Title:	Semi-automated Analysis of Beading in Degenerating Axons
Authors:	Kumar, Prateesh V.C. Pullarkat, Pramod
Keywords:	Shape Analysis Neural Patterning Microscopy Barrel Cortex Axon and dendritic guidance Automated Pattern Recognition
Issue Date:	24-Apr-2025
Publisher:	Springer
Citation:	Neuroinformatics, 2025, Vol. 23, Article No.: 30
Abstract:	Axonal beading is a key morphological indicator of axonal degeneration, which plays a significant role in various neurodegenerative diseases and drug-induced neuropathies. Quantification of axonal susceptibility to beading using neuronal cell culture can be used as a facile assay to evaluate induced degenerative conditions, and thus aid in understanding mechanisms of beading and in drug development. Manual analysis of axonal beading for large datasets is labor-intensive and prone to subjectivity, limiting the reproducibility of results. To address these challenges, we developed a semi-automated Python-based tool to track axonal beading in time-lapse microscopy images. The software significantly reduces human effort by detecting the onset of axonal swelling. Our method is based on classical image processing techniques rather than an AI approach. This provides interpretable results while allowing the extraction of additional quantitative data, such as bead density, coarsening dynamics, and morphological changes over time. Comparison of results obtained through human analysis and the software shows strong agreement. The code can be easily extended to analyze diameter information of ridge-like structures in branched networks of rivers, road networks, blood vessels, etc.
Description:	Restricted Access
URI:	http://hdl.handle.net/2289/8398
Alternative Location:	https://doi.org/10.1007/s12021-025-09726-5
Copyright:	2025 The Author(s)
Appears in Collections:	Research Papers (SCM)

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