Keywords: Gray Matter, Analysis/Processing, Registration, Cortical Surface Reconstruction
Motivation: Accurate lifespan cortical surface reconstruction (CSR) is important for understanding brain anatomy and longitudinal studies.
Goal(s): We present a deep learning pipeline that simultaneously reconstructs cortical surfaces, performs spherical mapping, and establishes anatomical correspondences across subjects.
Approach: Our model learns multi-scale velocity fields to iteratively deform a spherical template to match the inner or outer cortical surfaces, with a constraint that registers subjects to a reference surface for one-to-one vertex correspondence.
Results: Evaluation on a dataset spanning ages from 1 week to 90 years demonstrates that our model accurately predicts surfaces with inter-subject correspondence.
Impact: Our end-to-end pipeline offers a fast and accurate method for generating cortical surfaces, making it an efficient tool for surface-based analysis of cortical morphology.
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