{"paper_id":"be266636-08e0-4ecd-a63d-d9b8e5a775a1","body_text":"Abstract\nGynecologic laparoscopy as a type of minimally invasive surgery (MIS) is performed via a live feed of a patient’s abdomen surveying the insertion and handling of various instruments for conducting treatment. Adopting this kind of surgical intervention not only facilitates a great variety of treatments, the possibility of recording said video streams is as well essential for numerous post-surgical activities, such as treatment planning, case documentation and education. Nonetheless, the process of manually analyzing surgical recordings, as it is carried out in current practice, usually proves tediously time-consuming. In order to improve upon this situation, more sophisticated computer vision as well as machine learning approaches are actively developed. Since most of such approaches heavily rely on sample data, which especially in the medical field is only sparsely available, with this work we publish the Gynecologic Laparoscopy ENdometriosis DAtaset (GLENDA) – an image dataset containing region-based annotations of a common medical condition named endometriosis, i.e. the dislocation of uterine-like tissue. The dataset is the first of its kind and it has been created in collaboration with leading medical experts in the field.\nAccess this chapter\nTax calculation will be finalised at checkout\nPurchases are for personal use only\nSimilar content being viewed by others\nNotes\n- 1.\ne.g. X-ray, computed tomography (CT) scans, magnetic resonance imaging (MRI), ultrasound, ...\n- 2.\n- 3.\ne.g. peritoneum, ovary, tube, ligaments, vagina, rectum, bladder, ureter, ...\n- 4.\nNote that due to the possibility of annotating several categories per image, e.g. GLENDA includes region-based annotations of up to three classes per image, for simplicity only frames with exactly one associated class have been chosen as examples.\nReferences\nAndrews, W., et al.: Revised american fertility society classification of endometriosis: 1985. Fertil. Steril. 43(3), 351–352 (1985)\nCanis, M., et al.: Revised american society for reproductive medicine classification of endometriosis: 1996. Fertil. Steril. 67(5), 817–821 (1997). https://doi.org/10.1016/S0015-0282(97)81391-X\nKeckstein, J.: Endometriosis in the intestinal tract – important facts for diagnosis and therapy. Coloproctology 39(2), 121–133 (2017). https://doi.org/10.1007/s00053-017-0144-5\nLeibetseder, A., Petscharnig, S., Primus, M.J., Kletz, S., Münzer, B., Schoeffmann, K., Keckstein, J.: LapGyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology. In: Proceedings of the 9th ACM Multimedia Systems Conference, pp. 357–362. ACM (2018)\nLitjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)\nMünzer, B., Leibetseder, A., Kletz, S., Schoeffmann, K.: ECAT - endoscopic concept annotation tool. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, W.-H., Vrochidis, S. (eds.) MMM 2019. LNCS, vol. 11296, pp. 571–576. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05716-9_48\nMünzer, B., Schoeffmann, K., Böszörmenyi, L.: Content-based processing and analysis of endoscopic images and videos: a survey. Multimed. Tools Appl. (2017). https://doi.org/10.1007/s11042-016-4219-z\nSchoeffmann, K., Husslein, H., Kletz, S., Petscharnig, S., Muenzer, B., Beecks, C.: Video retrieval in laparoscopic video recordings with dynamic content descriptors. Multimed. Tools Appl. 77(13), 16813–16832 (2018). https://doi.org/10.1007/s11042-017-5252-2\nStauder, R., Ostler, D., Kranzfelder, M., Koller, S., Feußner, H., Navab, N.: The TUM LapChole dataset for the M2CAI 2016 workflow challenge. arXiv preprint arXiv:1610.09278 (2016)\nTsui, C., Klein, R., Garabrant, M.: Minimally invasive surgery: national trends in adoption and future directions for hospital strategy. Surg. Endosc. 27(7), 2253–2257 (2013)\nTwinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., Padoy, N.: EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imag. 36(1), 86–97 (2017). https://doi.org/10.1109/TMI.2016.2593957\nYe, M., Giannarou, S., Meining, A., Yang, G.Z.: Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations. Med. Image Anal. 30, 144–157 (2016)\nAcknowledgements\nThis work was funded by the FWF Austrian Science Fund under grant P 32010-N38.\nAuthor information\nAuthors and Affiliations\nCorresponding author\nEditor information\nEditors and Affiliations\nRights and permissions\nCopyright information\n© 2020 Springer Nature Switzerland AG\nAbout this paper\nCite this paper\nLeibetseder, A., Kletz, S., Schoeffmann, K., Keckstein, S., Keckstein, J. (2020). GLENDA: Gynecologic Laparoscopy Endometriosis Dataset. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_36\nDownload citation\nDOI: https://doi.org/10.1007/978-3-030-37734-2_36\nPublished:\nPublisher Name: Springer, Cham\nPrint ISBN: 978-3-030-37733-5\nOnline ISBN: 978-3-030-37734-2\neBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science","source_license":"CC0","license_restricted":false}