debio/pl-neuproseg repository overview

################################ pl-neuproseg ################################

Abstract

This application applies a trained UNET model to MRI data of prostates and outputs a segmented volume. Note, this application has already been trained a priori and the purpose of this plugin is to deploy this trained model and perform a segmentation on existing preprocessed data.

The preprocessing of data is out of scope of this application, and the assumption is that input data has been properly preprocessed.

The original python code was developed by Anneke Meyer and adapted to a CHRIS plugin during NAMIC project week at MIT, Jan 8-12th, 2018.

⁠Data

Input data is required to run this plugin and has been supplied as part of this repository in the data directory. This directory contains test sets as input to the trained model.

Deploy

This repository is associated with dockerhub and automated builds are enabled. Thus, to install this container, simply do

.. code-block:: bash

docker pull fnndsc/pl-neuproseg

Alternatively, you can also build a local version of this container:

.. code-block:: bash

docker build -t local/pl-neuproseg .

Run

⁠Using a container

Assuming you have pulled the fnndsc/pl-neuproseg container, and assuming the use of the data directory in this repository,

.. code-block:: bash

mkdir output
chmod 777 output # So that the container can write results here!
docker run -v $(pwd)/data/ProstateX-0029:/incoming -v $(pwd)/output:/outgoing   \
        fnndsc/pl-neuproseg neuproseg.py --multistream           \
        /incoming /outgoing

This will run the containerized segmenter on the passed input directory (on the host), writing output to the passed output directory.

The chmod 777 output is necessary to allow the container to store data in output -- in some cases, particularly in NFS mapped spaces (if output is on an NFS space) and if the NFS space is mounted as rootsquash, then root on the local machine might not be able to write to a directory, pending its permissions.

Also, note that the $(pwd) is necessary in the above since docker requires absolute directory specs in mapping.

On successful completion, the output directory will contain the segmentated image data.

⁠Directly on the metal

To run directly, several dependencies have to be satisfied -- thus we recommend using the container. If, however, you wish to run on the metal, and assuming these dependencies have been met, do

.. code-block:: bash

mkdir output
cd neuproseg
python3 neuproseg.py --multistream ../data/ProstateX-0029 ../output

See below for the setup of the app directly.

⁠Setup a python virtual environment

To run the app directly, we recommend setting up a python virtual environment. On Ubuntu, you can do

.. code-block:: bash

sudo apt install virtualenv virtualenvwrapper python3-tk

NOTE: the python3-tk is critical and must be installed before creating the virtual environment.

then, create a directory to contain all your python virual environments, e.g.

.. code-block:: bash

cd ~
mkdir python-venv
cd python-venv
virtualenv --python=python3.6 --system-site-packages pl_env

finally, run the following to use the virtual environment

.. code-block:: bash

export WORKON_HOME=~/python-venv source /usr/share/virtualenvwrapper/virtualenvwrapper.sh

we suggest adding the above to a file and then simply sourcing that file. Create the file once

.. code-block:: bash

echo "export WORKON_HOME=~/python-venv" > ~/penv echo "source /usr/share/virtualenvwrapper/virtualenvwrapper.sh" >> ~/penv

and then simply for any subsequent use

.. code-block:: bash

source ~/penv ; workon pl_env

⁠Install dependencies

In this virtual environment, install all the necessary dependencies

.. code-block:: bash

pip3 install -r requirements.txt