Automated Segmentation of Head Computed Tomography Images Using FSL.

Abstract:

OBJECTIVE:The aim of this study was to investigate the use of one magnetic resonance image-processing tool, FSL, in its ability to perform automated segmentation of computed tomographic images of the brain. METHODS:Head computed tomography (CT) images were brain extracted and segmented using the FSL tools BET and FAST, respectively. The products of segmentation were analyzed by histogram. The impact of image intensity inhomogeneity correction was investigated using simulated bias fields, 14 routine head CT scans, and selected illustrative clinical cases. RESULTS:FSL FAST performs direct segmentation of head CT images, permitting quantitation of gray and white matter densities and volumes, achieving a more complete segmentation than masking methods. "Bias field correction" reduced the covariance of image signal intensities of the total brain and gray matter images (P < 0.01). Correction is larger when the effects of beam hardening and radiation scatter are larger, resulting in improved segmentation. CONCLUSIONS:FSL FAST enables direct segmentation of head CT images.

journal_name

J Comput Assist Tomogr

authors

Cauley KA,Och J,Yorks PJ,Fielden SW

doi

10.1097/RCT.0000000000000660

subject

Has Abstract

pub_date

2018-01-01 00:00:00

pages

104-110

issue

1

eissn

0363-8715

issn

1532-3145

journal_volume

42

pub_type

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