GLENDA: Gynecologic Laparoscopy Endometriosis Dataset

Andreas Leibetseder, Sabrina Kletz, Klaus Schoeffmann, Simon Keckstein, J. Keckstein
In: Lecture Notes in Computer Science · 2019 · pp. 439–450 · doi:10.1007/978-3-030-37734-2_36 · W2998595069
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This work introduces GLENDA, the first region-based annotated dataset of laparoscopic gynecologic videos featuring endometriosis, designed to aid computer vision and machine learning research.

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Abstract

Gynecologic 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. Access this chapter Tax calculation will be finalised at checkout Purchases are for personal use only Similar content being viewed by others Notes - 1. e.g. X-ray, computed tomography (CT) scans, magnetic resonance imaging (MRI), ultrasound, ... - 2. - 3. e.g. peritoneum, ovary, tube, ligaments, vagina, rectum, bladder, ureter, ... - 4. Note 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.

References

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Acknowledgements

This work was funded by the FWF Austrian Science Fund under grant P 32010-N38. Author information Authors and Affiliations Corresponding author Editor information Editors and Affiliations Rights and permissions Copyright information © 2020 Springer Nature Switzerland AG About this paper Cite this paper Leibetseder, 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 Download citation DOI: https://doi.org/10.1007/978-3-030-37734-2_36 Published: Publisher Name: Springer, Cham Print ISBN: 978-3-030-37733-5 Online ISBN: 978-3-030-37734-2 eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science

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