A simple and robust method for automating analysis of na?ve and regenerating peripheral nerves

Abstract BackgroundManual axon histomorphometry (AH) is time- and resource-intensive, which has inspired many attempts at automation. However, there has been little investigation on implementation of automated programs for widespread use. Ideally such a program should be able to perform AH across imaging modalities and nerve states. AxonDeepSeg (ADS) is an open source deep learning program that has previously been validated in electron microscopy. We evaluated the robustness of ADS for peripheral nerve axonal histomorphometry in light micrographs prepared using two different methods. MethodsAxon histomorphometry using ADS and manual analysis (gold-standard) was performed on light micrographs of na?ve or regenerating rat median nerve cross-sections prepared with either toluidine-resin or osmium-paraffin embedding protocols. The parameters of interest included axon count, axon diameter, myelin thickness, and g-ratio. ResultsManual and automatic ADS axon counts demonstrated good agreement in na?ve nerves and moderate agreement on regenerating nerves. There were small but consistent differences in measured axon diameter, myelin thickness and g-ratio; however, absolute differences were small. Both methods appropriately identified differences between na?ve and regenerating nerves. ADS was faster than manual axon analysis. ConclusionsWithout any algorithm retraining, ADS was able to appropriately identify critical differences between na?ve and regenerating nerves and work with different sample preparation methods of peripheral nerve light micrographs. While there were differences between absolute values between manual and ADS, ADS performed consistently and required much less time. ADS is an accessible and robust tool for AH that can provide consistent analysis across protocols and nerve states.