ArticleNeuroImage2015
Validated automatic brain extraction of head CT images.
Article in NeuroImage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed, 94 citations in OpenAlex.
- 3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials.Neuroinformatics · 2021Trial
- PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT.NeuroImage. Clinical · 2017Trial
- Whole-brain histogram analysis and top 20% gray and white matter ratio of amyloid positron emission tomography: A comparison with the centiloid scale.Annals of nuclear medicine · 2026Article
- Data-centric strategies enable cross-center generalization of convolutional neural networks for CT brain extraction.Frontiers in neurology · 2026Article
- Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM).NPJ digital medicine · 2025Article
- Contrast quality control for segmentation task based on deep learning models-Application to stroke lesion in CT imaging.Frontiers in neurology · 2025Article
- Deep learning biomarker of chronometric and biological ischemic stroke lesion age from unenhanced CT.NPJ digital medicine · 2024Article
- Intravoxel incoherent motion imaging in stroke infarct core and penumbra is related to long-term clinical outcome.Scientific reports · 2024Article
- Evolution of Severe Closed Head Injury: Assessing Ventricular Volume and Behavioral Measures at 30 and 90 Days Post-Injury.Journal of clinical medicine · 2024Article
- Article
- Deep Learning-Based Extraction of Biomarkers for the Prediction of the Functional Outcome of Ischemic Stroke Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Latent Transformer Models for out-of-distribution detection.Medical image analysis · 2023Article
- A fast and accurate brain extraction method for CT head images.BMC medical imaging · 2023Article
- Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.International journal of computer assisted radiology and surgery · 2023Article
- SELF-SUPERVISED LEARNING WITH RADIOLOGY REPORTS, A COMPARATIVE ANALYSIS OF STRATEGIES FOR LARGE VESSEL OCCLUSION AND BRAIN CTA IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Article
- Dataset on acute stroke risk stratification from CT angiographic radiomics.Data in brief · 2022Article
- [EJNMMI research · 2022Article
- Machine learning-based automatic estimation of cortical atrophy using brain computed tomography images.Scientific reports · 2022Article
- Stroke population-specific neuroanatomical CT-MRI brain atlas.Neuroradiology · 2022Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundX-ray computed tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction - the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated.
aimTo systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported.
methodsAll images were thresholded using a 0-100 Hounsfield unit (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ=1mm(3)) and re-thresholded to 0-100HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: ICVautomated/ICVmanual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and an intraclass correlation (ICC) was estimated using a one-way ANOVA.
resultsSmoothing images improves brain extraction results using BET for all measures except specificity (all p<0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35. Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014, p<0.001), accuracy (0.9971 vs. 0.9971; median difference: 0.0001, p<0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p<0.001) and lower specificity (0.9981 vs. 0.9982; median difference: -0.0001, p<0.001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0.01, the mean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929, 95% CI: 0.91, 0.945) for successfully extracted brains.
conclusionBET performs well at brain extraction on thresholded, 1mm(3) smoothed CT images with an FI of 0.01 or 0.1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided.
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