Endoscopic Vision Challenge 2022
Creators: name: Aneeq Zia
name: Liu, Xi
name: Bhattacharyya, Kiran
name: Ziheng Wang
name: Berniker, Max
name: Jarc, Anthony
name: Chinedu Nwoye
name: Alapatt, Deepak
name: Murali, Aditya
name: Saurav Sharma
name: Vardazaryan, Armine
name: Padoy, Nicolas
name: Van Amsterdam, Beatrice
name: Psychogyios, Dimitris
name: Colleoni, Emanuele
name: Rau, Anita
name: Bano, Sophia
name: Yueming Jin
name: Joao Cartucho
name: Giannarou, Stamatia
name: Sharib Ali
name: Yueming Jin
name: Yamid Espinel López
name: Buc, Emmanuel
name: Roy, Bertrand Le
name: Teoule, Patrick
name: Reissfelder, Christoph
name: Bailey, Adam
name: Soonawalla, Zahir
name: Gordon-Weeks, Alex
name: Silva, Michael
name: Bartoli, Adrien
name: Roß, Tobias
name: Reinke, Annika
name: Bodenstedt, Sebastian
name: Stoyanov, Danail
name: Maier-Hein, Lena
name: Speidel, Stefanie
Publisher: name: Zenodo
Year: 2022
Subjects: Surgical Vision
Endoscopy
Classification
Detection
Segmentation
MICCAI
Challenge
weak learning
object detection
object localization
Surgical activity detection
surgical action detection
action triplet
tool-tissue interaction
CholecT50
deep learning
laparoscopic video
medical image analysis
Surgical action recognition
Surgical instrument segmentation
Prostatectomy
Multitask learning
Colonoscopy,
pose estimation
depth estimation
Tracking
Laparoscopy
Robotic surgery
Computer Assisted Interventions

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.<br> Contributing to the key aspect of perception, endoscopic vision thus constitutes a central component of SDS and computer-assisted interventions. <br> 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: SurgToolLoc - Endoscopic surgical tool localization by leveraging tool presence labels CholecTriplet2022 - Surgical Action Triplet Detection and Localization SAR-RARP50 - Instrumentation segmentation and Action Recognition on robotic Radical Prostatectomy SimCol-to-3D: Simulated Colonoscopy data for 3D (scene) reconstruction SurgT - A challenge for tissue tracking in surgery P2ILF - Preoperative to Intraoperative Laparoscopy Fusion