[Warning: only App versions >= 2.2 are currently maintained.]
Segment 72 white matter tracts.
Brainlife App for Automatic White Matter Bundle Segmentation using MIC-DKFZ/TractSeg. A tool for fast and accurate white matter bundle segmentation from Diffusion MRI.
TractSeg was developed by Jakob Wasserthal from Divison of Medical Image Computing at German Cancer Research Center (DKFZ). It uses pretrained 3D Fully Convolutional Neural Networks (FCNNs) to quickly identify human white matter tracts (bundles).
Plese refer to the official repository for more details: MIC-DKFZ/TractSeg.
brainlife.io is publicly funded and for the sustainability of the project it is helpful to Acknowledge the use of the platform. We kindly ask that you acknowledge the funding below in your publications and code reusing this code.
We kindly ask that you cite the following articles when publishing papers and code using this code:
You can submit this App online at https://doi.org/10.25663/brainlife.app.186 via the “Execute” tab.
Input: \ The dwi image in .nii format. TractSeg will generate CSD peaks from this dwi before running TOM tracking, Tractography, and Tractometry. The input dwi image must have the same "orientation" as the Human Connectome Project data (MNI space) (LEFT must be on the same side as LEFT of the HCP data). Alternatively, a peaks.nii.gz file can be given as input (App "TractSeg - from peaks" https://doi.org/10.25663/brainlife.app.684).
Optional inputs:
Output: \ The segmented white matter tracts.
config.json
with something like the following content with paths to your input files:
{
"dwi": "testdata/dwi.nii.gz",
"bvecs": "testdata/dwi.bvecs",
"bvals": "testdata/dwi.bvals",
"preprocess": false,
"csd": "csd",
"nr_fibers": 2000,
"bundles": "",
"tractometry_input": "peak_length"
}
main
.
./main
This App will generate four outputs:
This App only requires singularity to run.