4572973
doi
10.5281/zenodo.4572973
oai:zenodo.org:4572973
Lena Maier-Hein
German Cancer Research Center (DKFZ)
Danail Stoyanov
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK
Sebastian Bodenstedt
National Center for Tumor Diseases (NCT): Dresden
Martin Wagner
Heidelberg University Hospital
Beat Müller
Heidelberg University Hospital
Jonathan Chen
Heidelberg University Hospital
Benjamin Müller
Heidelberg University Hospital
Franziska Mathis-Ullrich
Karlsruhe Institute of Technology (KIT)
Paul Scheikl
Karlsruhe Institute of Technology (KIT)
Jorge Bernal
UAB, CVC, Barcelona, Spain
Aymeric Histache
ENSEA, ETIS, Cergy, France
Gloria Fernandes-Esparrach
Clinic Hospital Barcelona, Spain
Xavier Dray
Saint-Antoine Hospital, APHP, Paris, France
Sophia Bano
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK
Alessandro Casella
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy and Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
Francisco Vasconcelos
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK
Sara Moccia
Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy and Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Chinedu Nwoye
CAMMA Lab, University of Strasbourg & IHU Strasbourg
Deepak Alapatt
CAMMA Lab, University of Strasbourg & IHU Strasbourg
Armine Vardazaryan
CAMMA Lab, University of Strasbourg & IHU Strasbourg
Nicolas Padoy
CAMMA Lab, University of Strasbourg & IHU Strasbourg
Arnaud Huaulme
niv Rennes, INSERM, LTSI - UMR 1099, F35000, Rennes, France
Kanako Harada
Department of Mechanical Engineering, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Pierre Jannin
niv Rennes, INSERM, LTSI - UMR 1099, F35000, Rennes, France
Aneeq Zia
Intuitive Surgical
Kiran Bhattacharyya
Intuitive Surgical
Xi Liu
Intuitive Surgical
Ziheng Wang
Intuitive Surgical
Anthony Jarc
Intuitive Surgical
Endoscopic Vision Challenge 2021
Stefanie Speidel
National Center for Tumor Diseases (NCT): Dresden
info:eu-repo/semantics/openAccess
Creative Commons Attribution No Derivatives 4.0 International
https://creativecommons.org/licenses/by-nd/4.0/legalcode
Surgical Vision
Endoscopy, Classification
Segmentation
Detection
<p>Minimally invasive surgery using cameras to observe the internal anatomy is the preferred approach to many surgical procedures. Furthermore, other surgical disciplines rely on microscopic images or use flexible endoscopes for diagnostic purposes. As a result, endoscopic and microscopic image processing as well as surgical vision are evolving as techniques needed to facilitate computer assisted interventions (CAI). Algorithms that have been reported for such images include 3D surface reconstruction, salient feature motion tracking, instrument detection or activity recognition. However, what is missing so far are common datasets for consistent evaluation and benchmarking of algorithms against each other.</p>
<p>As a vision CAI challenge at MICCAI, our aim is to provide a formal framework for evaluating the current state of the art, gather researchers in the field and provide high quality data with protocols for validating endoscopic vision algorithms. EndoVis serves as an umbrella for different kinds of sub-challenges that tackle specific problems and applications in endoscopic/microsopic vision.</p>
Zenodo
2021-03-02
info:eu-repo/semantics/other
4572972
1614760289.752691
13838685
md5:1915c8ee1cb82e5b7a207e3dc69a2793
https://zenodo.org/records/4572973/files/EndoscopicVisionChallenge2021_02-10-2021_03-26-26.pdf
public
10.5281/zenodo.4572972
isVersionOf
doi