This is the code for generating NODDI maps (ODI, FISO, and FICVF) rapidly and simultaneously through deep learning methods.
This library will require the following the dependencies:
-
python3.5+
-
tensorflow1.8+ (
pip install --upgrade tensorflow-gpu
) -
keras2.1+ (
pip install keras
) -
webcolors (
pip install webcolors
) -
ants (see their github for install)
-
matplotlib
-
scikit-image
-
numpy
-
h5py
-
scipy
-
nibabel
-
matlab.engine
If you don't have these installed, the easiest is through the anaconda virtual environments.
conda create -n [your_env_name] python=3.6
This will install the conda environment. Now you need to install each of the pacakages either through pip
or conda install
. You will need CUDA9.0 and CUDNN7. You can get that through
conda install cudnn
The exception to this is the matlab.engine
package, which will be needed separately. This can be done by
cd "matlabroot\extern\engines\python"
python setup.py build --build-base="builddir" install --prefix="installdir"
To use the virtual environment, just run
source activate [your_env_name]
You will need to add my python_utils
library to the PYTHON_PATH
by
source path_setup.sh
To generate the figures for the paper
cd validation
python [whatever_figure_you_want]_fig.py
This should spit out the figure window pane through x11 and also save it in the results
folder (relative to the base). You will need to make the results
folder first, however.