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

Year: 2024

Authors: Zeng XR., Puonti O., Sayeed A., Herisse R., Mora J., Evancic K., Varadarajan D., Balbastre Y., Costantini I., Scardigli M., Ramazzotti J., Di Meo D., Mazzamuto G., Pesce L., Brady N., Cheli F., Pavone F.S., Hof P.R., Frost R., Augustinack J., Van der Kouwe A., Iglesias J.E., Fischl B.

Autors Affiliation: Massachusetts Gen Hosp, Athinoula A Martinos Ctr Biomed Imaging, Dept Radiol, 149 13th St, Boston, MA 02129 USA; Harvard Med Sch, Dept Radiol, 25 Shattuck St, Boston, MA 02115 USA; Copenhagen Univ Hosp, Danish Res Ctr Magnet Resonance, Ctr Funct & Diagnost Imaging & Res, Blegdamsvej 9, DK-2100 Copenhagen, Denmark; CNR, Natl Inst Opt CNR INO, Largo Enr Fermi 6, I-50125 Sesto Fiorentino, Italy; European Lab Nonlinear Spect LENS, Via Nello Carrara 1, I-50019 Sesto Fiorentino, Italy; Univ Florence, Dept Biol, Pza San Marco 4, I-50121 Florence, FI, Italy; Univ Florence, Dept Phys & Astron, Pza San Marco 4, I-50121 Florence, FI, Italy; Icahn Sch Med Mt Sinai, Nash Family Dept Neurosci, 1 Gustave L Levy Pl, New York, NY 10029 USA; Friedman Brain Inst, Icahn Sch Med Mt Sinai, 1 Gustave L Levy Pl, New York, NY 10029 USA.

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.

Journal/Review: CEREBRAL CORTEX

Volume: 34 (9)      Pages from: bhae362-1  to: bhae362-13

More Information: This research was primarily funded by the National Institute of Mental Health 1RF1MH123195. Support fort his research was provided in part by the BRAIN Initiative Cell Census Network grants U01MH117023 and UM1MH130981, the Brain Initiative Brain Connects consortium (U01NS132181, 1UM1NS132358), the National Institute for Biomedical Imaging and Bioengineering (1R01EB023281, R01EB006758, R21EB018907, R01EB019956,P41EB030006), the National Institute on Aging (1R56AG064027, 1R01AG064027, 5R01AG008122, R01AG016495, 1R01AG070988, 5R01AG057672. 1RF1AG080371), the National Institute of Mental Health (R01MH123195, R01MH121885), the National Institute for Neurological Disorders and Stroke (R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625, 5U24NS10059103, R01NS105820, U24NS135561), European Union’s Horizon 2020 research and innovation Framework Programme under grant agreement No. 654148 (Laserlab-Europe), Italian Ministry for Education in the framework of Euro-Bioimaging Italian Node (ESFRI research infrastructure), Fondazione CR Firenze(private foundation), and was made possible by the resources provided by Shared Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043. Additional support was provided by the NIH Blueprint for Neuroscience Research (5U01-MH093765), part of the multi-institutional Human Connectome Project. Much of the computation resources required for this research was performed on computational hardware generously provided by the Massachusetts Life Sciences Center (https://www.masslifesciences.com/). OP was supported by a grant from Lundbeckfonden (grant number R360-2021-395). JEI was supported by a grant from Jack Satter Foundation. XZ was supported by a post doctoral fellowship from Huntington’s Disease Society of America human biology project.
KeyWords: ex vivo MRI; cortical layers; high resolution; semi-supervised learning; neurodegenerative diseases
DOI: 10.1093/cercor/bhae362