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Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology
Thermal inkjet printing can generate more than 300,000 droplets of picoliter scale within one second stably, and the image analysis workflow is used to quantify the positive and negative values of the droplets. In this paper, the SimpleBlobDetector detection algorithm is used to identify and localiz...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573249/ https://www.ncbi.nlm.nih.gov/pubmed/36236321 http://dx.doi.org/10.3390/s22197222 |
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author | Zhu, Mingjie Shan, Zilong Ning, Wei Wu, Xuanye |
author_facet | Zhu, Mingjie Shan, Zilong Ning, Wei Wu, Xuanye |
author_sort | Zhu, Mingjie |
collection | PubMed |
description | Thermal inkjet printing can generate more than 300,000 droplets of picoliter scale within one second stably, and the image analysis workflow is used to quantify the positive and negative values of the droplets. In this paper, the SimpleBlobDetector detection algorithm is used to identify and localize droplets with a volume of 24 pL in bright field images and suppress bright spots and scratches when performing droplet location identification. The polynomial surface fitting of the pixel grayscale value of the fluorescence channel image can effectively compensate and correct the image vignetting caused by the optical path, and the compensated fluorescence image can accurately classify positive and negative droplets by the k-means clustering algorithm. 20 µL of the sample solution in the result reading chip can produce more than 100,000 effective droplets. The effective droplet identification correct rate of 20 images of random statistical samples can reach more than 99% and the classification accuracy of positive and negative droplets can reach more than 98% on average. This paper overcomes the problem of effectively classifying positive and negative droplets caused by the poor image quality of photographed picolitre ddPCR droplets caused by optical hardware limitations. |
format | Online Article Text |
id | pubmed-9573249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95732492022-10-17 Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology Zhu, Mingjie Shan, Zilong Ning, Wei Wu, Xuanye Sensors (Basel) Article Thermal inkjet printing can generate more than 300,000 droplets of picoliter scale within one second stably, and the image analysis workflow is used to quantify the positive and negative values of the droplets. In this paper, the SimpleBlobDetector detection algorithm is used to identify and localize droplets with a volume of 24 pL in bright field images and suppress bright spots and scratches when performing droplet location identification. The polynomial surface fitting of the pixel grayscale value of the fluorescence channel image can effectively compensate and correct the image vignetting caused by the optical path, and the compensated fluorescence image can accurately classify positive and negative droplets by the k-means clustering algorithm. 20 µL of the sample solution in the result reading chip can produce more than 100,000 effective droplets. The effective droplet identification correct rate of 20 images of random statistical samples can reach more than 99% and the classification accuracy of positive and negative droplets can reach more than 98% on average. This paper overcomes the problem of effectively classifying positive and negative droplets caused by the poor image quality of photographed picolitre ddPCR droplets caused by optical hardware limitations. MDPI 2022-09-23 /pmc/articles/PMC9573249/ /pubmed/36236321 http://dx.doi.org/10.3390/s22197222 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhu, Mingjie Shan, Zilong Ning, Wei Wu, Xuanye Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology |
title | Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology |
title_full | Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology |
title_fullStr | Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology |
title_full_unstemmed | Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology |
title_short | Image Segmentation and Quantification of Droplet dPCR Based on Thermal Bubble Printing Technology |
title_sort | image segmentation and quantification of droplet dpcr based on thermal bubble printing technology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573249/ https://www.ncbi.nlm.nih.gov/pubmed/36236321 http://dx.doi.org/10.3390/s22197222 |
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