Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/21497
Title: A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder
Author: Igual, Laura
Soliva, Joan Carles
Hernandez-Vela, Antonio
Escalera Guerrero, Sergio
Jimenez, Xavier
Vilarroya Oliver, Óscar
Radeva, Petia
Keywords: Cervell
Imatges per ressonància magnètica
Enginyeria biomèdica
Brain
Magnetic resonance imaging
Biomedical engineering
Issue Date: 17-Jan-2012
Publisher: BioMed Central
Abstract: Background Accurate 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. Method We 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. Results We 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. Conclusion CaudateCut 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.
Note: Reproducció del document publicat a: http://dx.doi.org/10.1186/1475-925X-10-105
It is part of: BioMedical Engineering OnLine 2011, 10:105
URI: http://hdl.handle.net/2445/21497
ISSN: 1475-925X
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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