Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles

We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF). In MALF, esti...

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Vydané v:	Frontiers in neuroscience Ročník 9; s. 61
Hlavní autori:	Tang, Xiaoying, Crocetti, Deana, Kutten, Kwame, Ceritoglu, Can, Albert, Marilyn S., Mori, Susumu, Mostofsky, Stewart H., Miller, Michael I.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Research Foundation 03.03.2015 Frontiers Media S.A
Predmet:	Accuracy Algorithms Alzheimer's disease automated brain segmentation Automation Bayesian analysis Brain research Datasets Dementia disorders Developmental disabilities Geriatrics Image processing likelihood-fusion Magnetic resonance imaging Medicine multi-atlas Neurodevelopmental disorders Neuroimaging Neuroscience Parameter estimation Pediatrics Random variables Registration Segmentation Skull skull-stripping subcortical structures Ventricle United States > US Baltimore Maryland ventricle multi-atlas automated brain segmentation skull-stripping likelihood-fusion subcortical structures
ISSN:	1662-453X, 1662-4548, 1662-453X
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Shrnutí:	We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF). In MALF, estimation of the parameter of interest is performed via maximum a posteriori estimation using the expectation-maximization (EM) algorithm. The likelihoods of multiple atlases are fused in the E-step while the optimal estimator, a single maximizer of the fused likelihoods, is then obtained in the M-step. There are two stages in the proposed pipeline; first the input T1-weighted image is automatically skull-stripped via a fast MALF, then internal brain structures of interest are automatically extracted using a regular MALF. We assess the performance of each of the two modules in the pipeline based on two sets of images with markedly different anatomical and photometric contrasts; 3T MPRAGE scans of pediatric subjects with developmental disorders vs. 1.5T SPGR scans of elderly subjects with dementia. Evaluation is performed quantitatively using the Dice overlap as well as qualitatively via visual inspections. As a result, we demonstrate subject-level differences in the performance of the proposed pipeline, which may be accounted for by age, diagnosis, or the imaging parameters (particularly the field strength). For the subcortical and ventricular structures of the two datasets, the hierarchical pipeline is capable of producing automated segmentations with Dice overlaps ranging from 0.8 to 0.964 when compared with the gold standard. Comparisons with other representative segmentation algorithms are presented, relative to which the proposed hierarchical pipeline demonstrates comparative or superior accuracy.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Reviewed by: Koen Van Leemput, Harvard Medical School, USA; John Ashburner, University College London Institute of Neurology, UK; Paul Aljabar, King's College London, UK Edited by: John Ashburner, University College London Institute of Neurology, UK This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience.
ISSN:	1662-453X 1662-4548 1662-453X
DOI:	10.3389/fnins.2015.00061