Minimally invasive surgery using cameras to observe the internal anatomy is the preferred approach to many surgical procedures. As a result, endoscopic image processing and surgical vision are evolving as techniques needed to facilitate computer assisted interventions (CAI). Algorithms that have been reported for endoscopic images include 3D surface reconstruction, salient feature motion tracking or instrument detection. However, what is missing so far are common datasets for consistent evaluation and benchmarking of algorithms against each other. As an endoscopic vision CAI challenge at MICCAI, our aim is to provide a formal framework for evaluating the current state of the art, gather researchers in the field and provide high quality data with protocols for validating endoscopic vision algorithms.
We invite the community to be part of organizing this challenge by contributing data for a specific sub-challenge in the field of endoscopic image processing and surgical vision (e.g. 3D surface reconstruction, tissue classification, feature or instrument tracking).
If you are interested in contributing your data and proposing a sub-challenge, please fill out the "Call for Data".
Based on a "Call for Data" four sub-challenges were selected:
- Instrument segmentation and tracking
- Automatic polyp detection in colonoscopy videos
- Early Barrett's cancer detection
- Detection of abnormalities in gastroscopic images
If you have any question regarding to this challenge, please send an email to the following address:
This challenge is endorsed by the International Society for Computer Aided Surgery (ISCAS) and organized by the open source and open data group of ISCAS. It is supported by the Transregional Collaborative Research Centre (TCRC) Cognition-Guided Surgery.