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Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling.

ABSTRACT: Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data.

SUBMITTER: Puonti O

PROVIDER: S-EPMC8117726 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling.

Puonti Oula O Iglesias Juan Eugenio JE Van Leemput Koen K

NeuroImage 20160907

Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four differe ...[more]

PMID: 27612647

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