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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Automatic detection of brain contours in MRI data sets.

M E Brummer¹, R M Mersereau, R L Eisner

¹Dept. of Radiol., Emory Univ. Sch. of Med., Atlanta, GA.

IEEE Transactions on Medical Imaging

|January 1, 1993

Summary

This study introduces automated software for detecting brain contours in 3D MRI scans. The novel method uses hierarchical processing and anatomical knowledge for accurate brain segmentation.

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Area of Science:

Medical Imaging
Neuroscience
Computer Science

Background:

Accurate brain contour detection is crucial for neuroimaging analysis.
Manual segmentation of 3D MRI data is time-consuming and prone to inter-observer variability.
Automated methods are needed to improve efficiency and reproducibility in brain imaging studies.

Purpose of the Study:

To develop and present a fully automated software procedure for detecting brain contours from 3D MRI data.
To validate the software's performance on clinical datasets.
To explore the generalizability of the automated segmentation technique.

Main Methods:

A hierarchical approach for structure detection in head data volumes.
Initial histogram-based thresholding with optional image intensity correction.
Refinement of binary threshold masks using 2D and 3D morphological operations incorporating anatomical knowledge.
Overlap tests on neighboring slices for coherent 3D brain mask propagation.

Main Results:

Successful automated detection of brain contours from 23 coronal and 1 sagittal whole-brain 3D MRI datasets.
The procedure demonstrated accurate segmentation by effectively discriminating between desired brain structures and undesired elements.
The method showed potential for generalization to other neuroimaging segmentation tasks.

Conclusions:

The presented software procedure offers a fully automated and robust method for brain contour detection in 3D MRI.
The integration of anatomical knowledge within morphological operations enhances segmentation accuracy.
The technique holds promise for broader applications in medical image analysis and neuroscientific research.