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Self-Trained Supervised Segmentation of Subcortical Brain Structures Using Multispectral Magnetic Resonance Images
Michele Larobina1, Loredana Murino2, Amedeo Cervo3
1Istituto di Biostrutture e Bioimmagini, CNR, Via Tommaso De Amicis 95, 80145 Napoli, Italy.
Biomed Research International
|November 20, 2015
Summary
This study demonstrates a new automated method for training supervised learning models to segment subcortical brain structures in MRI scans. This approach eliminates the need for manual operator intervention, improving efficiency in neuroimaging analysis.
Area of Science:
- Neuroimaging
- Machine Learning
- Medical Image Analysis
Background:
- Supervised classification methods for brain structure segmentation require extensive manual training data.
- Operator intervention for training data selection limits the widespread adoption of these methods.
Purpose of the Study:
- To investigate the feasibility of automatically training supervised methods for segmenting subcortical brain structures.
- To evaluate atlas-guided training for k-nearest neighbor (kNN) and principal component discriminant analysis (PCDA) classifiers.
Main Methods:
- Automated training data selection using probabilistic atlas registration.
- Utilized voxel intensities and spatial coordinates for kNN and PCDA classifiers.
- Evaluated on 20 multispectral magnetic resonance imaging datasets.
Main Results:
- Atlas-guided training effectively creates representative and reliable datasets automatically.
- Achieved successful segmentation of caudate, thalamus, pallidum, and putamen.
- Demonstrated the potential for fully automated supervised segmentation of brain images.
Conclusions:
- Automated atlas-guided training enables supervised methods for brain MRI segmentation without user interaction.
- This approach enhances the feasibility and efficiency of neuroimaging analysis.
- Paves the way for wider application of automated segmentation techniques.
