Published March 16, 2022 | Version v2
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Endoscopic Vision Challenge 2022

Creators

  • 1. Intuitive Surgical
  • 2. CAMMA Lab, University of Strasbourg & IHU Strasbourg
  • 3. University College London
  • 4. Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK
  • 5. The Hamlyn Centre for Robotic Surgery, Imperial College London, London
  • 6. Department of Engineering Science, University of Oxford, UK
  • 7. University College London, UK
  • 8. Université Clermont Auvergne, France
  • 9. Clermont University Hospital, France
  • 10. Saint-Etienne University Hospital, France
  • 11. Universitätsmedizin Mannheim, Germany
  • 12. Translational Gastroenterology Unit, Oxford University Hospitals NHS Trust, Oxford, UK
  • 13. Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
  • 14. Nuffield Department of Surgical Sciences,University of Oxford, Oxford, UK
  • 15. Department of Clinical Research and Innovation, Clermont University Hospital, France
  • 16. German Cancer Research Center, Germany
  • 17. National Center for Tumor Diseases Dresden, Germany

Description

With the advent of artificial intelligence as key technology in modern medicine, surgical data science (SDS) promises to improve the quality and value of the particular domain of interventional healthcare through capturing, organization, analysis, and modeling of data, thus creating benefit for both patients and medical staff. Holistic SDS concepts span the topics of context-aware perception in and beyond the operating room, data interpretation and real-time assistance or decision support. At the same time, minimally invasive surgery using cameras to observe the internal anatomy has become the state-of-the-art approach to many surgical procedures.
Contributing to the key aspect of perception, endoscopic vision thus constitutes a central component of SDS and computer-assisted interventions.


From this arises the necessity for high-quality common datasets that allow the scientific community to perform comparative benchmarking and validation of endoscopic vision algorithms. With EndoVis, we present you a large collection of publicly accessible datasets comprising various computer vision tasks (classification, segmentation, detection, localization,…) and subdisciplines ranging from laparoscopy to coloscopy and surgical training. These datasets can be used for both de novo development as well as validation of methods. EndoVis organizes highprofile international challenges for the comparative validation of endoscopic vision algorithms that focus on different problems each year at MICCAI, thus representing a major driving force of advancements in the field. This year we propose 6 different sub-challenges under the umbrella of EndoVis:

  1. SurgToolLoc - Endoscopic surgical tool localization by leveraging tool presence labels
  2. CholecTriplet2022 - Surgical Action Triplet Detection and Localization
  3. SAR-RARP50 - Instrumentation segmentation and Action Recognition on robotic Radical Prostatectomy
  4. SimCol-to-3D: Simulated Colonoscopy data for 3D (scene) reconstruction
  5. SurgT - A challenge for tissue tracking in surgery
  6. P2ILF - Preoperative to Intraoperative Laparoscopy Fusion

Files

EndoVis2022.pdf

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