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PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction

This repository contains the PyTorch implementation of the paper:

PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction [Paper]

Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary

The 4th Workshop on Machine Learning in Clinical Neuroimaging (MLCN2021)

Abstract. Pial Neural Network (PialNN) is a 3D deep learning framework for pial surface reconstruction. It is trained end-to-end to deform a given initial white matter surface to a target pial surface by a sequence of learned deformation blocks. PialNN is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices within 1s runtime.

Get Started

Dependencies

The following packages are required for running PialNN:

Dataset

The following files are required for training and testing. The data should be generated by FreeSurfer or in the same format.

./data/train/SUBJECT_NAME/mri/orig.mgz
./data/train/SUBJECT_NAME/surf/lh.white
./data/train/SUBJECT_NAME/surf/lh.pial
./data/train/SUBJECT_NAME/surf/rh.white
./data/train/SUBJECT_NAME/surf/rh.pial

orig.mgz is the brain MRI volume. lh.white/rh.white are the meshes of initial white matter cortical surfaces of left/right cerebrum hemispheres. lh.pial/rh.pial are ground truth pial surface meshes. An example data is provided in ./data/test/example/.

We use HCP Young Adult Dataset for training and testing in this paper.

Running Demos

You can run the following codes to predict a pial surface based on the provided example data.

git clone https://github.com/m-qiang/PialNN.git
python eval.py --data_path=./data/test/ --hemisphere=lh --save_mesh_eval=True

The output pial surface mesh will be saved in ./ckpts/eval/ as .obj format.

Training

Download your training data to ./data/train/ and run

python train.py --data_path=./data/train/ --hemisphere=lh

For more information about the training configuration, please see

python train.py --help

The pretrained models of PialNN for both left and right hemispheres are provided:

./ckpts/model/pialnn_pretrained_lh.pt
./ckpts/model/pialnn_pretrained_rh.pt

Evaluation

Download your testing data to ./data/test/ and run

python eval.py --data_path=./data/test/ --hemisphere=lh

Three distance-based matrics will be computed: Chamfer Distance (CD), Average Absolute Distance (AD), and Hausdorff Distance (HD).

For the purpose of debugging or validation, the evaluation result of the provided example data in ./data/test/example/ should be

  • CD=0.39, AD=0.20, HD=0.40