@InProceedings{Leibetseder2021, author = {Andreas Leibetseder and Klaus Schoeffmann and Joerg Keckstein and Simon Keckstein}, booktitle = {2021 International Conference on Content-Based Multimedia Indexing (CBMI)}, title = {{Post-surgical Endometriosis Segmentation in Laparoscopic Videos}}, year = {2021}, month = {jun}, pages = {1--4}, publisher = {IEEE}, abstract = {Endometriosis is a common women's condition exhibiting a manifold visual appearance in various body-internal locations. Having such properties makes its identification very difficult and error-prone, at least for laymen and non-specialized medical practitioners. In an attempt to provide assistance to gynecologic physicians treating endometriosis, this demo paper describes a system that is trained to segment one frequently occurring visual appearance of endometriosis, namely dark endometrial implants. The system is capable of analyzing laparoscopic surgery videos, annotating identified implant regions with multi-colored overlays and displaying a detection summary for improved video browsing.}, doi = {10.1109/cbmi50038.2021.9461900}, keywords = {Endometriosis, Lesion Segmentation, Mask R-CNN}, url = {http://dx.doi.org/10.1109/cbmi50038.2021.9461900} }