Endoscopic Vision Challenge 2024 (EndoVis-Classification-Tracking + EndoVis-Segmentation)
Creators: name: Speidel, Stefanie
name: Maier-Hein, Lena
name: Stoyanov, Danail
name: Kirchner, Max
name: Jenke, Alexander
name: Bodenstedt, Sebastian
name: Kolbinger, Fiona
name: Saldanha, Oliver Lester
name: Kather, Jakob Nikolas
name: Zhou, S.Kevin
name: Zhao, Shang
name: Wang, Qiyuan
name: Sun, Dai
name: Rueckert, Tobias
name: Palm, Christoph
name: Wilhelm, Dirk
name: Feußner, Hubertus
name: Rueckert, Daniel
name: Ding, Hao
name: Unberath, Mathias
name: Schmidt, Adam
name: Salcudean, Tim
name: Mohareri, Omid
name: DiMaio, Simon
name: Hoffmann, Hanna
name: Egger, Jan
name: Bady, Setareh
name: Hölzle, Frank
name: Röhrig, Rainer
name: Puladi, Behrus
name: Daher, Rema
name: Ju, Xinwei
name: Caramalau, Razvan
name: Huang, Baoru
name: Vasconcelos, Francisco
name: Zia, Aneeq
name: Berniker, Max
name: Perreault, Conor
name: Nespolo, Rogerio
name: Wang, Ziheng
name: Jarc, Anthony
name: Reinke, Annika
name: Bano, Sophia
Publisher: name: Zenodo
Year: 2024
Subjects: Surgical Vision
Endoscopy
Classification
Detection
Segmentation
MICCAI 2024 challenges
Federated Learning
Laparoscopic Video Analysis
Laparoscopic Surgery
Appendicitis
Appendectomy
Multi-relation
Multi-task
Surgical workflow analysis
Endoscopic Vision
Surgical Instruments
Instance Segmentation
Keypoint Estimation
Phase Recognition
Non-adversarial Robustness
Surgical Tool Segmentation
Robotics Surgery
Minimally Invasive Surgery
EndoVis
Surgical tracking
Deformable
Reconstruction
Skill assessment
Open surgery
Suturing
Artificial intelligence
Colonoscopy
Semantic segmentation
Active learning
Weak learning
Object detection
Object localization
Activity recognition

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:



FedSurg: Federated Learning for Surgical Vision

HiSWA-RLLS: Hierarchical surgical workflow analysis for robotic left lateral sectionectomy

PhaKIR: Surgical Procedure Phase Recognition, Keypoint Estimation, and Instrument Instance Segmentation

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarially Generated Corruptions

STIR: Surgical Tissue Tracking Using the STIR (Surgical Tattoos in Infrared) Dataset

OSS: Open Suturing Skills Challenge

SegCol: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data

CASVU: Context-aware surgical visual understanding


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