Airway-related quantitative imaging biomarkers (QIB) are crucial for examination, diagnosis, and prognosis in lung diseases, while the manual delineation of airway structures is unduly burdensome. Competitors are encouraged to devise automatic airway segmentation models with high robustness and generalization abilities. This challenge is an open-call challenge and new submissions are allowed after the conference.
Register the challenge from https://codalab.lisn.upsaclay.fr/competitions/13076#participate
It is of note that you need to send the registeration form to the organizers.
More details can be found here
- Prepare you python inference code
- Prepare all the container files as the following structure:
── Dockerdir
├── Dockerfile (this file includes your basic settings)
└── requirements.txt (this file includes the list of your python packages)
├── ...
└── predict.py
- Build the docker
docker build -f Dockerfile -t YOUR_TEAM_NAME . - Save the docker
docker save YOUR_TEAM_NAME:latest -o Teamname_TaskNO.tar.gz - Send the packed docker and instructions to the organizers, please test the container file first before sending to us.