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Sensitivity of CNN image analysis to multifaceted measurements of neurite growth

Quantitative analysis of neurite growth and morphology is essential for understanding the determinants of neural development and regeneration, however, it is complicated by the labor-intensive process of measuring diverse parameters of neurite outgrowth. Consequently, automated approaches have been...

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Autores principales: Vecchi, Joseph T., Mullan, Sean, Lopez, Josue A., Rhomberg, Madeline, Yamamoto, Annamarie, Hallam, Annabelle, Lee, Amy, Sonka, Milan, Hansen, Marlan R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464248/
https://www.ncbi.nlm.nih.gov/pubmed/37620759
http://dx.doi.org/10.1186/s12859-023-05444-4
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author Vecchi, Joseph T.
Mullan, Sean
Lopez, Josue A.
Rhomberg, Madeline
Yamamoto, Annamarie
Hallam, Annabelle
Lee, Amy
Sonka, Milan
Hansen, Marlan R.
author_facet Vecchi, Joseph T.
Mullan, Sean
Lopez, Josue A.
Rhomberg, Madeline
Yamamoto, Annamarie
Hallam, Annabelle
Lee, Amy
Sonka, Milan
Hansen, Marlan R.
author_sort Vecchi, Joseph T.
collection PubMed
description Quantitative analysis of neurite growth and morphology is essential for understanding the determinants of neural development and regeneration, however, it is complicated by the labor-intensive process of measuring diverse parameters of neurite outgrowth. Consequently, automated approaches have been developed to study neurite morphology in a high-throughput and comprehensive manner. These approaches include computer-automated algorithms known as 'convolutional neural networks' (CNNs)—powerful models capable of learning complex tasks without the biases of hand-crafted models. Nevertheless, their complexity often relegates them to functioning as 'black boxes.' Therefore, research in the field of explainable AI is imperative to comprehend the relationship between CNN image analysis output and predefined morphological parameters of neurite growth in order to assess the applicability of these machine learning approaches. In this study, drawing inspiration from the field of automated feature selection, we investigate the correlation between quantified metrics of neurite morphology and the image analysis results from NeuriteNet—a CNN developed to analyze neurite growth. NeuriteNet accurately distinguishes images of neurite growth based on different treatment groups within two separate experimental systems. These systems differentiate between neurons cultured on different substrate conditions and neurons subjected to drug treatment inhibiting neurite outgrowth. By examining the model's function and patterns of activation underlying its classification decisions, we discover that NeuriteNet focuses on aspects of neuron morphology that represent quantifiable metrics distinguishing these groups. Additionally, it incorporates factors that are not encompassed by neuron morphology tracing analyses. NeuriteNet presents a novel tool ideally suited for screening morphological differences in heterogeneous neuron groups while also providing impetus for targeted follow-up studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05444-4.
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spelling pubmed-104642482023-08-30 Sensitivity of CNN image analysis to multifaceted measurements of neurite growth Vecchi, Joseph T. Mullan, Sean Lopez, Josue A. Rhomberg, Madeline Yamamoto, Annamarie Hallam, Annabelle Lee, Amy Sonka, Milan Hansen, Marlan R. BMC Bioinformatics Research Quantitative analysis of neurite growth and morphology is essential for understanding the determinants of neural development and regeneration, however, it is complicated by the labor-intensive process of measuring diverse parameters of neurite outgrowth. Consequently, automated approaches have been developed to study neurite morphology in a high-throughput and comprehensive manner. These approaches include computer-automated algorithms known as 'convolutional neural networks' (CNNs)—powerful models capable of learning complex tasks without the biases of hand-crafted models. Nevertheless, their complexity often relegates them to functioning as 'black boxes.' Therefore, research in the field of explainable AI is imperative to comprehend the relationship between CNN image analysis output and predefined morphological parameters of neurite growth in order to assess the applicability of these machine learning approaches. In this study, drawing inspiration from the field of automated feature selection, we investigate the correlation between quantified metrics of neurite morphology and the image analysis results from NeuriteNet—a CNN developed to analyze neurite growth. NeuriteNet accurately distinguishes images of neurite growth based on different treatment groups within two separate experimental systems. These systems differentiate between neurons cultured on different substrate conditions and neurons subjected to drug treatment inhibiting neurite outgrowth. By examining the model's function and patterns of activation underlying its classification decisions, we discover that NeuriteNet focuses on aspects of neuron morphology that represent quantifiable metrics distinguishing these groups. Additionally, it incorporates factors that are not encompassed by neuron morphology tracing analyses. NeuriteNet presents a novel tool ideally suited for screening morphological differences in heterogeneous neuron groups while also providing impetus for targeted follow-up studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05444-4. BioMed Central 2023-08-24 /pmc/articles/PMC10464248/ /pubmed/37620759 http://dx.doi.org/10.1186/s12859-023-05444-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Vecchi, Joseph T.
Mullan, Sean
Lopez, Josue A.
Rhomberg, Madeline
Yamamoto, Annamarie
Hallam, Annabelle
Lee, Amy
Sonka, Milan
Hansen, Marlan R.
Sensitivity of CNN image analysis to multifaceted measurements of neurite growth
title Sensitivity of CNN image analysis to multifaceted measurements of neurite growth
title_full Sensitivity of CNN image analysis to multifaceted measurements of neurite growth
title_fullStr Sensitivity of CNN image analysis to multifaceted measurements of neurite growth
title_full_unstemmed Sensitivity of CNN image analysis to multifaceted measurements of neurite growth
title_short Sensitivity of CNN image analysis to multifaceted measurements of neurite growth
title_sort sensitivity of cnn image analysis to multifaceted measurements of neurite growth
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464248/
https://www.ncbi.nlm.nih.gov/pubmed/37620759
http://dx.doi.org/10.1186/s12859-023-05444-4
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