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Abstract #4722

Deep learning segmentation (AxonDeepSeg) to generate axonal-property map from ex vivo human optic chiasm using light microscopy

Thibault Tabarin1, Maria Morozova2,3, Carsten Jaeger2, Henriette Rush3, Markus Morawski3, Stefan Geyer2, and Siawoosh Mohammadi1

1Department of Neurophysics, Medical center Hamburg-Eppendorf, Hamburg, Germany, 2Department of Neurophysics, Max Planck Institute for Human cognitive and Brain Sciences, Leipzig, Germany, 3Paul Flechsig Institut of Brain Research, University Leipzig, Leipzig, Germany

Development of in-vivo histology using MRI needs validation strategies with gold standard methods. Ex-vivo histology combined with microscopy could become such a strategy; however, for comparing larger field-of-views automatic segmentation of axons and myelin will be required. State-of-the-art segmentation has recently involved deep learning (DL). In this work, we investigated the recently published AxonDeepSeg deep learning algorithm (ADS). We successful applied ADS on light microscopy images of an optical chiasm sample, improved the segmentation of myelin to access the full properties of individual fibers, and finally created microstructural maps such as the histology g-ratio map.

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