Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim..
Measurement of nuclear-to-cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label-free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes and orientations to calculate cross-polarized diffraction image (p-DI) pairs divided into three nuclear size groups of OCMS , OCMO and OCML based on three prostate cell structures. Binary classifications were conducted among the three groups with image parameters extracted by the algorithm of gray-level co-occurrence matrix. The averaged accuracy of support vector machine (SVM) classifier on test dataset of p-DI was found to be 98.8% and 97.5% respectively for binary classifications of OCMS vs OCMO and OCMO vs OCML for the prostate cancer cell structure. The values remain about the same at 98.9% and 97.8% for the smaller prostate normal cell structures. The robust performance of SVM over clustering classifiers suggests that the high-order correlations of diffraction patterns are potentially useful for label-free detection of single cells with large N:C ratios.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
Journal of biophotonics - 13(2020), 9 vom: 16. Sept., Seite e202000036 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Jing [VerfasserIn] |
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| Links: |
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| Themen: |
Cell modeling |
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| Anmerkungen: |
Date Completed 23.06.2021 Date Revised 23.06.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/jbio.202000036 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM310863236 |
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| 245 | 1 | 0 | |a Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes |
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| 500 | |a Date Revised 23.06.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. | ||
| 520 | |a Measurement of nuclear-to-cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label-free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes and orientations to calculate cross-polarized diffraction image (p-DI) pairs divided into three nuclear size groups of OCMS , OCMO and OCML based on three prostate cell structures. Binary classifications were conducted among the three groups with image parameters extracted by the algorithm of gray-level co-occurrence matrix. The averaged accuracy of support vector machine (SVM) classifier on test dataset of p-DI was found to be 98.8% and 97.5% respectively for binary classifications of OCMS vs OCMO and OCMO vs OCML for the prostate cancer cell structure. The values remain about the same at 98.9% and 97.8% for the smaller prostate normal cell structures. The robust performance of SVM over clustering classifiers suggests that the high-order correlations of diffraction patterns are potentially useful for label-free detection of single cells with large N:C ratios | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a cell modeling | |
| 650 | 4 | |a cytology | |
| 650 | 4 | |a diffraction imaging | |
| 650 | 4 | |a light scattering | |
| 700 | 1 | |a Xu, Yaohui |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Wenjin |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Yuhua |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Heng |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Jun Q |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Xin-Hua |e verfasserin |4 aut | |
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