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Human axon radii estimation at MRI scale: Deep learning combined with large-scale light microscopy

MPG-Autoren
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Morozova,  Maria
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Jäger,  Carsten
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Weiskopf,  Nikolaus
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Mohammadi,  Siawoosh
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Zitation

Mordhorst, L., Morozova, M., Papazoglou, S., Fricke, B., Oeschger, J. M., Rusch, H., et al. (2021). Human axon radii estimation at MRI scale: Deep learning combined with large-scale light microscopy. In Proceedings of the 2021 German Workshop on Medical Image Computing. Wiesbaden: Springer.


Zitierlink: https://hdl.handle.net/21.11116/0000-0008-6A2F-E
Zusammenfassung
Non-invasive assessment of axon radii via MRI is of increasing interest in human brain research. Its validation requires representative reference data that covers the spatial extent of an MRI voxel (e.g., 1mm2). Due to its small field of view, the commonly used manually labeled electron microscopy (mlEM) can not representatively capture sparsely occurring, large axons, which are the main contributors to the effective mean axon radius (reff) measured with MRI. To overcome this limitation, we investigated the feasibility of generating representative reference data from large-scale light microscopy (lsLM) using automated segmentation methods including a convolutional neural network (CNN).