Human Axon Radii Estimation at MRI Scale: Deep Learning Combined with Large-scale Light Microscopy
Laurin Mordhorst, Maria Morozova, Sebastian Papazoglou, Björn Fricke, Jan M. Oeschger, Henriette Rusch, Carsten Jäger, Markus Morawski, Nikolaus Weiskopf, Siawoosh Mohammadi
Universitätsklinikum Hamburg-Eppendorf, Institute of Systems Neuroscience
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). We determined large, mis-/undetected axons as the main error source for the estimation of reff (≈ 10 %). Our results suggest that the proposed pipeline can be used to generate reference data for the MRI-visible reff and even bears the potential to map spatial, anatomical variation of reff.