ArticleHuman brain mapping2020
Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- Fast surface reconstruction of human brain MRI: benchmarking deep-learning based morphometry tools.Scientific reports · 2026Article
- Mitigating the Impact of MR Sequence Parameters: Increasing the Robustness of DL-Based Cortical Thickness Estimates.Human brain mapping · 2026Article
- Novel epigenetic loci identified from an epigenome-wide association study underlying brain structural changes in bipolar disorder.Psychological medicine · 2026Article
- Preeclampsia as a reversible risk factor for Alzheimer's disease: A prospective MRI study on morphological changes of the cerebral cortex and impairment of cognitive functions.The journal of prevention of Alzheimer's disease · 2026Article
- Contusions bias cortical thickness estimates after traumatic brain injury: A TRACK-TBI study.NeuroImage. Clinical · 2026Article
- Field strength-dependent sensitivity of chemical exchange saturation transfer: a methodological comparison of 3 Tesla and 7 Tesla in a clinical cohort.Brain communications · 2026Article
- Resources are associated with functional outcome and brain morphometry in childhood cancer survivors.Frontiers in oncology · 2026Article
- BrainAgeMS: protocol for a prospective comparative study of brain aging in healthy adults and people with multiple sclerosis.Frontiers in neurology · 2026Observational
- Multimodal Self-Supervised Learning for Early Alzheimer's: Cross-Modal MRI-PET, Longitudinal Signals, and Site Invariance.Diagnostics (Basel, Switzerland) · 2025Article
- Leptomeningeal metastases at recurrence in IDH-wildtype glioblastomas: incidence, risk factors, and prognosis based on postcontrast FLAIR imaging.European radiology · 2025Article
- Alterations of perfusion and functional connectivity of the cingulate motor area are associated with psychomotor retardation in major depressive disorder.European archives of psychiatry and clinical neuroscience · 2025Article
- Application of improved graph convolutional network for cortical surface parcellation.Scientific reports · 2025Article
- Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.Chinese medical journal · 2025Review
- Stimulus-induced rotary saturation imaging of visually evoked response: A pilot study.NMR in biomedicine · 2025Article
- Machine learning-based reconstruction of 2D MRI for quantitative morphometry in epilepsy.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Article
- Impact of amyloid and tau positivity on longitudinal brain atrophy in cognitively normal individuals.Alzheimer's research & therapy · 2024Article
- Three-dimensional infrared scanning: an enhanced approach for spatial registration of probes for neuroimaging.Neurophotonics · 2024Article
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5 authors.
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Abstract
Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique to derive such measures from non-surface-based volumetric tissue maps. ANTs provides an open-source method for estimating cortical thickness, derived by applying DiReCT to an atlas-based segmentation. In this paper, we propose DL+DiReCT, a method using high-quality deep learning-based neuroanatomy segmentations followed by DiReCT, yielding accurate and reliable cortical thickness measures in a short time. We evaluate the methods on two independent datasets and compare the results against surface-based measures from FreeSurfer. Good correlation of DL+DiReCT with FreeSurfer was observed (r = .887) for global mean cortical thickness compared to ANTs versus FreeSurfer (r = .608). Experiments suggest that both DiReCT-based methods had higher sensitivity to changes in cortical thickness than Freesurfer. However, while ANTs showed low scan-rescan robustness, DL+DiReCT showed similar robustness to Freesurfer. Effect-sizes for group-wise differences of healthy controls compared to individuals with dementia were highest with the deep learning-based segmentation. DL+DiReCT is a promising combination of a deep learning-based method with a traditional registration technique to detect subtle changes in cortical thickness.
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