Semi-automated Analysis of Beading in Degenerating Axons.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Pretheesh Kumar V C, Pramod Pullarkat
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引用次数: 0

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.

退化轴突中串珠的半自动分析。
轴突串珠是轴突变性的重要形态学指标,在各种神经退行性疾病和药物性神经病中起着重要作用。利用神经元细胞培养量化轴突对串珠的易感性可以作为一种简便的方法来评估诱导的退行性疾病,从而有助于理解串珠的机制和药物开发。人工分析大型数据集的轴突头部是劳动密集型的,容易出现主观性,限制了结果的可重复性。为了解决这些挑战,我们开发了一个半自动的基于python的工具来跟踪延时显微镜图像中的轴突串珠。该软件通过检测轴突肿胀的发作显着减少了人类的努力。我们的方法是基于经典的图像处理技术,而不是人工智能方法。这提供了可解释的结果,同时允许提取额外的定量数据,如珠密度、粗化动力学和随时间的形态变化。通过人工分析和软件分析得到的结果比较显示出很强的一致性。该代码可以很容易地扩展到分析河流、道路、血管等分支网络中的山脊状结构的直径信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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