Decomposing the Hounsfield Unit

Kemmling, André; Wersching, H.; Berger, Klaus; Knecht, Stefan; Groden, Christoph; Nölte, I.

doi:10.1007/s00062-011-0123-0

Cited by 45 publications

(23 citation statements)

References 35 publications

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“…5,9 Methods available for segmenting CT images to measure global volume metrics such as total intracrancial volume (TIV) and TBV from images with no detectable pathology were not formally validated. [10][11][12] Some wellvalidated methods segment only TIV 13,14 but not TBV. However, TBV is more indicative of disease conditions in neurodegenerative dieseases, 15 and TIV is used merely as a nuisance variable for normalization purposes.…”

mentioning

confidence: 99%

A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease

Adduru

Baum

Zhang

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Total brain volume and total intracranial volume are important measures for assessing whole-brain atrophy in Alzheimer disease, dementia, and other neurodegenerative diseases. Unlike MR imaging, which has a number of well-validated fully-automated methods, only a handful of methods segment CT images. Available methods either use enhanced CT, do not estimate both volumes, or require formal validation. Reliable computation of total brain volume and total intracranial volume from CT is needed because head CTs are more widely used than head MRIs in the clinical setting. We present an automated head CT segmentation method (CTseg) to estimate total brain volume and total intracranial volume. MATERIALS AND METHODS: CTseg adapts a widely used brain MR imaging segmentation method from the Statistical Parametric Mapping toolbox using a CT-based template for initial registration. CTseg was tested and validated using head CT images from a clinical archive. RESULTS: CTseg showed excellent agreement with 20 manually segmented head CTs. The intraclass correlation was 0.97 (P , .001) for total intracranial volume and 0.94 (P , .001) for total brain volume. When CTseg was applied to a cross-sectional Alzheimer disease dataset (58 with Alzheimer disease patients and 58 matched controls), CTseg detected a loss in percentage total brain volume (as a percentage of total intracranial volume) with age (P , .001) as well as a group difference between patients with Alzheimer disease and controls (P , .01). We observed similar results when total brain volume was modeled with total intracranial volume as a confounding variable. CONCLUSIONS: In current clinical practice, brain atrophy is assessed by inaccurate and subjective "eyeballing" of CT images. Manual segmentation of head CT images is prohibitively arduous and time-consuming. CTseg can potentially help clinicians to automatically measure total brain volume and detect and track atrophy in neurodegenerative diseases. In addition, CTseg can be applied to large clinical archives for a variety of research studies. ABBREVIATIONS: AD 4 Alzheimer disease; BET 4 Brain Extraction Tool; ICC 4 intraclass correlation coefficient; TBV 4 total brain volume; TIV 4 total intracranial volume; TPM 4 tissue probability map

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mentioning

confidence: 99%

A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease

Adduru

Baum

Zhang

et al. 2020

AJNR Am J Neuroradiol

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“…The images in that study originated from the earliest CT scanners that were commercially available, before the invention of slip-ring technology to enable continuous tube rotation, and before the invention of multi-row detectors and spiral scanning techniques 17 . More recent is the work by Gupta et al 18 and Kemmling et al 19 . In Gupta et al 18 a heuristic rule-based method with adaptive intensity thresholding was proposed to segment CSF, WM and GM.…”

Section: Introductionmentioning

confidence: 98%

“…Development and evaluation of their method was done on the same patient data and their reference standard was manually contoured on high confidence regions only. In Kemmling et al 19 a probabilistic atlas was constructed from pre-segmented MR data and was registered to CT to extract CSF, WM and GM, but no quantitative evaluation was provided. Other methods have focused on segmentation of CSF or ventricles only 20–22 .…”

Section: Introductionmentioning

confidence: 99%

White Matter and Gray Matter Segmentation in 4D Computed Tomography

Manniesing

Oei

Oostveen

et al. 2017

Sci Rep

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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.

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“…To extract information from standardized pc-ASPECTS maps and to reduce potential bias in quantitative texture analysis, all NCCT images were registered to a custom MNI-152 CT reference image [5] using two-step affine algorithms [14]. Registration success was visually verified by two MDs (UH and PS: 8 years of clinical experience in diagnostic neuroradiology in acute care full-service hospitals).…”

Section: Registration To Standard Spacementioning

confidence: 99%

“…Standardized pc-ASPECTS area maps [thalamus left/right (l/r), pons, midbrain, territory of the posterior cerebral artery (PCA) l/r, cerebellum l/r] were derived as follows: First, an experienced neuroradiologist (UH) performed manual segmentations of the respective regions on the original NCCT images of the 63 healthy subjects using Analyze 11.0 Software (Biomedical Imaging Resource, Mayo Clinic, Rochester, MN) [2]. Second, manual segmentations were transformed into standard space by employing transformation matrices and control point grids obtained from image registration to the custom MNI-152 CT reference image [5]. Third, all segmentations were added and final standard maps were defined using median cut-off points.…”

Section: Pc-aspects Mapsmentioning

confidence: 99%

Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans

et al. 2020

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Objectives Triage of patients with basilar artery occlusion for additional imaging diagnostics, therapy planning, and initial outcome prediction requires assessment of early ischemic changes in early hyperacute non-contrast computed tomography (NCCT) scans. However, accuracy of visual evaluation is impaired by inter-and intra-reader variability, artifacts in the posterior fossa and limited sensitivity for subtle density shifts. We propose a machine learning approach for detecting early ischemic changes in pc-ASPECTS regions (Posterior circulation Alberta Stroke Program Early CT Score) based on admission NCCTs. Methods The retrospective study includes 552 pc-ASPECTS regions (144 with infarctions in follow-up NCCTs) extracted from pre-therapeutic early hyperacute scans of 69 patients with basilar artery occlusion that later underwent successful recanalization. We evaluated 1218 quantitative image features utilizing random forest algorithms with fivefold cross-validation for the ability to detect early ischemic changes in hyperacute images that lead to definitive infarctions in follow-up imaging. Classifier performance was compared to conventional readings of two neuroradiologists. Results Receiver operating characteristic area under the curves for detection of early ischemic changes were 0.70 (95% CI [0.64; 0.75]) for cerebellum to 0.82 (95% CI [0.77; 0.86]) for thalamus. Predictive performance of the classifier was significantly higher compared to visual reading for thalamus, midbrain, and pons (P value < 0.05). Conclusions Quantitative features of early hyperacute NCCTs can be used to detect early ischemic changes in pc-ASPECTS regions. The classifier performance was higher or equal to results of human raters. The proposed approach could facilitate reproducible analysis in research and may allow standardized assessments for outcome prediction and therapy planning in clinical routine.

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Decomposing the Hounsfield Unit

Cited by 45 publications

References 35 publications

A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease

A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease

White Matter and Gray Matter Segmentation in 4D Computed Tomography

Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans

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