Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each block to capture the multi-scale MRI information of each vertex and its neighborhood. This is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices in one second. The performance is evaluated on the Human Connectome Project (HCP) dataset including T1-weighted MRI scans of 300 subjects. The experimental results demonstrate that PialNN reduces the geometric error of the predicted pial surface by 30% compared to state-of-the-art deep learning approaches.
@article{arxiv.2109.03693,
title = {PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction},
author = {Qiang Ma and Emma C. Robinson and Bernhard Kainz and Daniel Rueckert and Amir Alansary},
journal= {arXiv preprint arXiv:2109.03693},
year = {2021}
}
Comments
Accepted in The 4th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN2021)