ArticleNeuroinformatics2021
Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs.
Article in Neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- segcsvdHuman brain mapping · 2026Article
- Assessing CT-based volumetric analysis via deep learning for idiopathic normal pressure hydrocephalus.Brain communications · 2026Article
- A generalizable 3D framework and model for self-supervised learning in medical imaging.NPJ digital medicine · 2025Article
- Regional free-water diffusion is more strongly related to neuroinflammation than neurodegeneration.Journal of neurology · 2025Article
- Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients.Cancers · 2025Article
- Examining the association between sleep apnea and total hippocampal volumes in cognitive impairment.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- segcsvdHuman brain mapping · 2024Article
- Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity.Communications medicine · 2024Article
- Tau follows principal axes of functional and structural brain organization in Alzheimer's disease.Nature communications · 2024Article
- Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images.Radiological physics and technology · 2023Article
- Distinct brain morphometry patterns revealed by deep learning improve prediction of aphasia severity.Research square · 2023Article
- Amyloid-PET of the white matter: Relationship to free water, fiber integrity, and cognition in patients with dementia and small vessel disease.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2023Article
- Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg.Frontiers in neurology · 2023Article
- Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation.Human brain mapping · 2022Article
- AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus.Neural computing & applications · 2022Article
- A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain.PloS one · 2022Article
- Intracranial volume segmentation for neurodegenerative populations using multicentre FLAIR MRI.Neuroimage. Reports · 2021Article
- Application of Evans Index in Normal Pressure Hydrocephalus Patients: A Mini Review.Frontiers in aging neuroscience · 2021Review
- Brain atrophy trajectories predict differential functional performance in Alzheimer's disease: Moderations with apolipoprotein E and sex.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
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Authors and funding
21 authors.
Funding
Abstract
Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural network (CNN) to segment the ICV and ventricles from both single and multi-contrast MRI data. Our models were trained on a large dataset from two multi-site studies (N = 528 subjects for ICV, N = 501 for ventricular segmentation) consisting of older adults with varying degrees of cerebrovascular lesions and atrophy, which pose significant challenges for most segmentation approaches. The models were tested on 238 participants, including subjects with vascular cognitive impairment and high white matter hyperintensity burden. Two of the three test sets came from studies not used in the training dataset. We assessed our algorithms relative to four state-of-the-art ICV extraction methods (MONSTR, BET, Deep Extraction, FreeSurfer, DeepMedic), as well as two ventricular segmentation tools (FreeSurfer, DeepMedic). Our multi-contrast models outperformed other methods across many of the evaluation metrics, with average Dice coefficients of 0.98 and 0.96 for ICV and ventricular segmentation respectively. Both models were also the most time efficient, segmenting the structures in orders of magnitude faster than some of the other available methods. Our networks showed an increased accuracy with the use of a conditional random field (CRF) as a post-processing step. We further validated both segmentation models, highlighting their robustness to images with lower resolution and signal-to-noise ratio, compared to tested techniques. The pipeline and models are available at: https://icvmapp3r.readthedocs.io and https://ventmapp3r.readthedocs.io to enable further investigation of the roles of ICV and ventricles in relation to normal aging and neurodegeneration in large multi-site studies.
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Registered trials
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