{"title":"退化轴突中串珠的半自动分析。","authors":"Pretheesh Kumar V C, Pramod Pullarkat","doi":"10.1007/s12021-025-09726-5","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"30"},"PeriodicalIF":2.7000,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Semi-automated Analysis of Beading in Degenerating Axons.\",\"authors\":\"Pretheesh Kumar V C, Pramod Pullarkat\",\"doi\":\"10.1007/s12021-025-09726-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":49761,\"journal\":{\"name\":\"Neuroinformatics\",\"volume\":\"23 2\",\"pages\":\"30\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuroinformatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12021-025-09726-5\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroinformatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12021-025-09726-5","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Semi-automated Analysis of Beading in Degenerating Axons.
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.
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.