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

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

doi:10.1101/2023.12.06.570416

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

bioRxiv [Preprint]. 2023 Dec 8:2023.12.06.570416. doi: 10.1101/2023.12.06.570416.

Authors

Xiangrui Zeng^{1

2}, Oula Puonti^{1

3}, Areej Sayeed^{1

2}, Rogeny Herisse^{1

2}, Jocelyn Mora^{1

2}, Kathryn Evancic^{1

2}, Divya Varadarajan^{1

2}, Yael Balbastre^{1

2}, Irene Costantini^{4

5

6}, Marina Scardigli⁵, Josephine Ramazzotti⁵, Danila DiMeo⁵, Giacomo Mazzamuto^{4

5

7}, Luca Pesce⁵, Niamh Brady⁵, Franco Cheli⁵, Francesco Saverio Pavone^{4

5

7}, Patrick R Hof⁸, Robert Frost^{1

2}, Jean Augustinack^{1

2}, André van der Kouwe^{1

2}, Juan Eugenio Iglesias^{1

2}, Bruce Fischl^{1

2}

Affiliations

¹ Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
² Harvard Medical School, Department of Radiology, Boston, MA, USA.
³ Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark.
⁴ National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy.
⁵ European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy.
⁶ Department of Biology, University of Florence, Italy.
⁷ Department of Physics and Astronomy, University of Florence, Italy.
⁸ Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Abstract

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging 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 (MUS) 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.

Keywords: Cortical Layers; Ex vivo MRI; High Resolution; Segmentation.

Publication types

Preprint

Grants and funding

This research was primarily funded by the National Institute of Mental Health 1RF1MH123195. Support for this 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 Bio-engineering (1R01EB023281, R01EB006758, R21EB018907, R01EB019956, P41EB030006), the National Institute on Aging (1R56AG064027, 1R01AG064027, 5R01AG008122, R01AG016495, 1R01AG070988, 5R01AG057672. 1RF1AG080371), the National Institute of Mental Health (R01 MH123195, R01 MH121885), 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 postdoctoral fellowship from Huntington’s Disease Society of America human biology project.