A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit-hyperactivity disorderReport as inadecuate




A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit-hyperactivity disorder - Download this document for free, or read online. Document in PDF available to download.

BioMedical Engineering OnLine

, 10:105

First Online: 05 December 2011Received: 08 August 2011Accepted: 05 December 2011

Abstract

BackgroundAccurate automatic segmentation of the caudate nucleus in magnetic resonance images MRI of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations.

MethodWe present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure.

ResultsWe apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit-hyperactivity disorder ADHD patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis.

ConclusionCaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.

KeywordsBrain caudate nucleus segmentation MRI atlas-based strategy Graph Cut framework Electronic supplementary materialThe online version of this article doi:10.1186-1475-925X-10-105 contains supplementary material, which is available to authorized users.

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Author: Laura Igual - Joan Carles Soliva - Antonio Hernández-Vela - Sergio Escalera - Xavier Jiménez - Oscar Vilarroya - Petia R

Source: https://link.springer.com/



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