This repository provides the code for the experiments on the method called randomized SIFT (rSIFT) as described in
A. Hain, D. Jörgens, R. Moreno, "Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering", NeuroImage, 2023.
Make sure that the following dependencies are installed:
- Python 3.8
mrtrix3(follow instructions at https://mrtrix.readthedocs.io/en/latest/index.html)scilpy(follow instructions at https://github.com/scilus/scilpy)- CUDA (Make sure to match the CUDA version with the tensorflow version specified in the requirements.)
Create a virtual environment using by:
virtualenv -p $(which python3) <path_to_environment>
Then, install the package in the activated environment:
source <path_to_environment>/bin/activate
pip install -e <path_to_randomised_filtering_repo>
After that, the python scripts in the scripts folder will be available through
autocompletion in the command line whenever the virtual environment is activated.
The weights of the best performing CV model for each classifier type are provided in the folder data/models.
These weights were obtained with the tensorflow version specified in requirements.txt.
- Data must be downloaded from the Human Connectome Project website at https://www.humanconnectome.org/study/hcp-young-adult.
- Tractograms can be created following the description at https://zenodo.org/record/1477956#.YVTb3jqxU5l
- For streamline compression, use the Dipy function
compress_streamlineswith tol_error=0.35 (https://dipy.org/documentation/1.4.1./reference/dipy.tracking/#dipy.tracking.streamline.compress_streamlines)
The script sift_experiment.sh is the anchor and launches all commands for one rSIFT
experiment. The individual scripts rf_* can be launched individually, too. Each
provides a brief help text when invoked with the option -h.
The collection of different rSIFT experiments (with different parameters) can be
launched using the script main.sh.