Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning
A new label‐free imaging flow cytometry method for noninvasive and automated biological cell classification is presented. Each cell is rolled during flow, and its off‐axis holograms from multiple viewpoints are acquired. Using the reconstructed quantitative phase profiles of the cell projections, hi...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202300433 |
| _version_ | 1797348577795637248 |
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| author | Anat Cohen Matan Dudaie Itay Barnea Francesca Borrelli Jaromír Běhal Lisa Miccio Pasquale Memmolo Vittorio Bianco Pietro Ferraro Natan T. Shaked |
| author_facet | Anat Cohen Matan Dudaie Itay Barnea Francesca Borrelli Jaromír Běhal Lisa Miccio Pasquale Memmolo Vittorio Bianco Pietro Ferraro Natan T. Shaked |
| author_sort | Anat Cohen |
| collection | DOAJ |
| description | A new label‐free imaging flow cytometry method for noninvasive and automated biological cell classification is presented. Each cell is rolled during flow, and its off‐axis holograms from multiple viewpoints are acquired. Using the reconstructed quantitative phase profiles of the cell projections, highly discriminating features, enabling cell detection, classification, and differentiation, are extracted via a modified ResNet‐18 deep convolutional neural network architecture. The model is first validated by classifying metastatic breast carcinoma cells (MCF‐7) and normal human mammary epithelial cells (MCF‐10A). An increase in classification accuracy by 1% is achieved when processing five interferometric projections versus processing a single interferometric projection. This model is further tested on four types of white blood cells and exhibits an accuracy increase of 5% when processing 12 interferometric projections versus processing a single interferometric projection. This approach is shown to be superior to that of using conventional 2D‐rotation augmentation, and can be used to decrease substantially the number of cell examples needed for training the classification model without impairing the results. This novel concept has great potential to be incorporated into label‐free imaging flow cytometry and improve cell classification, and be used to detect various types of medical conditions and diseases. |
| first_indexed | 2024-03-08T12:08:02Z |
| format | Article |
| id | doaj.art-bb722f24c8a241b6858375914e829630 |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-03-08T12:08:02Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-bb722f24c8a241b6858375914e8296302024-01-23T05:32:23ZengWileyAdvanced Intelligent Systems2640-45672024-01-0161n/an/a10.1002/aisy.202300433Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep LearningAnat Cohen0Matan Dudaie1Itay Barnea2Francesca Borrelli3Jaromír Běhal4Lisa Miccio5Pasquale Memmolo6Vittorio Bianco7Pietro Ferraro8Natan T. Shaked9Department of Biomedical Engineering Faculty of Engineering Tel Aviv University Tel Aviv 69978 IsraelDepartment of Biomedical Engineering Faculty of Engineering Tel Aviv University Tel Aviv 69978 IsraelDepartment of Biomedical Engineering Faculty of Engineering Tel Aviv University Tel Aviv 69978 IsraelInstitute of Applied Sciences and Intelligent Systems “E. Caianiello” CNR-ISASI Via Campi Flegrei 34 80078 Pozzuoli Napoli ItalyInstitute of Applied Sciences and Intelligent Systems “E. Caianiello” CNR-ISASI Via Campi Flegrei 34 80078 Pozzuoli Napoli ItalyInstitute of Applied Sciences and Intelligent Systems “E. Caianiello” CNR-ISASI Via Campi Flegrei 34 80078 Pozzuoli Napoli ItalyInstitute of Applied Sciences and Intelligent Systems “E. Caianiello” CNR-ISASI Via Campi Flegrei 34 80078 Pozzuoli Napoli ItalyInstitute of Applied Sciences and Intelligent Systems “E. Caianiello” CNR-ISASI Via Campi Flegrei 34 80078 Pozzuoli Napoli ItalyInstitute of Applied Sciences and Intelligent Systems “E. Caianiello” CNR-ISASI Via Campi Flegrei 34 80078 Pozzuoli Napoli ItalyDepartment of Biomedical Engineering Faculty of Engineering Tel Aviv University Tel Aviv 69978 IsraelA new label‐free imaging flow cytometry method for noninvasive and automated biological cell classification is presented. Each cell is rolled during flow, and its off‐axis holograms from multiple viewpoints are acquired. Using the reconstructed quantitative phase profiles of the cell projections, highly discriminating features, enabling cell detection, classification, and differentiation, are extracted via a modified ResNet‐18 deep convolutional neural network architecture. The model is first validated by classifying metastatic breast carcinoma cells (MCF‐7) and normal human mammary epithelial cells (MCF‐10A). An increase in classification accuracy by 1% is achieved when processing five interferometric projections versus processing a single interferometric projection. This model is further tested on four types of white blood cells and exhibits an accuracy increase of 5% when processing 12 interferometric projections versus processing a single interferometric projection. This approach is shown to be superior to that of using conventional 2D‐rotation augmentation, and can be used to decrease substantially the number of cell examples needed for training the classification model without impairing the results. This novel concept has great potential to be incorporated into label‐free imaging flow cytometry and improve cell classification, and be used to detect various types of medical conditions and diseases.https://doi.org/10.1002/aisy.202300433cell classificationsconvolutional neural networksflow cytometryinterferometric phase microscopylabel-free imaging |
| spellingShingle | Anat Cohen Matan Dudaie Itay Barnea Francesca Borrelli Jaromír Běhal Lisa Miccio Pasquale Memmolo Vittorio Bianco Pietro Ferraro Natan T. Shaked Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning Advanced Intelligent Systems cell classifications convolutional neural networks flow cytometry interferometric phase microscopy label-free imaging |
| title | Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning |
| title_full | Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning |
| title_fullStr | Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning |
| title_full_unstemmed | Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning |
| title_short | Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning |
| title_sort | label free imaging flow cytometry for cell classification based on multiple interferometric projections using deep learning |
| topic | cell classifications convolutional neural networks flow cytometry interferometric phase microscopy label-free imaging |
| url | https://doi.org/10.1002/aisy.202300433 |
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