title: A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder.
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.
  
  
  METHODS
  We present CaudateCut: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Applied Mathematics and Analysis, University of Barcelona (UB), Gran Via de les Corts Catalanes 585, Barcelona 08007, Spain. ligual@maia.ub.es
authors.forename: Laura
authors.initials: L
authors.lastname: Igual
authors.forename: Joan Carles
authors.initials: JC
authors.lastname: Soliva
authors.forename: Antonio
authors.initials: A
authors.lastname: Hernández-Vela
authors.forename: Sergio
authors.initials: S
authors.lastname: Escalera
authors.forename: Xavier
authors.initials: X
authors.lastname: Jiménez
authors.forename: Oscar
authors.initials: O
authors.lastname: Vilarroya
authors.forename: Petia
authors.initials: P
authors.lastname: Radeva
mesh_terms: D000293:Adolescent; D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis / Q000473:pathology*; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology*; D002421:Caudate Nucleus / Q000473:pathology*; D002648:Child; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008962:Models, Theoretical; D015203:Reproducibility of Results