@unpublished{AllanKondoBodenstedtetal., author = {Allan, Max and Kondo, Satoshi and Bodenstedt, Sebastian and Leger, Stefan and Kadkhodamohammadi, Rahim and Luengo, Imanol and Fuentes, Felix and Flouty, Evangello and Mohammed, Ahmed and Pedersen, Marius and Kori, Avinash and Alex, Varghese and Krishnamurthi, Ganapathy and Rauber, David and Mendel, Robert and Palm, Christoph and Bano, Sophia and Saibro, Guinther and Shih, Chi-Sheng and Chiang, Hsun-An and Zhuang, Juntang and Yang, Junlin and Iglovikov, Vladimir and Dobrenkii, Anton and Reddiboina, Madhu and Reddy, Anubhav and Liu, Xingtong and Gao, Cong and Unberath, Mathias and Kim, Myeonghyeon and Kim, Chanho and Kim, Chaewon and Kim, Hyejin and Lee, Gyeongmin and Ullah, Ihsan and Luna, Miguel and Park, Sang Hyun and Azizian, Mahdi and Stoyanov, Danail and Maier-Hein, Lena and Speidel, Stefanie}, title = {2018 Robotic Scene Segmentation Challenge}, doi = {10.48550/arXiv.2001.11190}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-50049}, pages = {11}, abstract = {In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of exvivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modfications on U-Nets and other popular CNN architectures [1]. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs.}, subject = {Minimal-invasive Chirurgie}, language = {en} }