Cell counting for in vivo flow cytometry signals with baseline drift
In biomedical research fields, the in vivo flow cytometry (IVFC) is a widely used technology which is able to monitor target cells dynamically in living animals. Although the setup of IVFC system has been well established, baseline drift is still a challenge in the process of quantifying circulating...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2017-05-01
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| Series: | Journal of Innovative Optical Health Sciences |
| Subjects: | |
| Online Access: | http://www.worldscientific.com/doi/pdf/10.1142/S1793545817500080 |
| _version_ | 1811200917517631488 |
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| author | Xiaoling Wang Yuanzhen Suo Dan Wei Hao He Fan Wu Xunbin Wei |
| author_facet | Xiaoling Wang Yuanzhen Suo Dan Wei Hao He Fan Wu Xunbin Wei |
| author_sort | Xiaoling Wang |
| collection | DOAJ |
| description | In biomedical research fields, the in vivo flow cytometry (IVFC) is a widely used technology which is able to monitor target cells dynamically in living animals. Although the setup of IVFC system has been well established, baseline drift is still a challenge in the process of quantifying circulating cells. Previous methods, i.e., the dynamic peak picking method, counted cells by setting a static threshold without considering the baseline drift, leading to an inaccurate cell quantification. Here, we developed a method of cell counting for IVFC data with baseline drift by interpolation fitting, automatic segmentation and wavelet-based denoising. We demonstrated its performance for IVFC signals with three types of representative baseline drift. Compared with non-baseline-correction methods, this method showed a higher sensitivity and specificity, as well as a better result in the Pearson’s correlation coefficient and the mean-squared error (MSE). |
| first_indexed | 2024-04-12T02:11:52Z |
| format | Article |
| id | doaj.art-eff94e699d154cdeb01fc447b15537b4 |
| institution | Directory Open Access Journal |
| issn | 1793-5458 1793-7205 |
| language | English |
| last_indexed | 2024-04-12T02:11:52Z |
| publishDate | 2017-05-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj.art-eff94e699d154cdeb01fc447b15537b42022-12-22T03:52:23ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052017-05-011031750008-11750008-1010.1142/S179354581750008010.1142/S1793545817500080Cell counting for in vivo flow cytometry signals with baseline driftXiaoling Wang0Yuanzhen Suo1Dan Wei2Hao He3Fan Wu4Xunbin Wei5Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, P. R. ChinaMed-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, P. R. ChinaMed-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, P. R. ChinaMed-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, P. R. ChinaSchool of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. ChinaMed-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, P. R. ChinaIn biomedical research fields, the in vivo flow cytometry (IVFC) is a widely used technology which is able to monitor target cells dynamically in living animals. Although the setup of IVFC system has been well established, baseline drift is still a challenge in the process of quantifying circulating cells. Previous methods, i.e., the dynamic peak picking method, counted cells by setting a static threshold without considering the baseline drift, leading to an inaccurate cell quantification. Here, we developed a method of cell counting for IVFC data with baseline drift by interpolation fitting, automatic segmentation and wavelet-based denoising. We demonstrated its performance for IVFC signals with three types of representative baseline drift. Compared with non-baseline-correction methods, this method showed a higher sensitivity and specificity, as well as a better result in the Pearson’s correlation coefficient and the mean-squared error (MSE).http://www.worldscientific.com/doi/pdf/10.1142/S1793545817500080In vivo flow cytometrycell countingbaseline driftsignal processing |
| spellingShingle | Xiaoling Wang Yuanzhen Suo Dan Wei Hao He Fan Wu Xunbin Wei Cell counting for in vivo flow cytometry signals with baseline drift Journal of Innovative Optical Health Sciences In vivo flow cytometry cell counting baseline drift signal processing |
| title | Cell counting for in vivo flow cytometry signals with baseline drift |
| title_full | Cell counting for in vivo flow cytometry signals with baseline drift |
| title_fullStr | Cell counting for in vivo flow cytometry signals with baseline drift |
| title_full_unstemmed | Cell counting for in vivo flow cytometry signals with baseline drift |
| title_short | Cell counting for in vivo flow cytometry signals with baseline drift |
| title_sort | cell counting for in vivo flow cytometry signals with baseline drift |
| topic | In vivo flow cytometry cell counting baseline drift signal processing |
| url | http://www.worldscientific.com/doi/pdf/10.1142/S1793545817500080 |
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