Unsupervised clustering of AxonDeepSeg morphometrics uncovers early patterns of axon-myelin secondary degeneration following neurotrauma

Abstract Reliable quantification of axon and myelin ultrastructure is essential for understanding white-matter pathology, yet conventional manual methods are subjective, labour-intensive, and limited to compactly myelinated fibres. Here, we adapted and validated a custom AxonDeepSeg model for automated segmentation of transmission electron micrographs from the rat optic nerve following partial transection or sham surgery. The trained model achieved accuracy and Dice similarity indices comparable to published benchmarks, and automated axon diameter measurements closely matched manual quantification. Beyond reproducing traditional morphometrics, AxonDeepSeg detected subtle yet significant increases in g-ratio, solidity, and axon-to-fibre area ratio two weeks post-injury-changes that were undetectable by manual analysis-indicating early alterations in axon-myelin geometry preceding overt demyelination. Unsupervised clustering of multidimensional morphometric data identified six distinct axon–myelin phenotypes, two of which (Clusters 1 and 2) exhibited pronounced injury-related remodelling not apparent on assessment of the full, un-clustered dataset. These findings demonstrate that automated morphometry not only enhanced throughput and reproducibility but also increased sensitivity to otherwise undetectable microstructural changes in subsets of myelinated axons. This integrative workflow provides a scalable framework for identifying early and subtle indicators of axonal pathology and secondary degeneration in neurotrauma and other demyelinating conditions.