This cumulative dataset includes two versions of GLENDA (Gynecologic Laparoscopy ENdometriosis DAtaset). Following list gives a brief description of all contained classes (both versions). Peritoneum: As one of the most frequently diagnosed types, peritoneal endometriosis is found on the peritoneum, i.e. the lining of the abdominal cavity, which occurs in a mixture of red, yellow and white colors. Ovary: Endometriosis is as well very frequently found on ovaries, the outer capsule of which appears in a shade of white. Uterus: Uterine endometriosis or adenomyosis thickens the organ, which typically is colored in a red-tint. Deep infiltrating endometriosis (DIE): Non-shallow endometriosis that is found on specific locations such as the rectum, the rectovaginal space or uterine ligaments is described as Deep Infil- trating Endometriosis (DIE). Due to the variety of involved locations no distinct visual appearance can be attributed to this type of class, other than highlighting that typically the color spectrum in recorded laparascopic videos lacks green tones. No pathology: Video sequences containing no visible pathology in relation to endometriosis are included in the dataset, providing counter examples to above categories. Since this class does not contain any region-based annotations, in addition to a sequence showing a non-pathological uterus, below listing particularly includes examples of several anatomical structures from above pathological classes (e.g. peritoneum and ovaries). Again it is not possible to make any assumptions about the color and shape of objects within this class, since it includes images covering most areas of the pelvic region. v1.0 GLENDA (Gynecologic Laparoscopy ENdometriosis DAtaset) comprises over 25 000 images taken from 400+ gynecologic laparoscopy surgeries and is purposefully created to be utilized for a variety of automatic content analysis problems in the context of Endometriosis recognition. GLENDA_v1.0.zip Dataset including annotated classes together with adjacent video frames for dataset augmentation, e.g. tracking. Annotations are given as binary masks, one file per annotations, e.g. potentially multiple files per frame. Additionally, all non pathological frames are included as well. GLENDA_v1.0_no_segments_multicolor.zip Only annotated files in MS COCO format, i.e. multi-colored annotation masks one per frame. 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. Glenda_v1.5_classes.zip Revised annotated files in MS COCO format, i.e. multi-colored annotation masks one per frame. In addition, files have more meaningful names and statistics as well as simple visualization is included. GLENDA_v1.5_no_pathology.zip Separated archive containing only frames of non pathological content. You are kindly requested to cite the original work that led to the creation of the dataset: https://doi.org/10.1007/978-3-030-37734-2_36. 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. For the latest updates, please visit the dataset's homepage: http://ftp.itec.aau.at/datasets/GLENDA.