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

Area of Science:

• Neuroscience
• Medical Image Analysis
• Computational Biology

Background:

• Computational morphometry enables large-scale, longitudinal studies of peripheral nerve structure.
• Manual methods for nerve analysis are time-consuming and impractical for extensive research.
• Existing computational approaches face challenges in reliability and interpretability.

Purpose of the Study:

• To review the entire computational morphometry pipeline for peripheral nerve analysis.
• To identify factors influencing the reliability and interpretability of morphometric outputs.
• To highlight challenges in medical image analysis and quantitative neuroanatomy using peripheral nerves as a model.

Main Methods:

• Examination of the complete morphometry pipeline from sample acquisition to metric extraction.
• Analysis of how protocol variability, model generalization, and ground truth ambiguity affect results.
• Case study using peripheral nerve morphometry to illustrate broader issues.

Main Results:

• Morphometric output reliability is pipeline-conditional, influenced by all stages, not solely segmentation accuracy.
• Variability in protocols, limited model generalization, and ambiguous ground truth hinder reproducibility, especially in pathological tissues.
• Issues identified in peripheral nerve morphometry are representative of wider challenges in quantitative neuroanatomy.

Conclusions:

• Progress in computational morphometry requires more than algorithmic refinement.
• Shared datasets, uncertainty-aware validation, and linking structural metrics to functional relevance are crucial.
• Future advancements necessitate closer integration between experimental and clinical contexts.