This repository uses a modified grt123 solution to implement a DIAG-processor docker image. The processor can be applied to a list of 3D lung images and the algorithm will:
Compute cancer scores
Cancer scores indicate the likelihood of the image containing a cancer.
Designate regions of interest
Regions of interest are locations in the input image that the algorithm analyzed to compute its "cancer score".
Original grt123 solution can be found here.
The processor uses docker to build a containerized runtime environment for the grt123 algorithm to run in.
If you are in a linux environment with docker installed, issue the command:
./build_processor_docker.sh processor [--version-tag VERSION_TAG] [--git-commit GIT_COMMIT_ID] [--push] [-h|--help]
to build the DIAG docker image.
--version-tag VERSION_TAG
labels the docker image with a version tag, if not specified the tag is left empty creating latest
.--git-commit GIT_COMMIT_ID
overrides the internally baked gid commit id used by the processor, if not specified will attempt to look for it in the .git
folder.--push
will attempt to push the images to the private DIAG-docker-registry after building. -h, --help
will display some a short help message.The algorithm requires the following hardware configuration to run:
The measured runtime for the algorithm on a system matching the specifications is:
The input directory is a directory lung image volumes. Images are: