antsnetct

Cortical thickness with ANTsPyNet

Installation

Install a complete runnable container from Docker Hub.

For non-containerized installation (not recommended), first install the system requirements ANTs, c3d, and trim_neck.sh. Then install with pip

git clone https://github.com/ftdc-picsl/antsnetct
pip install antsnetct

Configuration

All users must set the environment variable TEMPLATEFLOW_HOME to a location containing the template to be used. You can use any template as long as it has both a _T1w.nii.gz file and an associated brain mask.

Configuration for docker

docker run --rm -it -v /path/to/local/templateflow:/opt/templateflow \
  -e TEMPLATEFLOW_HOME=/opt/templateflow \
  -e ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=2 \
  antsnetct:latest --help

Configuration for singularity

Because singularity does not allow the container to set its user, additional options are required.

export SINGULARITYENV_ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=2
export SINGULARITYENV_TEMPLATEFLOW_HOME=/opt/templateflow
singularity run --cleanenv --no-home --home /home/antspyuser \
  -B /path/to/local/templateflow:/opt/templateflow \
  antsnetct_latest.sif --help

Cross-sectional thickness

Basic pipeline

• Conform to standard orientation.
• Trim neck (optional).
• Brain extraction (done by antspynet, or supply your own brain mask dataset, or have one in the input dataset).
• Segmentation and bias correction. Priors can come from deep_atropos or user-supplied priors. Priors can be used as input to antsAtroposN4.sh, or used directly as the final segmentation.
• Denoise and bias-correct T1w using the segmentation.
• Thickness computation. Same as antsCorticalThickness.sh.
• Warp to template - similar to antsCorticalThickness.sh
• Generate template space derivatives, Jacobian, GMP, thickness, etc.

See antsnetct --help for current usage.

Longitudinal thickness

First run the cross-sectional (cx) pipeline on all time points.

Defaults to running all session T1w images, but user can input a custom list.

The SST is built from the processed T1w images (ie, desc-biascorr) but the longitudinal segmentation is done with the preprocessed T1w images (ie, desc-preproc), which is oriented and optionally neck-trimmed, but not denoised or bias-corrected.

Pipeline overview:

• Build SST from the cx-processed T1w
• Define a common brain mask from the cx sessions (basically the union of the session masks in SST space)
• Segment SST with ANTsPyNet
• Register SST to group template (optional)

For each session image:

• Register to SST
• Warp SST segmentation to each session space
• Run denoising / N4 / Atropos on session T1w, using SST segmentation as priors.
• Compute cortical thickness on session T1w
• Warp derivatives to SST / group template space

See antsnetct --longitudinal --help for current usage.