Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation
This code implements a deep learning method for white matter fiber clustering using diffusion MRI data, as described in the following paper:
Yuqian Chen, Chaoyi Zhang, Yang Song, Nikos Makris, Yogesh Rathi, Weidong Cai, Fan Zhang, Lauren J. O’Donnell. Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation. (MICCAI 2021, travel award)
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 trainign 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, Yuqian, Chaoyi Zhang, Yang Song, Nikos Makris, Yogesh Rathi, Weidong Cai, Fan Zhang, and Lauren J. O’Donnell. "Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2021.