Computational Morphometry of Peripheral Nerves: A Pipeline Perspective on Reproducibility and Generalization

Computational morphometry has transformed the quantitative analysis of peripheral nerve structure, enabling large-scale, computational, and longitudinal studies that were previously impractical using manual methods. However, this review argues that the reliability and interpretability of morphometric outputs are fundamentally pipeline-conditional, shaped by assumptions introduced across sample acquisition, preparation, imaging, annotation, segmentation, and metric extraction rather than by segmentation accuracy alone. By examining the full morphometry pipeline, we show how protocol variability, limited model generalization, and ambiguity in expert-defined ground truth propagate downstream and constrain reproducibility, particularly in pathological tissue. Using peripheral nerve morphometry as a tractable model system, we highlight issues that are representative of broader challenges in medical image analysis and quantitative neuroanatomy. We conclude that progress in computational morphometry will depend less on incremental algorithmic improvements and more on shared datasets, uncertainty-aware validation, and closer alignment between structural metrics and functional relevance in both experimental and clinical contexts.