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A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder.

Igual L, Soliva JC, Hernández-Vela A, Escalera S, Jiménez X, Vilarroya O, Radeva P - Biomed Eng Online (2011)

Bottom Line: 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.Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%.

View Article: PubMed Central - HTML - PubMed

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

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 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.

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.

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Related in: MedlinePlus

Flowchart of the atlas-based segmentation approach. Flowchart of the atlas-based segmentation approach used in this work.
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Related In: Results  -  Collection

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Figure 2: Flowchart of the atlas-based segmentation approach. Flowchart of the atlas-based segmentation approach used in this work.

Mentions: In this work, the atlas-based segmentation of the caudate largely follows the strategy proposed by [18]. The main steps in the algorithm are illustrated in Figure 2 and described thus:


A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder.

Igual L, Soliva JC, Hernández-Vela A, Escalera S, Jiménez X, Vilarroya O, Radeva P - Biomed Eng Online (2011)

Flowchart of the atlas-based segmentation approach. Flowchart of the atlas-based segmentation approach used in this work.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3252254&req=5

Figure 2: Flowchart of the atlas-based segmentation approach. Flowchart of the atlas-based segmentation approach used in this work.
Mentions: In this work, the atlas-based segmentation of the caudate largely follows the strategy proposed by [18]. The main steps in the algorithm are illustrated in Figure 2 and described thus:

Bottom Line: 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.Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%.

View Article: PubMed Central - HTML - PubMed

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

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 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.

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.

Show MeSH
Related in: MedlinePlus