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CortexMorph: fast cortical thickness estimation via diffeomorphic registration using VoxelMorph.

Richard McKinley1, Christian Rummel1

  • 1Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, Bern, Switzerland.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|December 5, 2025
PubMed
Summary
This summary is machine-generated.

CortexMorph uses deep learning to rapidly estimate cortical thickness from MRI scans, offering a faster and sensitive alternative to traditional methods for detecting neurological conditions.

Keywords:
Deep learningMRIMorphometryUnsupervised image registrationcortical thickness

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Cortical thickness estimation is crucial for diagnosing neurological and psychiatric disorders.
  • Surface-based methods like Freesurfer are common but can be time-consuming.
  • The DiReCT method offers an alternative, but its registration step is slow.

Purpose of the Study:

  • To develop a faster method for cortical thickness estimation using deep learning.
  • To combine deep learning-based segmentation with a novel deformation field regression method.
  • To maintain or improve sensitivity to cortical atrophy detection.

Main Methods:

  • Introduced CortexMorph, an unsupervised deep learning method to regress deformation fields for DiReCT.
  • Integrated CortexMorph with a deep learning segmentation model for rapid thickness estimation.
  • Validated the method on the OASIS-3 dataset and a synthetic cortical thickness phantom.

Main Results:

  • CortexMorph enables region-wise cortical thickness estimation in seconds from T1-weighted MRI.
  • The combined approach demonstrates high sensitivity to subvoxel cortical thinning.
  • Achieved faster processing times compared to traditional iterative registration methods.

Conclusions:

  • CortexMorph significantly accelerates DiReCT-based cortical thickness analysis.
  • This method provides a rapid and sensitive tool for detecting cortical atrophy.
  • The approach holds promise for clinical applications in neurological and psychiatric research.