ArticleAJNR. American journal of neuroradiology2020
A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease.
Article in AJNR. American journal of neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 31 citations in OpenAlex.
- Intracranial volume variation in Chinchorro mummies: a comparative study with pre-hispanic farmers and contemporary Chilean populations.Scientific reports · 2025Article
- Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.Journal of imaging informatics in medicine · 2025Article
- Brain Atrophy Is Associated with Hematoma Expansion in Intracerebral Hemorrhage, Depending on Coagulation Status.Journal of clinical medicine · 2025Article
- Automatic segmentation and quantitative analysis of brain CT volume in 2-year-olds using deep learning model.Frontiers in neurology · 2025Article
- Rapidly progressive brain atrophy in ventilated patients: a retrospective descriptive study.Scientific reports · 2024Article
- Rapidly progressive cerebral atrophy following a posterior cranial fossa stroke: Assessment with semiautomatic CT volumetry.Acta neurochirurgica · 2023Article
- Brain atrophy and endovascular treatment effect in acute ischemic stroke: a secondary analysis of the MR CLEAN trial.International journal of stroke : official journal of the International Stroke Society · 2022Article
- A Comparison of Global Brain Volumetrics Obtained from CT versus MRI Using 2 Publicly Available Software Packages.AJNR. American journal of neuroradiology · 2022Article
- Brain CT can predict low lean mass in the elderly with cognitive impairment: a community-dwelling study.BMC geriatrics · 2022Article
- A novel CT-based automated analysis method provides comparable results with MRI in measuring brain atrophy and white matter lesions.Neuroradiology · 2021Article
- Rapidly progressive brain atrophy in septic ICU patients: a retrospective descriptive study using semiautomatic CT volumetry.Critical care (London, England) · 2021Article
- The effects of the skull on CT imaging of the brain: a skull and brain phantom study.The British journal of radiology · 2021Article
- Pediatric Head CT: Automated Quantitative Analysis with Quantile Regression.AJNR. American journal of neuroradiology · 2021Article
- Severe Encephalatrophy and Related Disorders From Long-Term Ketamine Abuse: A Case Report and Literature Review.Frontiers in psychiatry · 2021Article
- Severe Brain Atrophy Predicts Poor Clinical Outcome After Endovascular Treatment of Acute Basilar Artery Occlusion: An Automated Volumetric Analysis of a Nationwide Registry.Frontiers in aging neuroscience · 2021Article
- Novel multi-linear quantitative brain volume formula for manual radiological evaluation of brain atrophy.European journal of radiology open · 2020Article
- The diagnostic accuracy of CTseg segmentation software for dementia in a New Zealand memory service.Journal of Alzheimer's disease reportsArticle
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeTotal 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
methodsCTseg 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.
resultsCTseg showed excellent agreement with 20 manually segmented head CTs. The intraclass correlation was 0.97 (
conclusionsIn 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.
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