Performing label-fusion-based segmentation using multiple automatically generated templates
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Open Access
- 19 May 2012
- journal article
- research article
- Published by Wiley in Human Brain Mapping
- Vol. 34 (10), 2635-2654
- https://doi.org/10.1002/hbm.22092
Abstract
Classically, model‐based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi‐atlas‐based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel‐by‐voxel label‐voting procedure. In this article, we demonstrate how the multi‐atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high‐resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model‐based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi‐atlas label‐fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively). Hum Brain Mapp 34:2635–2654, 2013.Keywords
This publication has 70 references indexed in Scilit:
- BEaST: Brain extraction based on nonlocal segmentation techniqueNeuroImage, 2012
- A Bayesian model of shape and appearance for subcortical brain segmentationNeuroImage, 2011
- A reproducible evaluation of ANTs similarity metric performance in brain image registrationNeuroImage, 2011
- Cortical Development in Typically Developing Children With Symptoms of Hyperactivity and Impulsivity: Support for a Dimensional View of Attention Deficit Hyperactivity DisorderAmerican Journal of Psychiatry, 2011
- LEAP: Learning embeddings for atlas propagationNeuroImage, 2010
- Automated segmentation of mouse brain images using extended MRFNeuroImage, 2009
- Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registrationNeuroImage, 2009
- Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brainMedical Image Analysis, 2008
- Construction of a 3D probabilistic atlas of human cortical structuresNeuroImage, 2008
- Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturationProceedings of the National Academy of Sciences of the United States of America, 2007