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

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Published on: January 7, 2019

AUTOMATIC PARCELLATION OF CORTICAL SURFACES USING RANDOM FORESTS.

Yu Meng¹, Gang Li², Yaozong Gao¹

¹Department of Computer Science, University of North Carolina at Chapel Hill, NC, USA ; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|September 26, 2015

Summary

This study introduces a novel brain mapping method for automatic cortical surface parcellation using random forests and graph cuts. The technique achieves high accuracy in defining gyral-based regions, outperforming existing approaches.

Keywords:

Cortical surface parcellation Haar-like features context feature graph cuts random forests

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Area of Science:

Neuroimaging
Computational Neuroscience
Medical Image Analysis

Background:

Accurate parcellation of cortical surfaces is crucial for brain mapping.
Existing methods for automated brain region identification face challenges in accuracy and consistency.

Purpose of the Study:

To develop an automated method for parcellating cortical surfaces into anatomically and functionally meaningful gyral-based regions.
To improve the accuracy and spatial consistency of brain surface parcellation.

Main Methods:

A novel approach combining random forests and graph cuts for cortical surface parcellation.
Utilizing random forests with auto-context for initial rough parcellation based on cortical features.
Employing graph cuts for refining parcellation accuracy and spatial consistency.
Introducing Haar-like features defined on spherical mappings of cortical surfaces.

Main Results:

The proposed method achieved an average Dice ratio of 0.902 on 39 adult brain MR images.
Demonstrated superior performance compared to state-of-the-art methods in cortical surface parcellation.
Successfully parcellated cortical surfaces into 35 manually labeled regions with high accuracy.

Conclusions:

The proposed random forest and graph cut-based method offers an accurate and automated solution for cortical surface parcellation.
This technique advances brain mapping by providing reliable identification of gyral-based regions.
The novel feature definition and hybrid approach enhance the precision of neuroanatomical region identification.