Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation
This code implements a deep learning method for white matter fiber clustering using diffusion MRI data, as described in the following paper:
Chen Y, Zhang C, Xue T, Song Y, Makris N, Rathi Y, Cai W, Zhang F, O'Donnell LJ. Deep Fiber Clustering: Anatomically Informed Fiber Clustering with Self-supervised Deep Learning for Fast and Effective Tractography Parcellation. NeuroImage. 2023 Apr 3:120086.
The code has been tested with Python 3.7, Pytorch 1.7.1, CUDA 10.1 on Ubuntu 18.04.
whitematteranalysis
scikit-learn
To train a model for fiber clustering with tractography data:
python train.py -indir <path of training data>
To evaluate the model with testing data:
python test.py -indir <path of testing data> -modeldir <path of training model>
Fast and effective fiber clustering was achieved with the proposed method. Below is a visualization of the obtained clusters.
The training model and testing dataset are available here: https://github.com/SlicerDMRI/DFC/releases
See our project page https://deepfiberclustering.github.io/ for more details.
Chen Y, Zhang C, Xue T, Song Y, Makris N, Rathi Y, Cai W, Zhang F, O'Donnell LJ. Deep Fiber Clustering: Anatomically Informed Fiber Clustering with Self-supervised Deep Learning for Fast and Effective Tractography Parcellation. NeuroImage. 2023 Apr 3:120086.