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Related Experiment Video

Updated: Dec 23, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning.

Michael Rebsamen^1,2, Yannick Suter^3,4, Roland Wiest¹

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

Frontiers in Neurology

|April 24, 2020

Summary

This summary is machine-generated.

This study introduces a deep learning method for rapid brain morphometry from MRI scans, offering clinically relevant accuracy for diagnosing neurological disorders. The approach significantly reduces processing time compared to traditional tools.

Keywords:

MRI cortical thickness deep learning epilepsy human brain morphometry

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Published on: November 14, 2019

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A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

Area of Science:

Neuroimaging
Computational Neuroscience
Artificial Intelligence in Medicine

Background:

Brain morphometry using MRI is crucial for diagnosing neurological disorders.
Current morphometric analysis tools are computationally intensive and time-consuming for clinical use.

Purpose of the Study:

To develop and validate a deep learning approach for rapid brain morphometry prediction from T1-weighted MRI.
To assess the accuracy and clinical relevance of deep learning-derived morphometric measures.

Main Methods:

A convolutional neural network (CNN) was trained on 574 subjects using FreeSurfer 6.0 for ground truth generation.
The CNN predicted 165 morphometric measures, including subcortical volumes and cortical thickness/curvature.

Main Results:

CNN predictions showed good correlation with FreeSurfer data (ICC > 0.75 for some regions).
Predicted cortical thinning rates aligned with known age-related gray matter atrophy.
The model effectively distinguished epilepsy patients from controls, comparable to large-scale studies.

Conclusions:

Deep learning enables rapid (seconds) estimation of human brain morphometry from MRI.
The developed method offers comparable accuracy to existing techniques for subcortical volumes and cortical thicknesses.