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

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Published on: June 9, 2018

A novel framework for segmentation of deep brain structures based on Markov dependence tree.

Jue Wu¹, Albert C S Chung

¹Bioengineering Program, The Hong Kong University of Science and Technology, Hong Kong.

|March 17, 2009

Summary

This study introduces a novel framework for segmenting deep brain structures like the caudate nucleus, putamen, and thalamus in brain images. The method achieves high accuracy without manual initialization, aiding neurological research.

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Three-Dimensional Shape Modeling and Analysis of Brain Structures

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Published on: November 14, 2019

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

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Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Area of Science:

Medical Image Analysis
Neuroimaging
Computer Vision

Background:

Deep brain segmentation is challenging due to small structure size, shape variations, and unclear boundaries.
Accurate segmentation of deep brain structures is crucial for neurological studies and clinical applications.

Purpose of the Study:

To develop an automated, template-based framework for multi-object segmentation of deep brain structures.
To improve the accuracy and efficiency of segmenting the caudate nucleus, putamen, and thalamus.

Main Methods:

A template-based framework fusing edge features, region statistics, and inter-structure constraints.
Utilizing a hierarchical Markov dependence tree (MDT) for efficient multi-object matching.
Refinement via B-spline based non-rigid registration using a single training example.

Main Results:

Achieved average Dice scores of 0.80 for caudate nuclei, 0.81 for putamina, and 0.84 for thalami.
Demonstrated successful validation on a public T1-weighted MRI database.
The approach requires only one example for training, reducing data dependency.

Conclusions:

The proposed framework offers an effective and automated solution for deep brain structure segmentation.
This method shows promise for enhancing the analysis of brain imaging data.
The template-based approach with MDT and B-spline registration provides robust segmentation performance.