Endoscopic Vision Challenge 2023
Creators
- Stefanie Speidel1
- Lena Maier-Hein2
- Danail Stoyanov3
- Sebastian Bodenstedt1
- Annika Reinke4
- Sophia Bano3
- Alexander Jenke1
- Martin Wagner5
- Marie Daum5
- Ala Tabibian5
- Adrito Das6
- Yitong Zhang6
- Francisco Vasconcelos6
- Dimitris Psychogyios6
- Danyal Z. Khan7
- Hani J. Marcus7
- Aneeq Zia8
- Xi Liu8
- Kiran Bhattacharyya8
- Ziheng Wang8
- Max Berniker8
- Conor Perreault8
- Anthony Jarc8
- Anand Malpani9
- Kimberly Glock9
- Haozheng Xu10
- Chi Xu10
- Baoru Huang10
- Stamatia Giannarou10
- 1. National Center for Tumor Diseases Dresden, Germany
- 2. German Cancer Research Center, Germany
- 3. University College London, United Kingdom
- 4. German Cancer ResearcGerman Cancer Research Center, German
- 5. Heidelberg University Hospital
- 6. Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
- 7. Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK and UCL Queen Square Institute of Neurology, University College London, United Kingdom
- 8. Intuitive Surgical
- 9. Surgical Science
- 10. The Hamlyn Centre for Robotic Surgery, Imperial College London,London SW7 2AZ, UK
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:
- SIMS: Surgical Instrument Multi-Domain Segmentation Challenge
- PitVis: Surgical workflow and instrument recognition in endonasal surgery
- SurgToolLoc: Surgical tool localization and keypoint detection by leveraging tool presence labels
- Syn-ISWAS: Synthetic data for Instrument Segmentation and Workflow Analysis in Surgery
- SurgRIPE: Surgical Robot Instrument Pose Estimation
Files
EndoscopicVisionChallenge2023_09-04-2023_01-33-14_v2.pdf
Files
(203.6 kB)
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