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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 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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Manniesing R,Oei MTH,Oostveen LJ,Melendez J,Smit EJ,Platel B,Sánchez CI,Meijer FJA,Prokop M,van Ginneken B

doi

10.1038/s41598-017-00239-z

subject

Has Abstract

pub_date

2017-03-09 00:00:00

pages

119

issue

1

issn

2045-2322

pii

10.1038/s41598-017-00239-z

journal_volume

7

pub_type

杂志文章

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