Running HippUnfold with Docker on Windows

Note: These instructions assume you have Docker installed already on a Windows system. Docker can also run on Linux or MacOS with similar commands, but here we will assume the default Windows CLI is being used.

First time setup

Open your Windows Command Prompt by clicking the bottom left Windows button and type cmd followed by Enter. This is where you will enter your HippUnfold commands. Feel free to make a new directory with mkdir or move to a directory you would like to work out of with cd, and for this example we will work from:

cd c:\Users\jordan\Downloads\

Pull the container (this will take some time and storage space, but like an installation it only needs to be done once and can then be run on many datasets):

docker pull khanlab/hippunfold:latest

Run HippUnfold without any arguments to print the short help:

docker run -it --rm khanlab/hippunfold:latest    

Use the -h option to get a detailed help listing:

docker run -it --rm khanlab/hippunfold:latest -h

Note that all the Snakemake command-line options are also available in HippUnfold, and can be listed with --help-snakemake:

docker run -it --rm khanlab/hippunfold:latest --help-snakemake

Running an example

Download and extract a single-subject BIDS dataset for this test from hippunfold_test_data.tar. Here we will also assume you chose to save and extract to the directory c:\Users\jordan\Downloads\.

This contains a ds002168/ directory with a single subject, that has a both T1w and T2w images.

├── dataset_description.json
└── sub-1425
    └── anat
        ├── sub-1425_T1w.json
        ├── sub-1425_T1w.nii.gz
        ├── sub-1425_T2w.json
        └── sub-1425_T2w.nii.gz

2 directories, 6 files

Now let’s run HippUnfold on the test dataset. Docker will need read/write access to the input and output directories, respectively. This is achieved with the -v flag. This ‘binds’ or ‘mounts’ a directory to a new directory inside the container.

docker run -it --rm -v c:\Users\jordan\Downloads\ds002168:/bids -v c:\Users\jordan\Downloads\ds002168_hippunfold:/output khanlab/hippunfold:latest /bids /output participant --modality T1w -n


-v c:\Users\jordan\Downloads\ds002168:/bids tells Docker to mount the directory c:\Users\jordan\Downloads\ds002168 into a new directory inside the container named /bids. We then do the same things for our output directory named ds002168_hippunfold, which we mount to /output inside the container. These arguments are not specific to HippUnfold but rather are general ways to use Docker. You may want to familiarize yourself with Docker options.

Everything after we specified the container (khanlab/hippunfold:latest) are arguments to HippUnfold itself. The first of these arguments (as with any BIDS App) are the input directory (/bids), the output directory (/output), and then the analysis level (participant). The participant analysis level is used in HippUnfold for performing the segmentation, unfolding, and any participant-level processing. The group analysis is used to combine subfield volumes across subjects into a single tsv file. The --modality flag is also required, and describes which image we use for segmentation. Here we used the T1w image. We also used the --dry-run/-n option to just print out what would run, without actually running anything.

When you run the above command, a long listing will print out, describing all the rules that will be run. Now, to actually run the workflow, we need to specify how many cores to use and leave out the dry-run option. The Snakemake --cores option tells HippUnfold how many cores to use. Using --cores 8 means that HippUnfold will only make use of 8 cores at most. Generally speaking you should use --cores all, so it can make maximal use of all the CPU cores it has access to on your system. This is especially useful if you are running multiple subjects.

Running the following command (hippunfold on a single subject) may take ~30 minutes if you have 8 cores, shorter if you have more cores, but could be much longer (several hours) if you only have a single core.

docker run -it --rm -v c:\Users\jordan\Downloads\ds002168:/bids -v c:\Users\jordan\Downloads\ds002168_hippunfold:/output khanlab/hippunfold:latest /bids /output participant --modality T1w -p --cores all

After this completes, you should have a ds002168_hippunfold directory with outputs for the one subject.

Exploring different options

If you alternatively want to run HippUnfold using a different modality, e.g. the high-resolution T2w image in the BIDS test dataset, you can use the --modality T2w option. In this case, since the T2w image in the test dataset has a limited FOV, we should also make use of the --t1-reg-template command-line option, which will make use of the T1w image for template registration, since a limited FOV T2w template does not exist.

docker run -it --rm -v c:\Users\jordan\Downloads\ds002168:/bids -v c:\Users\jordan\Downloads\ds002168_hippunfold_t2w:/output khanlab/hippunfold:latest /bids /output participant --modality T2w --t1-reg-template -p --cores all

Note that if you run with a different modality, you should use a separate output directory, since some of the files would be overwritten if not.