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Performing label-fusion-based segmentation using multiple automatically generated templates.

M Mallar Chakravarty¹, Patrick Steadman, Matthijs C van Eede

¹Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada; Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Canada.

Human Brain Mapping

|May 22, 2012

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This summary is machine-generated.

This study introduces MAGeT Brain, an automated method for brain segmentation using multiple atlases, improving accuracy in mouse and human neuroimaging compared to traditional techniques.

Area of Science:

Neuroimaging
Computational Neuroscience
Medical Image Analysis

Background:

Model-based segmentation of magnetic resonance imaging (MRI) volumes traditionally relies on nonlinear registration to labeled atlases, facing limitations from atlas biases and registration errors.
Multi-atlas approaches enhance segmentation by fusing multiple independent segmentations derived from various templates.

Purpose of the Study:

To extend the multi-atlas segmentation approach by developing MAGeT Brain (Multiple Automatically Generated Templates Brain), a method that utilizes a library of automatically generated templates.
To evaluate the efficacy of MAGeT Brain for segmenting mouse and human brain structures using different nonlinear registration algorithms.

Main Methods:

MAGeT Brain generates multiple automatically derived brain templates to overcome the limitations of unique, time-consuming manual atlases.

Keywords:

atlases globus pallidus label-fusion mouse imaging multi-atlas nonlinear registration segmentation small animal imaging striatum subcortical anatomy thalamus

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The method was tested on high-resolution mouse brain atlases and human basal ganglia/thalamus atlases derived from serial histology.

Segmentation accuracy was assessed using nonlinear registration algorithms ANIMAL and ANTs, with evaluation metrics including Dice Kappa values.

Main Results:

MAGeT Brain improved segmentation of the mouse anterior commissure (κ = 0.801) but showed a potential ceiling effect for hippocampal segmentation.
For human subcortical structures, MAGeT Brain enhanced segmentation accuracy across all tested regions (striatum, globus pallidus, thalamus) compared to standard model-based methods (κ = 0.845, 0.752, 0.861).
When using three manually derived input templates, MAGeT Brain achieved accuracy comparable to or exceeding standard multi-atlas label-fusion segmentation (κ = 0.894, 0.815, 0.895).

Conclusions:

MAGeT Brain offers an effective extension of the multi-atlas segmentation paradigm by leveraging automatically generated templates.
The method demonstrates improved segmentation accuracy for specific mouse and human brain structures, presenting a valuable tool for neuroimaging analysis.
Further investigation may be warranted to address potential ceiling effects observed in certain structures like the hippocampus.