Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T ex vivo MRI of the human cerebral cortex
Year: 2024
Type: article
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 μm, 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.
Authors: Zeng, Xiangrui, Puonti, Oula, Sayeed, Areej, Herisse, Rogeny, Mora, Jocelyn, Evancic, Kathryn, Varadarajan, Divya, Balbastre, Yael, Costantini, Irene, Scardigli, Marina, Ramazzotti, Josephine, DiMeo, Danila, Mazzamuto, Giacomo, Pesce, Luca, Brady, Niamh, Cheli, Franco, Saverio Pavone, Francesco, Hof, Patrick R, Frost, Robert, Augustinack, Jean, van der Kouwe, Andre, Iglesias, Juan Eugenio, Fischl, Bruce
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Massachusetts Life Sciences Center, Laserlab-Europe, European Commission, Lundbeckfonden, Fondazione Cassa di Risparmio di Firenze, National Institutes of Health, National Institute on Aging, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources
Awards: G106490469, G15294626, Acquisition-independent machine learning for morphometric analysis of underrepresented aging populations with clinical and low-field brain MRI, G2998073318, G3387845073, Segmenting Brain Structures for Neurological Disorders, Inferring in Vivo Cytoarchitectural Borders in the Medial Temporal Lobe, G434337789, OCTology: histology using optical coherence tomography, G4855150021, G5100925459, Computeserver Structural &Functional Image Analysis, G6296793415, MRI, Genetics & Cognitive Precursors of AD and Dementia, Open-source software for multi-scale mapping of the human brain, G6956531571, Software Tools for Automated Modeling of White Matter Fascicles from Diffusion MR, The Integrated Initiative of European Laser Research Infrastructures, A Storage Area Network for Structural and Functional Image Analysis, G9000012841, G9000052245, G9000118446, G9000130061, G9000163195, G9000185783, G9000193426, G9000253598, G9000300321, G9000339556, Functionally guided adult whole brain cell atlas in human and NHP, VCID Biomarkers Coordinating Center, FreeSurfer Development, Maintenance, and Hardening, BRAIN CONNECTS: Mapping Connectivity of the Human Brainstem in a Nuclear Coordinate System, Silicon Graphics Prism Extreme 128P/1TB
OpenAlex ID: W7073539947