Liu, Wan, Qi Lu, Zhizheng Zhuo, Yuxing Li, Yunyun Duan, Pinnan Yu, Liying Qu, Chuyang Ye, and Yaou Liu. "Volumetric Segmentation of White Matter Tracts with Label Embedding." NeuroImage (2022), 250: 118934.

• Linux & OSX, Python>=3.6
• Pytorch
• Mrtrix 3 (>=3.0 RC3)
• FSL

• We use TractSeg as the baseline, and install it from local source code:

git clone https://github.com/MIC-DKFZ/TractSeg.git
pip install -e TractSeg

• Install BatchGenerators:

git clone https://github.com/MIC-DKFZ/batchgenerators.git
pip intall -e batchgenerators

• Create a file ~/.tractseg/config.txt, and write the path of your own directory in config.txt, e.g. working_dir=Your_OutputPath.

• Download our code as zip, and unzip it.
• Save our code in the same directory of TractSeg code, i.e. Your_CodePath.

• Download the HCP scans and the gold standard of WM tracts.
• Extract the input peaks images from dMRI scans with 'Your_CodePath/TractSegWithLabelEmbedding/bin/Generate_Peaks.py'.
• Arrange the peaks and annotations of different subjects to the following structure -> used for network testing:

Your_DataPath/HCP_for_training_COPY/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])
Your_DataPath/HCP_for_training_COPY/subject_02/
      ...

• Remove the non-brain area of data in HCP_for_training_COPY fold with 'Your_CodePath/TractSegWithLabelEmbedding/bin/Remove_Nonbrain.py', and arrange the data to the following structure -> used for network training:

Your_DataPath/HCP_preproc/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])
Your_DataPath/HCP_preproc/subject_02/
      ...

• Adapt 'Your_CodePath/TractSegWithLabelEmbedding/tractseg/libs/system_config.py' and modify DATA_PATH to 'Your_DataPath/HCP_preproc'.
• Adapt 'Your_CodePath/TractSegWithLabelEmbedding/tractseg/data/subjects.py' with the list of your subject IDs.

• Set the temporary enviroment variable in terminal to our code path:

export PYTHONPATH=$PYTHONPATH:Your_CodePath/TractSegWithLabelEmbedding

• Train the network:

python run Your_CodePath/TractSegWithLabelEmbedding/bin/ExpRunner

• The training output is saved in 'Your_OutputPath/hcp_exp/my_custom_experiment'.

• Set the temporary enviroment variable in terminal to our code path:

export PYTHONPATH=$PYTHONPATH:Your_CodePath/TractSegWithLabelEmbedding

• You can directly test the network using the provided data in the example fold and network model (link1 & link2) after modifying the corresponding paths in 'Your_CodePath/TractSegWithLabelEmbedding/bin/ExpRunner_test.py':

python run Your_CodePath/TractSegWithLabelEmbedding/bin/ExpRunner_test.py

• Test the network with your trained model and test data after modifing the corresponding paths in ExpRunner_test.py.

Note that we did experiments through modificating the TractSeg-v2.0, and the main modifications are listed as follows:

1. Add extra scripts to prepare data as in 3.2 Data preparation and test model as in 'TractSegWithLabelEmbedding/bin/ExpRunner_test.py'.
   
2. Add extra scripts to define network as in 'TractSegWithLabelEmbedding/tractseg/models/unet_pytorch_deepup_simp1.py' (used for network training) and 'TractSegWithLabelEmbedding/tractseg/models/unet_pytorch_deepup_simp1_test.py' (used for network testing).
   
3. Modify the loss settings as in 'TractSegWithLabelEmbedding/tractseg/libs/trainer.py' and 'TractSeg_labelembed/tractseg/models/base_model.py'.