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DeepVBM: A fully automatic and efficient voxel-based morphometry via deep learning-based segmentation and

Pei-Mao Sun1, Teng-Yi Huang1, Tzu-Chao Chuang2

  • 1Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.

Magnetic Resonance Imaging
|February 4, 2026
PubMed
Summary
This summary is machine-generated.

FuseMorph, a deep learning method, significantly reduces computational demands for brain imaging analysis. This enables efficient, large-scale neuroimaging studies, aiding in early detection of neurodegenerative diseases.

Keywords:
Automated neuroimaging analysisBrain MRIDeep learningImage registrationVoxel-based morphometry

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Artificial Intelligence in Medicine

Background:

  • Voxel-based morphometry (VBM) is crucial for analyzing brain structure using MRI.
  • Conventional VBM faces computational challenges, hindering large-scale neuroimaging database analysis.

Purpose of the Study:

  • Introduce FuseMorph, a deep learning registration method to overcome VBM computational hurdles.
  • Develop DeepVBM, an automated pipeline for efficient processing of high-resolution MRI datasets.

Main Methods:

  • FuseMorph refines registration via iterative inference and parameter search, avoiding full backpropagation.
  • DeepVBM integrates FuseMorph for streamlined, automated VBM analysis.
  • Validation on multiple datasets, including Alzheimer's disease cohorts.

Main Results:

  • FuseMorph achieves registration accuracy comparable to state-of-the-art methods, even on CPUs.
  • DeepVBM substantially reduces computation time for MRI processing.
  • Successfully detected gray matter atrophy patterns in Alzheimer's disease patients.

Conclusions:

  • FuseMorph and DeepVBM offer an accessible, efficient, and scalable solution for neuroimaging research.
  • The method shows potential for early detection and monitoring of neurodegenerative changes.
  • Facilitates efficient analysis of large, multi-center neuroimaging studies.