About DL+DiReCT

DL+DiReCT combines a deep learning-based neuroanatomy segmentation and cortex parcellation with a diffeomorphic registration technique to measure cortical thickness from T1w MRI.

If you are using DL+DiReCT in your research, please cite (bibtex) the corresponding publication:

Rebsamen, M, Rummel, C, Reyes, M, Wiest, R, McKinley, R.
Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation.
Human brain mapping. 2020; 41: 4804-4814. https://doi.org/10.1002/hbm.25159

Installation

Create virtual environment (optional)

Download and install Miniconda and create a new conda environment:

conda create -y -n DL_DiReCT python=3.10
source activate DL_DiReCT

Install DL+DiReCT

cd ${HOME}
git clone https://github.com/SCAN-NRAD/DL-DiReCT.git
cd DL-DiReCT
pip install numpy && pip install -e .

Usage

Run dl+direct on a T1-weighted MRI including skull-stripping (--bet) using HD-BET with:

source activate DL_DiReCT
dl+direct --subject <your_subj_id> --bet <path_to_t1_input.nii.gz> <output_dir>

Following files of interest are generated in the output directory:

- T1w_norm.nii.gz		Re-sampled input volume
- T1w_norm_seg.nii.gz		Segmentation
- T1w_norm_thickmap.nii.gz	Thickness map
- result-vol.csv		Segmentation volumes
- result-thick.csv		ROI-wise mean cortical thickness
- result-thickstd.csv		ROI-wise standard deviations of cortical thickness
- label_def.csv			Label definitions of the segmentation

Results may be collected into FreeSurfer alike statistics files with stats2table.

Contrast-enhanced (CE) MRI

To process images with a contrast agent (contrast-enhanced), use the option --model v6 (Rebsamen et al., 2022).

Available Models

The following models are available with the --model ... option:

• v0: Default, for un-enhanced T1w MRI, cortex parcellation with Desikan-Killiany atlas (Rebsamen et al., 2020)
• v6: For both contrast-enhanced and un-enhanced MRI (Rebsamen et al., 2022)
• v7: Same as v6, with 74 region per hemisphere according the Destrieux atlas (Rebsamen et al., 2022)

Frequently Asked Questions

For further details, consult the corresponding publication and the FAQ or contact us