TopoFit

TopoFit is a learning-based tool that rapidly fits a topologically-correct surface to cerebral cortex in brain MRI. This code base implements the model described in the following paper:

TopoFit: Rapid Reconstruction of Topologically-Correct Cortical Surfaces
Andrew Hoopes, Juan Eugenio Iglesias, Bruce Fischl, Douglas Greve, Adrian V. Dalca
Medical Imaging with Deep Learning. 2022.

To evaluate a pretrained TopoFit model, you can download the development version of FreeSurfer and run the mris_estimate_wm command line tool. However, to develop and train a TopoFit model using custom data, clone this repository and follow the instructs below.

Download file requirement

The guided (or neighborhood-based) training loss requires a 500MB neighorhood mapping file that is too large to store on GitHub. In order to train a model, you must download neighorhoods.npz and move it to the topofit subdirectory of this repository.

Preprocessing

Brain surface data needs to be preprocessed so that all 'ground-truth' meshes share the same template topology. First, FreeSurfer's recon-all command must be run on each subject's T1w brain MRI. Following this, the preprocess script must be run on each recon output:

./preprocess /path/to/recon/subject

This will generate additional surface files in the subject's surf subdirectory.

Training

Once surfaces have been preprocessed, a TopoFit model is trained for a given brain hemisphere (lr or rh) with:

./train --hemi lh \
        --outdir /path/to/output/directory \
        --training-subjs /path/to/train.txt \
        --validation-subjs /path/to/validation.txt

In this example, train.txt and validation.txt are line-by-line lists of full paths to preprocessed recon subjects. Subjects will be randomly sampled from this list during training. Only 5-20 validation subjects are necessary. This script implements a learning-rate decay strategy based on the validation distance metric. Training will exit automatically once accuracy plateaus. In general, training should complete between 2000-3000 epochs. Logging and model weight checkpoints will be saved to the specified output directory.

Evaluation

Once a model has been trained, it can be evaluated on any set of recon-all subjects by running:

./evaluate --hemi lh \
           --model /path/to/output/directory/2000.pt \
           --subjs /path/to/recon/subject ...

This will save the predicted FreeSurfer-formatted surface as lh.white.topofit in the subject's surf subdirectory.

Docker and singularity

A Dockerfile recipe exists in the docker folder of this repository. Build commands for docker and singularity exist in the readme in the docker folder. A more specific readme exists in the docker directory.