Code is still under managing. Accepted in [CDMRI 2025] "Streamline Signature Net(SSN): Efficient White Matter Pathway Recognition for Bundles Parcellation Using Path Signature", Computational Diffusion MRI

Uploaded all files. But comments and instructions are still under developing.

License

This repository contains code under two licenses:

• File in src/new/lars.py is licensed under Apache-2.0, see LICENSE.Apache-2.0
• Files in src/ except src/new/lars.py are licensed under BSD-3-Clause, see LICENSE.BSD-3-Clause

run

conda create --name SSN python=3.12

conda activate SSN

pip install git+https://github.com/SlicerDMRI/whitematteranalysis.git

pip install torch torchvision torchaudio

pip install argparse signatory h5py matplotlib scikit-learn==1.5.2

conda install -c conda-forge libstdcxx-ng=13

git clone https://github.com/RenchZhao/Streamline_Signature_Net.git

cd Streamline_Signature_Net

if need to prepare datasets

conda create -n pnlpipe3 python=3.6

conda install -c mrtrix3 mrtrix3

possible bugs

ImportError: /home/user/anaconda3/envs/SSN/bin/../lib/libstdc++.so.6: version `GLIBCXX_3.4.32' not found (required by /home/user/anaconda3/envs/SSN/lib/python3.12/site-packages/signatory/_impl.cpython-312-x86_64-linux-gnu.so)

run: conda install -c conda-forge libstdcxx-ng=13

"cross_val_txt" variable in new/gen_train_h5.py contains subjects of 5-fold cross validation of HCP dataset: https://zenodo.org/records/1285152

You can copy contents below without quotation mark to your own "5_fold.txt": ''' fold1 = ['992774', '991267', '987983', '984472', '983773', '979984', '978578', '965771', '965367', '959574', '958976', '957974', '951457', '932554', '930449', '922854', '917255', '912447', '910241', '907656', '904044'] fold2 = ['901442', '901139', '901038', '899885', '898176', '896879', '896778', '894673', '889579', '887373', '877269', '877168', '872764', '872158', '871964', '871762', '865363', '861456', '859671', '857263', '856766'] fold3 = ['849971', '845458', '837964', '837560', '833249', '833148', '826454', '826353', '816653', '814649', '802844', '792766', '792564', '789373', '786569', '784565', '782561', '779370', '771354', '770352', '765056'] fold4 = ['761957', '759869', '756055', '753251', '751348', '749361', '748662', '748258', '742549', '734045', '732243', '729557', '729254', '715647', '715041', '709551', '705341', '704238', '702133', '695768', '690152'] fold5 = ['687163', '685058', '683256', '680957', '679568', '677968', '673455', '672756', '665254', '654754', '645551', '644044', '638049', '627549', '623844', '622236', '620434', '613538', '601127', '599671', '599469'] '''

new/get_label_dict.py is used to generate "label_di" variable in new/gen_train_h5.py