Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions

Irimia, Andrei; Maher, Alexander S.; Rostowsky, Kenneth A.; Chowdhury, Nahian F.; Hwang, Darryl; Law, Emma

doi:10.3389/fninf.2019.00009

Cited by 30 publications

(29 citation statements)

References 39 publications

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“…4 However, their method cannot be applied to singletime-point CT images with no image enhancement. Irimia et al 16 adapted SPM12 (http://www.fil.ion.ucl.ac.uk/spm/), 17 a widely used MR imaging-based segmentation method, for CT segmentation and validated it by comparing it with MR images segmented using FreeSurfer (http://surfer.nmr.mgh.harvard.edu). However, they validated only the accuracy of ventricular CSF and not TIV or TBV.…”

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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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“…These are lower than values obtained in a recent study comparing an enhanced segmentation approach based on SPM12 but incorporating neuroanatomy-constrained correction of tissue boundaries based on the local topological properties of the GM/WM interface. 31 Their approach obtains higher DSC for WM and CSF (0.85 and 0.91, respectively), indicating the success of their method, with a similar DSC measured for GM of 0.87. It is worth noting, however, that in order to demonstrate the utility of their method the study authors had to collect data from consented patients and had to exclude poor quality scans.…”

Section: Discussionmentioning

confidence: 65%

“…The objective function assists this process by weighting the probability maps of MNI space according to Bayesian inference principles and then deforming them so that they match the volumes being segmented. 10,31 For this work two sets of a priori tissue probability maps were employed; first, the default maps provided with the SPM12 software were used. Second, compatible maps from a study that created a database of adult, age-specific MRI brain, and head templates were used.…”

Section: B Segmentation Using Spm12mentioning

confidence: 99%

“…30 Another study uses a segmentation protocol based on SPM12 combined with topologically constrained tissue boundary refinement to delineate WM, GM, and cerebrospinal fluid (CSF) and directly assesses accuracy by comparing CT segmentation for MRI segmentation in the same subjects. 31 Before clinical use can be contemplated a method is required to test the accuracy and reproducibility of the entire imaging chain, including the scanner, reconstruction, and the software for segmentation and quantitative analysis of the brain.…”

Section: Introductionmentioning

confidence: 99%

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Creation of an anthropomorphic CT head phantom for verification of image segmentation

Holmes¹,

Negus²,

Wiltshire³

et al. 2020

Medical Physics

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Purpose: Many methods are available to segment structural magnetic resonance (MR) images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for computed tomography (CT) segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast. Methods: To investigate these issues we have created a three-dimensional (3D) printed brain with realistic Hounsfield unit (HU) values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create stereolithography files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and three suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using two CT scanners, the realism of the phantom was assessed by the measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom. Results: Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (gray matter 32.9-35.8 phantom, 29.9-34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for gray matter). The performance of two scanners with two segmentation methods was compared, with the scanners found to have similar performance and with one segmentation method clearly superior to the other. Conclusion: The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.

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“…Intensity normalization is implemented to account for the potentially confounding effect of intersubject intensity brain differences. 4. The additional rotation of the B matrix in DWI image processing is necessary because resulting orientational information may be affected by the increased head motion which is often associated with DWI, given this modality's relatively long acquisition times [21].…”

Section: Notesmentioning

confidence: 99%

Multimodal imaging of cerebral microhemorrhages and white matter degradation in geriatric patients with mild traumatic brain injury

Calvillo¹,

Fan²,

Irimia³

2020

Preprint

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Traditionally, neurobiologists have utilized microscale techniques of scientific investigation to uncover the fundamental organization and function of brain cells and neuronal ensembles. In recent decades, however, macroscale brain imaging methods like magnetic resonance imaging (MRI) and computed tomography (CT) have facilitated a wider scope of understanding neural structure and function across the lifespan. Thanks to such methods, a broader picture of the relationship between microscale processes—studied by neurobiologists—and macroscale observations—made by clinicians—has emerged. More recently, the vascular component of neurodegeneration has come under renewed scrutiny partly due to increased appreciation of the relationship between neurovascular injury, cardiovascular disease and senescence. Cerebral microbleeds (CMBs) are among the smallest lesions of the cerebrum which can be visualized using MRI to indicate blood-brain barrier (BBB) impairment; as such, this class of hemorrhages are important for the evaluation and macroscale detection of geriatric patients’ microscale pathologies associated with neurovascular disease and/or neurodegeneration. This chapter details a streamlined protocol for MRI/CT multimodal imaging data acquisition, archiving and digital processing, including methods tailored for the analysis of susceptibility-weighted imaging (SWI) and diffusion-weighted imaging (DWI) scans to reveal CMB-related alterations of the human connectome. Efficient and effective MRI/CT methods like ours, when tailored for CMB and connectome analysis, are essential for future progress in this important field of scientific inquiry.

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Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions

Cited by 30 publications

References 39 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

Creation of an anthropomorphic CT head phantom for verification of image segmentation

Multimodal imaging of cerebral microhemorrhages and white matter degradation in geriatric patients with mild traumatic brain injury

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