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

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.

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Mordhorst, Laurin, Author
Morozova, Maria1, Author              
Papazoglou, Sebastian, Author
Fricke, Björn, Author
Oeschger, Jan M., Author
Rusch, Henriette, Author
Jäger, Carsten1, Author              
Morawski, Markus, Author
Weiskopf, Nikolaus1, Author              
Mohammadi, Siawoosh1, Author              
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1Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              

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 Abstract: 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).

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Language(s): eng - English
 Dates: 20212021
 Publication Status: Published in print
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Title: Proceedings of the 2021 German Workshop on Medical Image Computing
Source Genre: Proceedings
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Publ. Info: Wiesbaden : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISBN: 978-3-658-33197-9