Automated CT-based segmentation and quantification of total intracranial volume

• Objectives To develop an algorithm to segment and obtain an estimate of total intracranial volume (tICV) from computed tomography (CT) images. Materials and methods Thirty-six CT examinations from 18 patients were included. Ten patients were examined twice the same day and eight patients twice six months apart (these patients also underwent MRI). The algorithm combines morphological operations, intensity thresholding and mixture modelling. The method was validated against manual delineation and its robustness assessed from repeated imaging examinations. Using automated MRI software, the comparability with MRI was investigated. Volumes were compared based on average relative volume differences and their magnitudes; agreement was shown by a Bland-Altman analysis graph. Results We observed good agreement between our algorithm and manual delineation of a trained radiologist: the Pearson's correlation coefficient was r = 0.94, tICVml[manual] = 1.05 x tICVml[automated] - 33.78 (R-2 = 0.88). Bland-Altman analysis showed a bias of 31 mL and a standard deviation of 30 mL over a range of 1265 to 1526 mL. Conclusions tICV measurements derived from CT using our proposed algorithm have shown to be reliable and consistent compared to manual delineation. However, it appears difficult to directly compare tICV measures between CT and MRI.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)

Keyword

Total intracranial volume

Computed tomography

Skull stripping

Maximum likelihood estimator

alzheimers-disease

computed-tomography

brain-development

old-age

mri

dementia

images

robust

size

fsl

Radiology

Nuclear Medicine & Medical Imaging

Computed tomography; Magnetic resonance imaging; Maximum likelihood estimator; Skull stripping; Total intracranial volume

Publication and Content Type

ref (subject category)

art (subject category)