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This is a Quantitative Study study.

September 17, 2025

A deep learning pipeline for lifespan cortical surface reconstruction, spherical mapping, and anatomical correspondence

Key Points

The model accurately predicts cortical surfaces across ages from 1 week to 90 years, offering strong inter-subject correspondence.
Evaluation results show that the deep learning pipeline facilitates efficient surface-based analysis of cortical morphology.
The approach iteratively deforms a spherical template using multi-scale velocity fields to match cortical surfaces effectively.
This end-to-end pipeline improves the efficiency and accuracy of producing cortical surfaces for longitudinal studies.

Abstract

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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