ArticleClinical neuroradiology2012
Decomposing the Hounsfield unit: probabilistic segmentation of brain tissue in computed tomography.
Article in Clinical neuroradiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Contrast quality control for segmentation task based on deep learning models-Application to stroke lesion in CT imaging.Frontiers in neurology · 2025Article
- Role of Computed Tomography Angiography Enhancement Ratio in Predicting Prognosis in Patients With Stroke Undergoing Endovascular Treatment.Stroke (Hoboken, N.J.) · 2024Article
- Bifurcation matching for consistent cerebral vessel labeling in CTA of stroke patients.International journal of computer assisted radiology and surgery · 2023Article
- Imaging-based outcome prediction in posterior circulation stroke.Journal of neurology · 2022Article
- A Comparison of Global Brain Volumetrics Obtained from CT versus MRI Using 2 Publicly Available Software Packages.AJNR. American journal of neuroradiology · 2022Article
- Feasibility study of portable multi-energy computed tomography with photon-counting detector for preclinical and clinical applications.Scientific reports · 2021Article
- Head CT: Toward Making Full Use of the Information the X-Rays Give.AJNR. American journal of neuroradiology · 2021Review
- On the Use of Virtual Reality for Medical Imaging Visualization.Journal of digital imaging · 2021Article
- Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT.Frontiers in computational neuroscience · 2021Article
- Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans.Journal of neurology · 2020Article
- A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease.AJNR. American journal of neuroradiology · 2020Article
- Article
- Quantitative Rapid Assessment of Leukoaraiosis in CT : Comparison to Gold Standard MRI.Clinical neuroradiology · 2019Article
- Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions.Frontiers in neuroinformatics · 2019Article
- Inverse Perfusion Requirements of Supra- and Infratentorial Brain Metastases Formation.Frontiers in neurology · 2018Article
- Impact of Lesion Load Thresholds on Alberta Stroke Program Early Computed Tomographic Score in Diffusion-Weighted Imaging.Frontiers in neurology · 2018Article
- White Matter and Gray Matter Segmentation in 4D Computed Tomography.Scientific reports · 2017Article
- The Association of Lesion Location and Sleep Related Breathing Disorder in Patients with Acute Ischemic Stroke.PloS one · 2017Article
- Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of this study was to present and evaluate a standardized technique for brain segmentation of cranial computed tomography (CT) using probabilistic partial volume tissue maps based on a database of high resolution T1 magnetic resonance images (MRI).
methodsProbabilistic tissue maps of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) were derived from 600 normal brain MRIs (3.0 Tesla, T1-3D-turbo-field-echo) of 2 large community-based population studies (BiDirect and SEARCH Health studies). After partial tissue segmentation (FAST 4.0), MR images were linearly registered to MNI-152 standard space (FLIRT 5.5) with non-linear refinement (FNIRT 1.0) to obtain non-binary probabilistic volume images for each tissue class which were subsequently used for CT segmentation. From 150 normal cerebral CT scans a customized reference image in standard space was constructed with iterative non-linear registration to MNI-152 space. The inverse warp of tissue-specific probability maps to CT space (MNI-152 to individual CT) was used to decompose a CT image into tissue specific components (GM, WM, CSF).
resultsPotential benefits and utility of this novel approach with regard to unsupervised quantification of CT images and possible visual enhancement are addressed. Illustrative examples of tissue segmentation in different pathological cases including perfusion CT are presented.
conclusionAutomated tissue segmentation of cranial CT images using highly refined tissue probability maps derived from high resolution MR images is feasible. Potential applications include automated quantification of WM in leukoaraiosis, CSF in hydrocephalic patients, GM in neurodegeneration and ischemia and perfusion maps with separate assessment of GM and WM.
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