Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T <i>ex vivo</i> MRI of the human cerebral cortex

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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Publication Details

Journal
Cerebral Cortex
Published
2024-09-01
DOI
https://doi.org/10.1093/cercor/bhae362
Citations
11
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
4.45

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article

Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T ex vivo MRI of the human cerebral cortex

Niamh Brady, Giacomo Mazzamuto, Danila DiMeo, Marina Scardigli et al.
11 citations
Cerebral Cortex
Advanced Neuroimaging Techniques and Applications
4.45
article

Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T ex vivo MRI of the human cerebral cortex

Niamh Brady, Giacomo Mazzamuto, Danila DiMeo, Marina Scardigli, Divya Varadarajan, Kathryn Evancic, Bruce Fischl, Luca Pesce, Jocelyn Mora, Irene Costantini, Juan Eugenio Iglesias, Francesco S. Pavone, Josephine Ramazzotti, Oula Puonti, Robert Frost, Patrick R. Hof, Jean C. Augustinack, Xiangrui Zeng, Franco Cheli, André van der Kouwe, Yaël Balbastre, Rogeny Herisse, Areej Sayeed
article en
11 citations

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.

Cerebral CortexVol. 34(9)
Allen Institute for Brain Science (US), Harvard University (US), Lemuel Shattuck Hospital (US), Copenhagen University Hospital (DK), Instituto de Óptica "Daza de Valdés" (ES), Massachusetts General Hospital (US), Nello Carrara Institute of Applied Physics (IT), Imaging, Brain, and Neuropsychiatry (FR), National Institute of Optics (IT), Athinoula A. Martinos Center for Biomedical Imaging (US), National Research Council (IT), University of Florence (IT), Icahn School of Medicine at Mount Sinai (US)
Lundbeckfonden, National Institute on Aging, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, NIH Blueprint for Neuroscience Research
Partnerships for the goals
Openalex Percentile: Top 5%
Advanced Neuroimaging Techniques and Applications
4.45
DOIOpen Access PDFOpenAlex
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