Published April 23, 2024 | Version v2
Other Open

Endoscopic Vision Challenge 2024 (EndoVis-Classification-Tracking + EndoVis-Segmentation)

Creators

  • 1. National Center for Tumor Diseases Dresden, Germany
  • 2. German Cancer Research Center, Germany
  • 3. University College London, United Kingdom
  • 4. Center for Tumor Diseases Dresden, Germany
  • 5. Purdue University, West Lafayette, IN, USA
  • 6. Else Kröner Fresenius Center for Digital Health, Dresden, Germany
  • 7. School of Biomedical Engineering & Suzhou Institute for Advanced Research,Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), University of Science and Technology of China
  • 8. OTH Regensburg
  • 9. TU Munich
  • 10. ARCADE Lab, Johns Hopkins University
  • 11. UBC
  • 12. Intuitive
  • 13. University Hospital Essen
  • 14. RWTH Aachen
  • 15. Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK
  • 16. Intuitive Surgical

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. 

EndoVis (http://endovis.org/) organizes highprofile international challenges for the comparative validation of endoscopic vision algorithms that focus on different problems each year at MICCAI, comprising various computer vision tasks (classification, segmentation, detection, localization,…) and subdisciplines ranging from laparoscopy to coloscopy and surgical training. It acts umbrella for several sub-challenges in this field, this year we propose 8 different sub-challenges within EndoVis:

  1. FedSurg: Federated Learning for Surgical Vision
  2. HiSWA-RLLS: Hierarchical surgical workflow analysis for robotic left lateral sectionectomy
  3. PhaKIR: Surgical Procedure Phase Recognition, Keypoint Estimation, and Instrument Instance Segmentation
  4. SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarially Generated Corruptions
  5. STIR: Surgical Tissue Tracking Using the STIR (Surgical Tattoos in Infrared) Dataset
  6. OSS: Open Suturing Skills Challenge
  7. SegCol: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data
  8. SurgVU: Surgical Visual Understanding

This document includes both EndoVis-Classification-Tracking + EndoVis-Segmentation events.

Files

Endoscopic Vision Challenge 2024_EndoVis-Classification-Tracking_EndoVis-Segmentation_v2.pdf

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