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This is a Quantitative Study study.

September 17, 2025Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition

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

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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.

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Implication

This study demonstrates a deep learning pipeline for accurate cortical surface reconstruction and mapping, highlighting its utility in anatomical correspondence.

Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/68d4597b31b076d99fa5ca8b https://doi.org/10.58530/2025/0875

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Implication

This study demonstrates a deep learning pipeline for accurate cortical surface reconstruction and mapping, highlighting its utility in anatomical correspondence.

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

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Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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