Tool for fast and accurate white matter bundle segmentation from Diffusion MRI. It can create bundle segmentations, segmentations of the endregions of bundles and Tract Orientation Maps (TOMs).

The tool works very well for data similar to the Human Connectome Project. For other MRI datasets it works well for all bundles except for the Commissure Anterior (CA) and the Fornix (FX) which are incomplete sometimes.

TractSeg is the code for the papers TractSeg - Fast and accurate white matter tract segmentation (free arxiv version) and Tract orientation mapping for bundle-specific tractography. Please cite the papers if you use it.

• Install
• How to use
• FAQ
• Train your own model

TractSeg only runs on Linux and OSX. It works with Python 2 and Python 3.

• Pytorch (v1.0)
• Mrtrix 3
• FSL (if you already have a brain mask and do not use the option --preprocess this is not needed)

Latest stable version:

pip install https://github.com/MIC-DKFZ/TractSeg/archive/v1.9.zip

NOTE: See CHANGELOG for (breaking) changes of each version

You can also directly use TractSeg via Docker (contains all prerequisites).

sudo docker run -v /absolute/path/to/my/data/directory:/data \
-t wasserth/tractseg_container:master TractSeg -i /data/my_diffusion_file.nii.gz -o /data --preprocess

On OSX you might have to increase the Docker memory limit from the default of 2GB to something like 7GB. For GPU support use the container wasserth/tractseg_container_gpu:1.7.1 and run it with nvidia-docker.

To segment the bundles on a Diffusion Nifti image run the following command. (Diffusion.bvals and Diffusion.bvecs have to be in the same directory as the input image.) (You can use the example image provided in this repository under examples.)

TractSeg -i Diffusion.nii.gz --raw_diffusion_input

This will create a folder tractseg_ouput inside of the same directory as your input file with one binary segmentation nifti image for each bundle.

NOTE: Your input image should have the same orientation as MNI space. Using the option --preprocess TractSeg will automatically move your input image to MNI space (rigid registration).

TractSeg -i my/path/my_diffusion_image.nii.gz
         -o my/output/directory
         --bvals my/other/path/my.bvals
         --bvecs yet/another/path/my.bvec
         --raw_diffusion_input
         --preprocess

Use --help to see all options.

To avoid generating the MRtrix CSD peaks every time you run TractSeg you can also provide them directly by skipping the option --raw_diffusion_input.

TractSeg -i my/path/my_mrtrix_csd_peaks.nii.gz

For each bundle create a Tract Orientation Map (Wasserthal et al., Tract orientation mapping for bundle-specific tractography). This gives you one peak per voxel telling you the main orientation of the respective bundle at this voxel. Can be used for bundle-specific tracking (add option --track to generate streamlines).

TractSeg -i peaks.nii.gz --output_type TOM

Peaks and streamlines can be visualized using for example MITK Diffusion.

NOTE: Peaks have to be flipped along the z-axis to be displayed correctly in MITK.

Get segmentations of the regions were the bundles start and end (helpful for filtering fibers that do not run from start until end).

TractSeg -i peaks.nii.gz --output_type endings_segmentation

Tracks on TOMs and only keeps fibers not leaving the bundle mask and starting and ending in the endpoint regions.

TractSeg -i peaks.nii.gz --output_type tract_segmentation
TractSeg -i peaks.nii.gz --output_type endings_segmentation
TractSeg -i peaks.nii.gz --output_type TOM 
Tracking -i peaks.nii.gz

Create map showing where the method is uncertain about its segmentation (uses monte carlo dropout: https://arxiv.org/abs/1506.02142)

TractSeg -i peaks.nii.gz --uncertainty

See Documentation of Tractometry.

Best pratices for standard usecases.

The following list shows the index of each extracted bundle in the output file (if using --single_output_file).

0: AF_left         (Arcuate fascicle)
1: AF_right
2: ATR_left        (Anterior Thalamic Radiation)
3: ATR_right
4: CA              (Commissure Anterior)
5: CC_1            (Rostrum)
6: CC_2            (Genu)
7: CC_3            (Rostral body (Premotor))
8: CC_4            (Anterior midbody (Primary Motor))
9: CC_5            (Posterior midbody (Primary Somatosensory))
10: CC_6           (Isthmus)
11: CC_7           (Splenium)
12: CG_left        (Cingulum left)
13: CG_right   
14: CST_left       (Corticospinal tract)
15: CST_right 
16: MLF_left       (Middle longitudinal fascicle)
17: MLF_right
18: FPT_left       (Fronto-pontine tract)
19: FPT_right 
20: FX_left        (Fornix)
21: FX_right
22: ICP_left       (Inferior cerebellar peduncle)
23: ICP_right 
24: IFO_left       (Inferior occipito-frontal fascicle) 
25: IFO_right
26: ILF_left       (Inferior longitudinal fascicle) 
27: ILF_right 
28: MCP            (Middle cerebellar peduncle)
29: OR_left        (Optic radiation) 
30: OR_right
31: POPT_left      (Parieto‐occipital pontine)
32: POPT_right 
33: SCP_left       (Superior cerebellar peduncle)
34: SCP_right 
35: SLF_I_left     (Superior longitudinal fascicle I)
36: SLF_I_right 
37: SLF_II_left    (Superior longitudinal fascicle II)
38: SLF_II_right
39: SLF_III_left   (Superior longitudinal fascicle III)
40: SLF_III_right 
41: STR_left       (Superior Thalamic Radiation)
42: STR_right 
43: UF_left        (Uncinate fascicle) 
44: UF_right 
45: CC             (Corpus Callosum - all)
46: T_PREF_left    (Thalamo-prefrontal)
47: T_PREF_right 
48: T_PREM_left    (Thalamo-premotor)
49: T_PREM_right 
50: T_PREC_left    (Thalamo-precentral)
51: T_PREC_right 
52: T_POSTC_left   (Thalamo-postcentral)
53: T_POSTC_right 
54: T_PAR_left     (Thalamo-parietal)
55: T_PAR_right 
56: T_OCC_left     (Thalamo-occipital)
57: T_OCC_right 
58: ST_FO_left     (Striato-fronto-orbital)
59: ST_FO_right 
60: ST_PREF_left   (Striato-prefrontal)
61: ST_PREF_right 
62: ST_PREM_left   (Striato-premotor)
63: ST_PREM_right 
64: ST_PREC_left   (Striato-precentral)
65: ST_PREC_right 
66: ST_POSTC_left  (Striato-postcentral)
67: ST_POSTC_right
68: ST_PAR_left    (Striato-parietal)
69: ST_PAR_right 
70: ST_OCC_left    (Striato-occipital)
71: ST_OCC_right

import nibabel as nib
import numpy as np
from tractseg.python_api import run_tractseg
peaks = nib.load("tests/reference_files/peaks.nii.gz").get_data()
segmentation = run_tractseg(peaks)

You can use different types of tracking when doing --track:

• "Probabilistic" tracking on TOM peaks [default].
  TractSeg -i peaks.nii.gz --output_type TOM --track --only_track
  Probabilistic means that at each step a small random factor will be added to the direction given by the TOM peaks. If not doing this on low resolution data it sometimes gets difficult finding fibers running from start to end and covering the whole bundle.

• Probabilistic tracking on original FODs.
  TractSeg -i WM_FODs.nii.gz --output_type TOM --track --only_track --track_FODs iFOD2
  Is calling Mrtrix iFOD2 tracking internally. Does not use TOM peaks but the original FODs. The results will get filtered by the bundle mask and have to start and end in the endings masks.

The input 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). If the image orientation and the gradient orientation of your data is the same as in examples/Diffusion.nii.gz you are fine. Otherwise you should rigidly register your image to MNI space (the brains do not have to be perfectly aligned but must have the same LEFT/RIGHT orientation). If you use the option --preprocess TractSeg will do this automatically for you. Otherwise you can use the following FSL commands to rigidly register you image to MNI space (uses the FA to calculate the transformation as this is more stable):

calc_FA -i Diffusion.nii.gz -o FA.nii.gz --bvals Diffusion.bvals --bvecs Diffusion.bvecs \
--brain_mask nodif_brain_mask.nii.gz

flirt -ref tractseg/resources/MNI_FA_template.nii.gz -in FA.nii.gz \
-out FA_MNI.nii.gz -omat FA_2_MNI.mat -dof 6 -cost mutualinfo -searchcost mutualinfo

flirt -ref tractseg/resources/MNI_FA_template.nii.gz -in Diffusion.nii.gz \
-out Diffusion_MNI.nii.gz -applyxfm -init FA_2_MNI.mat -dof 6
cp Diffusion.bvals Diffusion_MNI.bvals
cp Diffusion.bvecs Diffusion_MNI.bvecs

Make sure your input image is in MNI space. Even if the input image is in MNI space the Mrtrix peaks might still be flipped. TractSeg is automatically checking for that and printing a warning if the peaks have the wrong orientation. However, in very few cases (~2%) TractSeg fails to detect the right flipping axis. In those cases you should view the peaks in mrview and make sure they have the proper orientation. Otherwise you might have to flip the sign along the x, y or z axis using the following command:

flip_peaks -i my_peaks.nii.gz -o my_peaks_flip_y.nii.gz -a y

You can use the following three options to improve your results:

• --super_resolution The input image is upsampled to 1.25mm resolution (the resolution TractSeg was trained on) and finally downsampled back to the original resolution. Using --super_resolution will output the image at 1.25mm. Especially if image resolution is low parts of the CA can get lost during downsampling.
• --bundle_specific_threshold Lowering the threshold for converting the model output to binary segmentations. Instead of 0.5 use 0.3 for CA and 0.4 for CST and FX. For all other bundles keep 0.5. This will increase sensitivity for those difficult bundles.
• --postprocess Will fill small holes in the segmentation and remove small blobs not connected to the rest of the segmentation. (not working for output_type TOM)

Using those options will improve the results for CA/FX quite often.

Per default the pretrained weights will be downloaded to and loaded from ~/.tractseg/. You can change this directory by adding weights_dir=/absolute/path_to_where/you_want_it to ~/.tractseg/config.txt in a new line (if the file does not exist yet you have to create it).
Normally the pretrained weights will automatically be downloaded in the background right when they are needed. In some cases you might want to download all of them at once. To do so you can simply run download_all_pretrained_weights and the weights will be download to ~/.tractseg/ or the location you specified in ~/.tractseg/config.txt.

You can check if you installed Mrtrix correctly if you can run the following command on your terminal: dwi2response -help

You can check if you installed FSL correctly if you can run the following command on your terminal: bet -help

TractSeg uses these commands so they have to be available.

TractSeg uses a pretrained model. However, you can also train your own model on your own data. But be aware: This is more complicated than just running with the pretrained model. The following guide is quite short and you might have problems following every step. Contact the author if you need help training your own model.

1. Install BatchGenerators: pip install https://github.com/MIC-DKFZ/batchgenerators/archive/master.zip
2. The folder structure of your training data should be the following:

custom_path/HCP/subject_01/
      '-> mrtrix_peaks.nii.gz       (mrtrix CSD peaks;  shape: [x,y,z,9])
      '-> bundle_masks.nii.gz       (Reference bundle masks; shape: [x,y,z,nr_bundles])
custom_path/HCP/subject_02/
      ...

3. Adapt the file tractseg/config/custom/My_custom_experiment.py.
4. Create a file ~/.tractseg/config.txt. This contains the path to your data directory, e.g. working_dir=custom_path.
5. Adapt tractseg.libs.DatasetUtils.scale_input_to_unet_shape() to scale your input data to the UNet input size of 144x144. This is not very convenient. Contact the author if you need help.
6. Adapt tractseg.libs.exp_utils.get_bundle_names() with the bundles you use in your reference data.
7. Adapt tractseg.libs.exp_utils.get_labels_filename() with the names of your label files.
8. Adapt tractseg.libs.Subjects with the list of your subject IDs.
9. Run ExpRunner --config My_custom_experiment
10. custom_path/hcp_exp/My_custom_experiment contains the results

To build a docker container with all dependencies run the following command in project root:

sudo docker build -t tractseg_container -f Dockerfile_CPU .