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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

Automated brain structure segmentation based on atlas registration and appearance models.

Fedde van der Lijn¹, Marleen de Bruijne, Stefan Klein

¹Departments of Medical Informatics and Radiology, Erasmus MC, 3000 CA Rotterdam, The Netherlands. f.vanderlijn@erasmusmc.nl

IEEE Transactions on Medical Imaging

|September 23, 2011

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This study introduces a new automated method for brain MRI segmentation, combining spatial and appearance information for accurate results. The novel technique improves robustness and applicability for analyzing brain structures like the hippocampus and cerebellum.

Area of Science:

Neuroimaging
Medical Image Analysis
Computational Neuroscience

Background:

Automated brain structure segmentation is crucial for large-scale neuroimaging studies.
Existing methods may struggle with complex intensity distributions and spatial model errors.

Purpose of the Study:

To develop a novel, robust, and widely applicable automated method for brain structure segmentation in MRI.
To combine spatial and appearance information for improved segmentation accuracy.

Main Methods:

A novel method combining spatial probability maps from atlas registration and a Gaussian scale-space feature-based appearance classifier.
Integration within a Bayesian framework with regularization, optimized using graph cuts.
Validation on two MRI datasets with expert-segmented hippocampus and cerebellum.

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Main Results:

Achieved high accuracy with mean Dice similarity indices of 0.95 for cerebellum and 0.87 for hippocampus.
Demonstrated superior or comparable performance against two other segmentation techniques.
Showcased increased robustness against spatial model errors and ability to segment structures with complex intensity distributions.

Conclusions:

The proposed atlas- and appearance-based segmentation method offers accurate and robust brain structure segmentation.
The technique is more widely applicable and reliable compared to existing methods.
Facilitates advanced analysis in large-scale neuroimaging research.