ArticleFrontiers in neuroscience2021
Convolutional Neural Net Learning Can Achieve Production-Level Brain Segmentation in Structural Magnetic Resonance Imaging.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
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- Association between vascular risk factors and white matter integrity among diverse predominantly older Hispanic/Latino adults from the SOL-INCA MRI study.GeroScience · 2026Article
- Depressive symptoms and neuroimaging markers of brain aging in an ethno-racially diverse sample: a Bayesian analysis.American journal of epidemiology · 2026Article
- Neighborhood greenspace and brain imaging outcomes in older adults without dementia from three US Alzheimer's Disease Research Centers.Health & place · 2026Article
- Free water, occipital volume and changes in depressive symptoms in the LifeAfter90 study.Scientific reports · 2026Article
- Peripheral vascular function, including endothelium-dependent measures, and dementia risk: The Framingham Heart Study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Diabetes, hyperglycemia, and brain MRI biomarkers: results from SOL-INCA MRI study.Nutrition & diabetes · 2026Article
- Circulating C-reactive protein influences polygenic risk of inflammatory genes expressed in brain endothelia for Alzheimer's disease.Neurobiology of disease · 2026Article
- Enlarged Perivascular Spaces Among Hispanic and Latino Adults in SOL-INCA-MRI.Journal of the American Heart Association · 2026Article
- Oxygen extraction fraction mapping between normal cognition and mild cognitive impairment in older adults using quantitative BOLD.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Domain-specific changes in everyday cognition: Associations with diagnosis change and gray matter volume change.Neuropsychology · 2026Article
- Hearing Loss, Brain Structure, Cognition, and Dementia Risk in the Framingham Heart Study.JAMA network open · 2025Article
- Does education promote resilience and brain health in those with exceptional longevity? The LifeAfter90 study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Genetic Architecture of Cerebral White Matter Hyperintensities in Diverse Hispanic/Latino Adults.Neurology. Genetics · 2025Article
- Relation of MRI visible perivascular spaces with global and regional brain structural connectivity measures: The Framingham Heart Study (FHS).Neurobiology of aging · 2025Article
- Depressive symptoms are associated with hippocampal volume in the oldest-old: The LifeAfter90 study.Psychiatry research. Neuroimaging · 2025Article
- Positive Childhood Experiences, Cognition, and Biomarkers of Alzheimer's Disease.International journal of environmental research and public health · 2025Article
- Sex-Specific Vulnerabilities to Subclinical Vascular Brain Injury in Early Late-Life: The Framingham Heart Study.Annals of neurology · 2025Article
- Disentangling the contributions of cerebrovascular-related white matter integrity markers to cognitive aging.Cerebral circulation - cognition and behavior · 2025Article
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4 authors.
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Abstract
Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as "production-level" because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.
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