Methods
Participants to this challenge should containerize their methods with Docker and submit this for evaluation. The test data will not be released to the public. The evaluation results obtained from the test data will be released during on-site period.
In most cases, containerization of a method is a simple and straightforward procedure. We have provided a python example and, if needed, will help you creating a Docker container of your method. Furthermore, many containers are available on the internet to be used as a basis, probably including your favorite programming environment and neuroimaging tools.
In most cases, containerization of a method is a simple and straightforward procedure. We have provided a python example and, if needed, will help you creating a Docker container of your method. Furthermore, many containers are available on the internet to be used as a basis, probably including your favorite programming environment and neuroimaging tools.
Containerization
The concept of containerization is to simplify the deployment of applications, in this case your IVD segmentation method. Docker is a technique to do so and will be used in this challenge.
Docker can be used to “wrap” your entire segmentation method (including all dependencies and the operating system) into a single container. This container can be run as if it would be a single standalone application, anywhere, on any platform. Because your method and all dependencies are included in the container, the method is guaranteed to run exactly the same all the time.
This is a very popular concept and has been used successfully in previous MICCAI challenges (e.g. the MSSEG challenge, WMH challenge ). Docker Hub provides a large overview of existing Docker containers (base images), that can be used to build your own container. Furthermore, many popular programming environments and image analysis methods have Dockerfiles available.
Data access
Because your container runs in an isolated environment, the data needs to be mapped into the container. The input data will be mapped into /input, read-only. The output of the method needs to be written into /output, as a file named result.nii.gz.
Computing environment & GPU computation
The challenge organizers will run your method on all test cases. As you can imagine, we have only limited resources available to process all submissions. We provide a desktop with a 3.6 GHz Intel(R) i7 CPU and a GTX 1080 Ti graphics card with 11 GB GPU memory to evaluate your method.
Examples
To help you containerize your segmentation method with Docker, we have provided some simple examples using python and matlab. The source code is on github: zengguodong/IVDM3SegChallenge.
Assistance
If you are unsure whether your method can be containerized or how to proceed, please contact us in advance. We will try to help you with Docker or find other means to submit your method for evaluation.
Docker commands
Your container will be run with the following commands:
CONTAINERID=`docker run -dit -v [TEST-DIR]:/input:ro -v /output ivdm3seg/[TEAM-NAME]`
docker exec $CONTAINERID [YOUR-COMMAND]
docker cp $CONTAINERID:/output [RESULT-TEAM]
docker stop $CONTAINERID
docker rm -v $CONTAINERID
docker exec $CONTAINERID [YOUR-COMMAND]
docker cp $CONTAINERID:/output [RESULT-TEAM]
docker stop $CONTAINERID
docker rm -v $CONTAINERID
