Developed by Tashrif Billah and Sylvain Bouix, Brigham and Women's Hospital (Harvard Medical School).

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pnlpipe containers

The pnlpipe docker container is publicly hosted at https://cloud.docker.com/u/tbillah/repository/docker/tbillah/pnlpipe

This repository provides recipes for building pnlpipe software containers. The containers contain the following software:

Python=3.6
ANTs=2.3.0
BRAINSTools
UKFTractography
tract_querier
dcm2niix
trainingDataT1AHCC
trainingDataT2Masks
whitematteranalysis

pnlpipe pipeline depends on two other software, installation of which requires you to agree to their license terms:

FreeSurfer 7.1.0
FSL 6.0.1

They are already installed in the tbillah/pnlpipe docker image. Befor using the image, you should review their respective licenses. A salient clause of FSL license states it is not free for commercial use. So, if you use tbillah/pnlpipe image, make sure you are aware of that limitation. The maintainer of this image is not and cannot be held liable for unlawful use of this image. On the other hand, obtain a FreeSurfer license key from here and save it as license.txt file in your host machine. To be able to run FreeSurfer, you have to mount the license key to this image as follows:

Docker

docker run --rm -ti -v /host/path/to/freesurfer/license.txt:/home/pnlbwh/freesurfer-7.1.0/license.txt \
-v /host/path/to/myData:/home/pnlbwh/myData \
tbillah/pnlpipe

When you run the container like above, it will give you a shell with all of the above software. -v /host/path/to/myData:/home/pnlbwh/data is for mounting your data into the container so you can analyze.

Singularity

(i) Build the container bootstrapping around our docker container:

export SINGULARITY_TMPDIR=$HOME
singularity build pnlpipe.sif docker://tbillah/pnlpipe:latest

(ii) Shell into the built container:

singularity shell --bind /host/path/to/freesurfer/license.txt:/home/pnlbwh/freesurfer-7.1.0/license.txt \
--bind /host/path/to/myData:/home/pnlbwh/myData \
pnlpipe.sif

(iii) Once inside the container, source pnlpipe environment:

alias ls='ls --color'
source /home/pnlbwh/.bashrc

Singularity mounts $HOME directory by default. So, if your data is in any subdirectory of $HOME, you should NOT need --bind /host/path/to/myData:/home/pnlbwh/myData.

NOTE While trying to analyze data, if you run into write permission issue, use the --writable-tmpfs flag:

singularity shell --bind /host/path/to/freesurfer/license.txt:/home/pnlbwh/freesurfer-7.1.0/license.txt \
--bind /host/path/to/myData:/home/pnlbwh/myData \
--writable-tmpfs \
pnlpipe.sif

Citation

If pipeline containers are useful in your research, please cite as below:

Billah, Tashrif*; Eckbo, Ryan*; Bouix, Sylvain; Norton, Isaiah; Processing pipeline for anatomical and diffusion weighted images, https://github.com/pnlbwh/pnlpipe, 2018, DOI: 10.5281/zenodo.2584271

Tests

Nominal test

Once inside the container, you can test its functionality with:

atlas.py --help

UKFTractography --help

DWIConvert --help

The above should print corresponding help messages without any error.

Detailed test

Additionally, you can run pnlpipe test https://github.com/pnlbwh/pnlpipe#tests to before doing your data analysis.

You are welcome to read the details of pnlpipe at https://github.com/pnlbwh/pnlpipe

Data analysis

With the above docker run and singularity shell commands, you mount your data inside the containers so you can analyze using pnlpipe. The files you generate at /home/pnlbwh/myData are saved at /host/path/to/myData.

NOTE The containers are not equipped with GUI by default. So, if you need to visually look at your MRI-- launch fsleyes, freeview etc from your host machine, not from the container. Since processed data is saved in the host directory that you mounted on the container, it should not be a problem to explore them from your host machine. Optionally, if you want to run applications that require GUI support, please see https://github.com/tashrifbillah/glxgears-containers for details.

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