TY  - JOUR
AU  - Laura Igual
AU  - Joan Carles Soliva
AU  - Antonio Hernandez
AU  - Sergio Escalera
AU  - Xavier Jimenez
AU  - Oscar Vilarroya
AU  - Petia Radeva
PY  - 2011//
TI  - A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder
T2  - BEO
JO  - BioMedical Engineering Online
SP  - 1
EP  - 23
VL  - 10
IS  - 105
KW  - Brain caudate nucleus
KW  - segmentation
KW  - MRI
KW  - atlas-based strategy
KW  - Graph Cut framework
N2  - 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.
SN  - 1475-925X
UR  - http://dx.doi.org/10.1186/1475-925X-10-105
N1  - MILAB;HuPBA
ID  - Laura Igual2011
ER  -