MASILab/WM_learning_release

White matter learning

White matter learning-Introduction

We proposed spatial localized patch-wise framework to delineate white matter regions from structural image. To achieve this target, we select six state-of-the-art (TractSeg,Recobundle,Xtract,Tracula,AFQ and AFQ_clipped) tractography algorithms to reconstruct white matter pathways as ground-truth to supervise whole training process. The pipeline of proposed method is shown in the following figure

We envision the proposed framework as a tool to localize white matter regions of interest when diffusion image is not present. The output of propo sed framework is similar to a probability map rather than a binary image, which can provide user with more options to adjust threshold to adjust bundle overlap or bundle overreach for one specific white matter pathway. Different bundle segmentation algorithms do not have exactly same definition for same white matter tracts. Thus, we provide six definition schemes for user to reconstruct white matter in their preferred scheme.

Source code

The github repo includes the train, valdiation and testing code. The different folder name corresponding to different tractography algorithm. As for specific output of each tractography algorithm, please check this repo

Singularity

All sorce code binaries, and third-party dependencies have been packaged into a singulairty container. Each method corresponding to one singularity. In the following part, we will show how to run singularity step by step. The sigularity can be found link

Step 1. Prepare your input and output directory

Let's assume that you work in the $HOME directory. So you can call following command

mkdir $HOME/{INPUTS,OUTPUTS}

Step 2. Move your T1 file into INPUTS directory

mv $your_T1_file $HOME/INPUTS

Then, the raw T1 file would be moved into $HOME/INPUTS folder. In the INPUTS directory, we don't make any assumption about the name of T1. But we assume that T1 file should be in format of .nii.gz

Step 3 Run the singularity

singularity run --bind $HOME/INPUTS:/INPUTS --bind $HOME/OUTPUTS:/OUTPUTS --contain -e --nv $singularity_path

Currently, all singularities only support GPU mode. We are working in the CPU mode.

Step 4 Check output

We have six singularity. The output of each of them can be checked in the followin link TractSeg,RecoBundles XTRACT,Tracula AFQ AFQclipped. The relationship between file name and saved white matter tract name can be found in doc folder in this repository

Reference:

Qi Yang*, Colin B Hansen*, Leon Y. Cai, Francois Rheault, Ho Hin Lee, Bramsh Qamar Chandio, Owen Williams, Susan Resnick, Eleftherios Garyfallidis, Adam W Anderson, Maxime Descoteaux, Kurt G Schilling, Bennett A Landman Learning white matter subject-specific segmentation from structural MRI. Medical Physics, submitted (2021)

Contact:

If you find any issues or questions, please feel free to contact me (qi.yang@vanderbilt.edu)