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

Content Provider	PubMed Central
Author	Tang, Xiaoying Crocetti, Deana Kwame, Kutten Can, Ceritoglu Albert, Marilyn S. Mori, Susumu Mostofsky, Stewart H. Miller, Michael I.
Copyright Year	2015
Abstract	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.
Related Links	http://dx.doi.org/10.3389/fnins.2015.00061
Starting Page	61
File Format	PDF
ISSN	1662453X
e-ISSN	1662453X
Journal	Frontiers in Neuroscience
Volume Number	9
Language	English
Publisher	Frontiers Media S.A.
Publisher Date	2015-03-01
Access Restriction	Open
Rights Holder	Frontiers Media S.A.
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Neuroscience

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