ArticleEuropean radiology2015
Automated CT-based segmentation and quantification of total intracranial volume.
Article in European radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Computed tomography-based nnU-Net for region-specific brain structural changes across the alzheimer's continuum and frontotemporal dementia subtypes.Scientific reports · 2025Article
- Determination of Species-Specific Differences in Intracranial Volume of Tuj Sheep and Hair Goats Using Stereology and Computed Tomography Methods.Veterinary medicine and science · 2024Article
- CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Machine learning-based automatic estimation of cortical atrophy using brain computed tomography images.Scientific reports · 2022Article
- Generalizing deep learning brain segmentation for skull removal and intracranial measurements.Magnetic resonance imaging · 2022Article
- Fully automated pelvic bone segmentation in multiparameteric MRI using a 3D convolutional neural network.Insights into 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
- 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
- A preclinical micro-computed tomography database including 3D whole body organ segmentations.Scientific data · 2018Article
- Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion.Human brain mapping · 2017Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesTo develop an algorithm to segment and obtain an estimate of total intracranial volume (tICV) from computed tomography (CT) images. MATERIALS AND
methodsThirty-six CT examinations from 18 patients were included. Ten patients were examined twice the same day and eight patients twice six months apart (these patients also underwent MRI). The algorithm combines morphological operations, intensity thresholding and mixture modelling. The method was validated against manual delineation and its robustness assessed from repeated imaging examinations. Using automated MRI software, the comparability with MRI was investigated. Volumes were compared based on average relative volume differences and their magnitudes; agreement was shown by a Bland-Altman analysis graph.
resultsWe observed good agreement between our algorithm and manual delineation of a trained radiologist: the Pearson's correlation coefficient was r = 0.94, tICVml[manual] = 1.05 × tICVml[automated] - 33.78 (R(2) = 0.88). Bland-Altman analysis showed a bias of 31 mL and a standard deviation of 30 mL over a range of 1265 to 1526 mL.
conclusionstICV measurements derived from CT using our proposed algorithm have shown to be reliable and consistent compared to manual delineation. However, it appears difficult to directly compare tICV measures between CT and MRI. KEY POINTS: • Automated estimation of tICV is in good agreement with manual tracing. • Consistent tICV estimations from repeated measurements demonstrate the robustness of the algorithm. • Automatically segmented volumes seem less variable than those from manual tracing. • Unbiased and automated tlCV estimation is possible from CT.
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