Published March 2, 2021 | Version v1
Other Open

Endoscopic Vision Challenge 2021

  • 1. National Center for Tumor Diseases (NCT): Dresden
  • 2. German Cancer Research Center (DKFZ)
  • 3. Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK
  • 4. Heidelberg University Hospital
  • 5. Karlsruhe Institute of Technology (KIT)
  • 6. UAB, CVC, Barcelona, Spain
  • 7. ENSEA, ETIS, Cergy, France
  • 8. Clinic Hospital Barcelona, Spain
  • 9. Saint-Antoine Hospital, APHP, Paris, France
  • 10. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy and Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
  • 11. Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy and Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
  • 12. CAMMA Lab, University of Strasbourg & IHU Strasbourg
  • 13. niv Rennes, INSERM, LTSI - UMR 1099, F35000, Rennes, France
  • 14. Department of Mechanical Engineering, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
  • 15. Intuitive Surgical


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.

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.



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