binary image (similar to below examples, albeit one image per annotation).

Version History

All previous dataset revisions are listed here and their release pages are linked.

v1.5 (this version)



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. Kletz, K. Schoeffmann, S. Keckstein and J. Keckstein. 2020. GLENDA: Gynecologic Laparoscopy Endometriosis Dataset. In Proceedings of the 26th International Conference on Multimedia Modeling, MMM 2020. Springer, Cham.


    author    = {Andreas Leibetseder and
                 Sabrina Kletz and
                 Klaus Schoeffmann and
                 Simon Keckstein and
                 J{\"{o}}rg Keckstein},
    title     = {{GLENDA:} Gynecologic Laparoscopy Endometriosis Dataset},
    booktitle = {MultiMedia Modeling - 26th International Conference, {MMM} 2020, Daejeon,
                 South Korea, January 5-8, 2020, Proceedings, Part {II}},
    series    = {Lecture Notes in Computer Science},
    volume    = {11962},
    pages     = {439--450},
    publisher = {Springer},
    year      = {2020},
    url       = {\_36},
    doi       = {10.1007/978-3-030-37734-2\_36},
    timestamp = {Mon, 09 Nov 2020 15:46:42 +0100},
    biburl    = {},
    bibsource = {dblp computer science bibliography,}

ENID is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 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.


Following list gives a brief description of all contained classes.

Following table shows dataset statistics in terms of patient cases, images and annotations. Since an image can have multiple different class annotations, please note that it is counted for the specific class covering the largest area. Finally, the rightmost columns contain links for previewing and downloading the datasets

IMPORTANT: By downloading, you agree to and adhere to above conditions.

annotated class cases frames annotations preview size download
peritoneum 84 257 489 27MB
ovary 27 53 54
uterus 11 17 25
die 29 55 59
total 102 373 628

unannotated class cases frames preview size download
no pathology 27 13 438 see v1.0 page 827MB