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Abstract Accurate morphometric analysis is essential to studying nerve development, injury, and regeneration, but current approaches are inefficient, inconsistent, or inaccessible. Here, we present AutoSNAP, a deep learning framework for automated nerve morphometry assessment. AutoSNAP employs a custom convolutional neural network trained on both injured and non-injured murine nerve cross sections.
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