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Magn Reson Imaging. 2015 May;33(4):474-84. doi: 10.1016/j.mri.2015.02.005. Epub 2015 Feb 7.

NABS: non-local automatic brain hemisphere segmentation.

Author information

1
Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain. Electronic address: josromgom@gmail.com.
2
Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
3
Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Spain.
4
Laboratoire Bordelais de Recherche en Informatique, Unité Mixte de Recherche CNRS (UMR 5800), PICTURA Research Group, 351, cours de la Libération F-33405 Talence cedex, France.

Abstract

In this paper, we propose an automatic method to segment the five main brain sub-regions (i.e. left/right hemispheres, left/right cerebellum and brainstem) from magnetic resonance images. The proposed method uses a library of pre-labeled brain images in a stereotactic space in combination with a non-local label fusion scheme for segmentation. The main novelty of the proposed method is the use of a multi-label block-wise label fusion strategy specifically designed to deal with the classification of main brain sub-volumes that process only specific parts of the brain images significantly reducing the computational burden. The proposed method has been quantitatively evaluated against manual segmentations. The evaluation showed that the proposed method was faster while producing more accurate segmentations than a current state-of-the-art method. We also present evidences suggesting that the proposed method was more robust against brain pathologies than the compared method. Finally, we demonstrate the clinical value of our method compared to the state-of-the-art approach in terms of the asymmetry quantification in Alzheimer's disease.

KEYWORDS:

Asymmetry; Brain segmentation; Brain volume analysis; MRI; Patch-based segmentation

PMID:
25660644
DOI:
10.1016/j.mri.2015.02.005
[Indexed for MEDLINE]
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