2018 Robotic Scene Segmentation Challenge

  • 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.

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Author:Max Allan, Satoshi Kondo, Sebastian Bodenstedt, Stefan Leger, Rahim Kadkhodamohammadi, Imanol Luengo, Felix Fuentes, Evangello Flouty, Ahmed Mohammed, Marius Pedersen, Avinash Kori, Varghese Alex, Ganapathy Krishnamurthi, David Rauber, Robert Mendel, Christoph PalmORCiDGND, Sophia BanoORCiD, Guinther Saibro, Chi-Sheng Shih, Hsun-An Chiang, Juntang Zhuang, Junlin Yang, Vladimir Iglovikov, Anton Dobrenkii, Madhu Reddiboina, Anubhav Reddy, Xingtong Liu, Cong Gao, Mathias UnberathORCiD, Myeonghyeon Kim, Chanho Kim, Chaewon Kim, Hyejin Kim, Gyeongmin Lee, Ihsan Ullah, Miguel Luna, Sang Hyun Park, Mahdi Azizian, Danail StoyanovORCiD, Lena Maier-HeinORCiD, Stefanie SpeidelORCiD
URN:urn:nbn:de:bvb:898-opus4-50049
URL / DOI:https://arxiv.org/abs/2001.11190
DOI:https://doi.org/10.48550/arXiv.2001.11190
Document Type:Preprint
Language:English
Year of first Publication:2020
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2022/07/29
Tag:Minimally invasive surgery; Robotic
GND Keyword:Minimal-invasive Chirurgie; Robotik
Pagenumber:11
Institutes:Fakult├Ąt Informatik und Mathematik
Fakult├Ąt Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
6 Technik, Medizin, angewandte Wissenschaften
research focus:Lebenswissenschaften und Ethik
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International