@InProceedings{Leibetseder2018b, title = {Lapgyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology}, author = {Andreas Leibetseder and Stefan Petscharnig and Manfred Jürgen Primus and Sabrina Kletz and Bernd Münzer and Klaus Schöffmann and Jörg Keckstein}, booktitle = {MMSys '18 Proceedings of the 9th ACM Multimedia Systems Conference}, year = {2018}, address = {New York (NY)}, month = {Juni}, pages = {357--362}, publisher = {ACM Press}, abstract = {Modern imaging technology enables medical practitioners to perform minimally invasive surgery (MIS), i.e. a variety of medical interventions inflicting minimal trauma upon patients, hence, greatly improving their recoveries. Not only patients but also surgeons can benefit from this technology, as recorded media can be utilized for speeding-up tedious and time-consuming tasks such as treatment planning or case documentation. In order to improve the predominantly manually conducted process of analyzing said media, with this work we publish four datasets extracted from gynecologic, laparoscopic interventions with the intend on encouraging research in the field of post-surgical automatic media analysis. These datasets are designed with the following use cases in mind: medical image retrieval based on a query image, detection of instrument counts, surgical actions and anatomical structures, as well as distinguishing on which anatomical structure a certain action is performed. Furthermore, we provide suggestions for evaluation metrics and first baseline experiments.}, doi = {10.1145/3204949.3208127}, url = {https://dl.acm.org/citation.cfm?id=3208127} }