Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients

the implementation of our dense regression activation map algorithm. The algorithm takes a CT image and its corresponding lobe segmentation as the input, generating a lesion segmentation as the output.

Installation

• Please check /docker_base/DockerFile for the required pacakges to build the docker image. Note that there is another /DockerFile is for build a docker image for grand-challenge algorithm.
• Regarding DGL library, we suggest you install 0.6.x. 0.4.x cannot be used because of bugs related to the implementation of graph attention networks.

Usage

• before training, run prepare_data.py to generate lobw-wise chunk images for training.

• For training, run train.py

• For testing, run process_pipeline.py

License

MIT