https://github.com/djoerch/randomised_filtering
Code for randomised tractogram filtering using rSIFT
https://github.com/djoerch/randomised_filtering
streamlines tractogram-filtering tractography
Last synced: about 1 year ago
JSON representation
Code for randomised tractogram filtering using rSIFT
- Host: GitHub
- URL: https://github.com/djoerch/randomised_filtering
- Owner: djoerch
- Created: 2021-03-10T03:17:41.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-07-14T21:33:01.000Z (almost 3 years ago)
- Last Synced: 2025-03-01T00:59:15.796Z (over 1 year ago)
- Topics: streamlines, tractogram-filtering, tractography
- Language: Python
- Homepage:
- Size: 120 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Randomised Filtering
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](https://doi.org/10.1016/j.neuroimage.2023.120248), 2023.
## Installation Instructions
#### Dependencies
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.)
#### Installation
Create a virtual environment using by:
```
virtualenv -p $(which python3)
```
Then, install the package in the activated environment:
```
source /bin/activate
pip install -e
```
After that, the **python** scripts in the `scripts` folder will be available through
autocompletion in the command line whenever the virtual environment is activated.
## Model weights
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
- 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_streamlines`
with tol_error=0.35
(https://dipy.org/documentation/1.4.1./reference/dipy.tracking/#dipy.tracking.streamline.compress_streamlines)
## How to use
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`.