White Matter and Gray Matter Segmentation in 4D Computed Tomography

Abstract Modern Computed Tomography (CT) scanners are capable of acquiring contrast dynamics of the whole brain, adding functional to anatomical information. Soft tissue segmentation is important for subsequent applications such as tissue dependent perfusion analysis and automated detection and quan...

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Autores principales:	Rashindra Manniesing, Marcel T. H. Oei, Luuk J. Oostveen, Jaime Melendez, Ewoud J. Smit, Bram Platel, Clara I. Sánchez, Frederick J. A. Meijer, Mathias Prokop, Bram van Ginneken
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a44cb4e4664d42eabd2483e3275e4a9e
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spelling	oai:doaj.org-article:a44cb4e4664d42eabd2483e3275e4a9e2021-12-02T12:31:47ZWhite Matter and Gray Matter Segmentation in 4D Computed Tomography10.1038/s41598-017-00239-z2045-2322https://doaj.org/article/a44cb4e4664d42eabd2483e3275e4a9e2017-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00239-zhttps://doaj.org/toc/2045-2322Abstract Modern Computed Tomography (CT) scanners are capable of acquiring contrast dynamics of the whole brain, adding functional to anatomical information. Soft tissue segmentation is important for subsequent applications such as tissue dependent perfusion analysis and automated detection and quantification of cerebral pathology. In this work a method is presented to automatically segment white matter (WM) and gray matter (GM) in contrast- enhanced 4D CT images of the brain. The method starts with intracranial segmentation via atlas registration, followed by a refinement using a geodesic active contour with dominating advection term steered by image gradient information, from a 3D temporal average image optimally weighted according to the exposures of the individual time points of the 4D CT acquisition. Next, three groups of voxel features are extracted: intensity, contextual, and temporal. These are used to segment WM and GM with a support vector machine. Performance was assessed using cross validation in a leave-one-patient-out manner on 22 patients. Dice coefficients were 0.81 ± 0.04 and 0.79 ± 0.05, 95% Hausdorff distances were 3.86 ± 1.43 and 3.07 ± 1.72 mm, for WM and GM, respectively. Thus, WM and GM segmentation is feasible in 4D CT with good accuracy.Rashindra ManniesingMarcel T. H. OeiLuuk J. OostveenJaime MelendezEwoud J. SmitBram PlatelClara I. SánchezFrederick J. A. MeijerMathias ProkopBram van GinnekenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rashindra Manniesing Marcel T. H. Oei Luuk J. Oostveen Jaime Melendez Ewoud J. Smit Bram Platel Clara I. Sánchez Frederick J. A. Meijer Mathias Prokop Bram van Ginneken White Matter and Gray Matter Segmentation in 4D Computed Tomography
description	Abstract Modern Computed Tomography (CT) scanners are capable of acquiring contrast dynamics of the whole brain, adding functional to anatomical information. Soft tissue segmentation is important for subsequent applications such as tissue dependent perfusion analysis and automated detection and quantification of cerebral pathology. In this work a method is presented to automatically segment white matter (WM) and gray matter (GM) in contrast- enhanced 4D CT images of the brain. The method starts with intracranial segmentation via atlas registration, followed by a refinement using a geodesic active contour with dominating advection term steered by image gradient information, from a 3D temporal average image optimally weighted according to the exposures of the individual time points of the 4D CT acquisition. Next, three groups of voxel features are extracted: intensity, contextual, and temporal. These are used to segment WM and GM with a support vector machine. Performance was assessed using cross validation in a leave-one-patient-out manner on 22 patients. Dice coefficients were 0.81 ± 0.04 and 0.79 ± 0.05, 95% Hausdorff distances were 3.86 ± 1.43 and 3.07 ± 1.72 mm, for WM and GM, respectively. Thus, WM and GM segmentation is feasible in 4D CT with good accuracy.
format	article
author	Rashindra Manniesing Marcel T. H. Oei Luuk J. Oostveen Jaime Melendez Ewoud J. Smit Bram Platel Clara I. Sánchez Frederick J. A. Meijer Mathias Prokop Bram van Ginneken
author_facet	Rashindra Manniesing Marcel T. H. Oei Luuk J. Oostveen Jaime Melendez Ewoud J. Smit Bram Platel Clara I. Sánchez Frederick J. A. Meijer Mathias Prokop Bram van Ginneken
author_sort	Rashindra Manniesing
title	White Matter and Gray Matter Segmentation in 4D Computed Tomography
title_short	White Matter and Gray Matter Segmentation in 4D Computed Tomography
title_full	White Matter and Gray Matter Segmentation in 4D Computed Tomography
title_fullStr	White Matter and Gray Matter Segmentation in 4D Computed Tomography
title_full_unstemmed	White Matter and Gray Matter Segmentation in 4D Computed Tomography
title_sort	white matter and gray matter segmentation in 4d computed tomography
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/a44cb4e4664d42eabd2483e3275e4a9e
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White Matter and Gray Matter Segmentation in 4D Computed Tomography

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