ArticleCerebral cortex (New York, N.Y. : 1991)2024
Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T ex vivo MRI of the human cerebral cortex.
Article in Cerebral cortex (New York, N.Y. : 1991), 2024. 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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10 citing papers in PubMed.
- Multilayer MEG source modelling enables depth-resolved laminar inference in humans.Nature communications · 2026Article
- Multi-dimensional diffusion MRI at ultra-high gradient strength for mapping axonal architecture and microstructure in the primate brain.bioRxiv : the preprint server for biology · 2026Article
- Visualizing cortical laminar architecture in the living human brain using next-generation ultra-high-gradient diffusion MRI.Communications biology · 2026Article
- Myelinated fiber labeling and orientation mapping of the human brain with light-sheet fluorescence microscopy.NeuroImage · 2025Article
- Article
- Visualizing cortical laminar architecture in the living human brain using next-generation ultra-high-gradient diffusion MRI.Research square · 2025Article
- Operationalizing postmortem pathology-MRI association studies in Alzheimer's disease and related disorders with MRI-guided histology sampling.Acta neuropathologica communications · 2025Article
- Layer-Dependent Effect of Aβ-Pathology on Cortical Microstructure With Ex Vivo Human Brain Diffusion MRI at 7 Tesla.Human brain mapping · 2025Article
- A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.Neuroinformatics · 2024Article
- Automated deep learning segmentation of high-resolution 7 Tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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23 authors.
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Abstract
Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Building on recent advancements in ultra-high-resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 $\mu $m, we propose a Multi-resolution U-Nets framework that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.