Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN)
With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied s...
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MDPI AG
2024-10-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/14/11/1348 |
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| author | Alok Bhattarai Jan Meyer Laura Petersilie Syed I. Shah Louis A. Neu Christine R. Rose Ghanim Ullah |
| author_facet | Alok Bhattarai Jan Meyer Laura Petersilie Syed I. Shah Louis A. Neu Christine R. Rose Ghanim Ullah |
| author_sort | Alok Bhattarai |
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| description | With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied software, which often demands extensive training and lacks automation capabilities for handling diverse datasets. A critical challenge arises if the fluorophores employed exhibit low brightness and a low signal-to-noise ratio (SNR). Consequently, manual intervention may become a necessity, introducing variability in the analysis outcomes even for identical samples when analyzed by different users. This leads to the incorporation of blinded analysis, which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using deep learning. We demonstrate the program’s ability to automate cell identification and study cellular ion dynamics using synthetic image stacks with varying SNR. This is followed by its application to experimental Na<sup>+</sup> and Ca<sup>2+</sup> imaging data from neurons and astrocytes in mouse brain tissue slices exposed to transient chemical ischemia. The results from DL-SCAN are consistent, reproducible, and free from user bias, allowing efficient and rapid analysis of experimental data in an objective manner. The open-source nature of the tool also provides room for modification and extension to analyze other forms of microscopy images specific to the dynamics of different ions in other cell types. |
| format | Article |
| id | doaj-art-20481e4f24a44d3fbc7f586ad5074659 |
| institution | Kabale University |
| issn | 2218-273X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Biomolecules |
| spelling | doaj-art-20481e4f24a44d3fbc7f586ad50746592024-11-26T17:53:54ZengMDPI AGBiomolecules2218-273X2024-10-011411134810.3390/biom14111348Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN)Alok Bhattarai0Jan Meyer1Laura Petersilie2Syed I. Shah3Louis A. Neu4Christine R. Rose5Ghanim Ullah6Department of Physics, University of South Florida, Tampa, FL 33647, USAInstitute of Neurobiology, Faculty of Mathematics and Natural Sciences, Heinrich-Heine-University, 40225 Düsseldorf, GermanyInstitute of Neurobiology, Faculty of Mathematics and Natural Sciences, Heinrich-Heine-University, 40225 Düsseldorf, GermanyDepartment of Physics, University of South Florida, Tampa, FL 33647, USAInstitute of Neurobiology, Faculty of Mathematics and Natural Sciences, Heinrich-Heine-University, 40225 Düsseldorf, GermanyInstitute of Neurobiology, Faculty of Mathematics and Natural Sciences, Heinrich-Heine-University, 40225 Düsseldorf, GermanyDepartment of Physics, University of South Florida, Tampa, FL 33647, USAWith the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied software, which often demands extensive training and lacks automation capabilities for handling diverse datasets. A critical challenge arises if the fluorophores employed exhibit low brightness and a low signal-to-noise ratio (SNR). Consequently, manual intervention may become a necessity, introducing variability in the analysis outcomes even for identical samples when analyzed by different users. This leads to the incorporation of blinded analysis, which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using deep learning. We demonstrate the program’s ability to automate cell identification and study cellular ion dynamics using synthetic image stacks with varying SNR. This is followed by its application to experimental Na<sup>+</sup> and Ca<sup>2+</sup> imaging data from neurons and astrocytes in mouse brain tissue slices exposed to transient chemical ischemia. The results from DL-SCAN are consistent, reproducible, and free from user bias, allowing efficient and rapid analysis of experimental data in an objective manner. The open-source nature of the tool also provides room for modification and extension to analyze other forms of microscopy images specific to the dynamics of different ions in other cell types.https://www.mdpi.com/2218-273X/14/11/1348live cell imagingcell segmentationtracking ion dynamicsstreamlittracking morphological changes |
| spellingShingle | Alok Bhattarai Jan Meyer Laura Petersilie Syed I. Shah Louis A. Neu Christine R. Rose Ghanim Ullah Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN) Biomolecules live cell imaging cell segmentation tracking ion dynamics streamlit tracking morphological changes |
| title | Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN) |
| title_full | Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN) |
| title_fullStr | Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN) |
| title_full_unstemmed | Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN) |
| title_short | Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN) |
| title_sort | deep learning based segmentation of cells and analysis dl scan |
| topic | live cell imaging cell segmentation tracking ion dynamics streamlit tracking morphological changes |
| url | https://www.mdpi.com/2218-273X/14/11/1348 |
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