FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images.
The extracellular matrix (ECM) is a complex network of biomolecules that plays an integral role in the structure, processes, and signaling mechanisms of cells and tissues. Identifying and quantifying changes in these matrix components provides insight into the mechanisms behind specific tissue remod...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0312196 |
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| author | Nicholas T Gigliotti Justin Lee Emily H Mang Giancarlo R Zambrano Mitra L Taheri |
| author_facet | Nicholas T Gigliotti Justin Lee Emily H Mang Giancarlo R Zambrano Mitra L Taheri |
| author_sort | Nicholas T Gigliotti |
| collection | DOAJ |
| description | The extracellular matrix (ECM) is a complex network of biomolecules that plays an integral role in the structure, processes, and signaling mechanisms of cells and tissues. Identifying and quantifying changes in these matrix components provides insight into the mechanisms behind specific tissue remodeling processes; however, quantifying these changes is challenging due to difficult imaging conditions, complexity of the ECM, and the subtlety of these changes. Current imaging techniques allow us to visualize these critical remodeling events and developments in image analysis have employed a combination of analysis software and machine learning techniques to improve the efficiency and accuracy with which features are measured. Although image analysis has seen much improvement in recent years, there has been no technique developed to address ambiguity in feature edges in electron microscopy images. Presented here is a new machine learning-based workflow for the analysis of microscopy images named FIRM (Feature Identification from Raw Microscopy) that uses a random forest classifier to identify ECM features of interest and generate binary segmentation masks for quantification with ImageJ-FIJI. FIRM performed with an F1 score of 0.794 and greater than 80% accuracy for number and size of features detected. FIRM had similar deviation from the ground truth in the number of identified fibrils, fibril size, and size distributions when compared to human analyses. The results suggest that FIRM performs as well as manual analysis and requires a fraction of the time. This analysis technique is more efficient, eliminates user bias, and can be easily optimized to identify a variety of features, making it useful for any discipline requiring image analysis. |
| format | Article |
| id | doaj-art-8991816b7c9f4216a3331145e8e4cc7b |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-8991816b7c9f4216a3331145e8e4cc7b2025-02-12T05:30:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031219610.1371/journal.pone.0312196FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images.Nicholas T GigliottiJustin LeeEmily H MangGiancarlo R ZambranoMitra L TaheriThe extracellular matrix (ECM) is a complex network of biomolecules that plays an integral role in the structure, processes, and signaling mechanisms of cells and tissues. Identifying and quantifying changes in these matrix components provides insight into the mechanisms behind specific tissue remodeling processes; however, quantifying these changes is challenging due to difficult imaging conditions, complexity of the ECM, and the subtlety of these changes. Current imaging techniques allow us to visualize these critical remodeling events and developments in image analysis have employed a combination of analysis software and machine learning techniques to improve the efficiency and accuracy with which features are measured. Although image analysis has seen much improvement in recent years, there has been no technique developed to address ambiguity in feature edges in electron microscopy images. Presented here is a new machine learning-based workflow for the analysis of microscopy images named FIRM (Feature Identification from Raw Microscopy) that uses a random forest classifier to identify ECM features of interest and generate binary segmentation masks for quantification with ImageJ-FIJI. FIRM performed with an F1 score of 0.794 and greater than 80% accuracy for number and size of features detected. FIRM had similar deviation from the ground truth in the number of identified fibrils, fibril size, and size distributions when compared to human analyses. The results suggest that FIRM performs as well as manual analysis and requires a fraction of the time. This analysis technique is more efficient, eliminates user bias, and can be easily optimized to identify a variety of features, making it useful for any discipline requiring image analysis.https://doi.org/10.1371/journal.pone.0312196 |
| spellingShingle | Nicholas T Gigliotti Justin Lee Emily H Mang Giancarlo R Zambrano Mitra L Taheri FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images. PLoS ONE |
| title | FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images. |
| title_full | FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images. |
| title_fullStr | FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images. |
| title_full_unstemmed | FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images. |
| title_short | FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images. |
| title_sort | firm image analysis a machine learning workflow for quantifying extracellular matrix components from electron microscopy images |
| url | https://doi.org/10.1371/journal.pone.0312196 |
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