aim-kannlab / plgg_segmentation_docker Goto Github PK

Main repo: https://github.com/AIM-KannLab/pLGG_Segmentation

Prerequisites:

• Install docker: https://docs.docker.com/engine/install
• Clone repo: git clone
• GPU: Nvidia GPU with CUDA support (tested on Nvidia A6000)
• The pretrained models can be downloaded at the following Drive link https://drive.google.com/file/d/1cbi3p9IoKWjKR-pl3yXde6ISx4hZy2DB/view?usp=sharing. Unzip this file, the unzipped folder should be named nnUnet_trained_models and placed in 'nnUnet/' folder.

0. Put all images in the 'example_input' folder
1. Build the docker image: sudo docker build -t segmentation . Note: this will take a while to build the docker image
2. Run the docker image: sudo docker run -i --gpus=all --ipc=host -v ${PWD}/output:/output/preprocessed -t segmentation Note: this will take a while to run the docker image, depending on how many images you have in the 'example_input' folder/ how many GPUs you have. Your output will be in the 'preprocessed' folder.

!To pass parameters to docker(modify input/output paths), when running the docker image, use the following command: sudo docker run -i --gpus=all --ipc=host -v ${PWD}/output:./output/ -t segmentation --CUDA_VISIBLE_DEVICES=0 --T2W_dir /example_input --output_path /output/

Optional: to clean dockers sudo docker image prune -a or docker image prune -a --filter "until=24h" or docker system prune

Example dataset (note, this is not the example MRI with brain tumor; this is just a sample dataset to test the pipeline): https://openneuro.org/datasets/ds000228/versions/1.1.0 To run on your own data, place T2w .nii.gz files in the 'example_input' folder.