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Updated: Feb 28, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Machine learning in a graph framework for subcortical segmentation.

Zhihui Guo^1,2, Satyananda Kashyap^3,2, Milan Sonka^3,2

¹Dept. of Biomedical Engineering, Univ. of Iowa, Iowa City, IA, USA 52242.

Proceedings of Spie--The International Society for Optical Engineering

|June 20, 2017

Summary

This summary is machine-generated.

We developed LOGISMOS-RF, a novel 3D graph-based machine learning method for segmenting the caudate and putamen in brain MRIs. This approach significantly improves accuracy over existing methods for quantitative neuroimaging.

Keywords:

Segmentation graph magnetic resonance images (MRI)random forest subcortical structure

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Area of Science:

Neuroimaging
Medical Image Analysis
Machine Learning

Background:

Automated segmentation of subcortical structures in human brain MRI is crucial for quantitative neuroimaging.
Challenges include poor boundary contrast and variable anatomical shapes, hindering accurate segmentation.
Existing methods often struggle with robustness and precision.

Purpose of the Study:

To introduce LOGISMOS-RF, a 3D graph-based machine learning method for accurate and robust segmentation of the caudate and putamen.
To improve volumetric and shape analyses in neuroimaging studies.
To outperform current state-of-the-art segmentation techniques.

Main Methods:

An atlas-based tissue classification and bias-field correction were applied for initial segmentation.
A 3D graph framework constructed a geometric graph for each initial segmentation.
A locally trained random forest classifier and max-flow algorithm were employed for segmentation.

Main Results:

LOGISMOS-RF demonstrated statistically significant improvements in accuracy compared to FreeSurfer and FSL.
The method achieved high Dice overlap coefficients for both caudate (0.89 ± 0.03) and putamen (0.89 ± 0.03).
Surface-to-surface distances also indicated superior performance.

Conclusions:

LOGISMOS-RF offers a robust and accurate solution for segmenting subcortical structures in brain MRI.
The method shows significant potential for advancing quantitative neuroimaging analyses.
This approach surpasses existing methods in segmenting the caudate and putamen.