Deep Learning Approach for Quantification of Fluorescently Labeled Blood Cells in Danio rerio (Zebrafish)

1. Kolaczkowska, E, Kubes, P. Neutrophil recruitment and function in health and inflammation. Nat Rev Immunol. 2013;13:159-175.
Google Scholar | Crossref | Medline | ISI2. Renshaw, SA, Loynes, CA, Trushell, DM, Elworthy, S, Ingham, PW, Whyte, MK. A transgenic zebrafish model of neutrophilic inflammation. Blood. 2006;108: 3976-3978.
Google Scholar | Crossref | Medline3. Harvie, EA, Huttenlocher, A. Neutrophils in host defense: new insights from zebrafish. J Leukoc Biol. 2015;98:523-537.
Google Scholar | Crossref | Medline4. Henry, KM, Loynes, CA, Whyte, MK, Renshaw, SA. Zebrafish as a model for the study of neutrophil biology. J Leukoc Biol. 2013;94:633-642.
Google Scholar | Crossref | Medline5. Nave, O, Elbaz, M. Artificial immune system features added to breast cancer clinical data for machine learning (ML) applications. Biosystems. 2021;202:104341.
Google Scholar | Crossref | Medline6. Rajpurkar Irvin, J, Ball, RL, Zhu, K, et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15:e1002686.
Google Scholar | Crossref | Medline7. Tyagi, G, Patel, N, Sethi, I. A fine-tuned convolution neural network based approach for phenotype classification of zebrafish embryo. Procedia Comput Sci. 2018;126: 1138-1144.
Google Scholar | Crossref8. Ishaq, O, Sadanandan, SK, Wählby, C. Deep fish. SLAS Disc Adv Life Sci R & D. 2017;22:102-107.
Google Scholar | Medline9. Huarng, MC, Shavit, JA. Simple and rapid quantification of thrombocytes in zebrafish larvae. Zebrafish. 2015;12:238-242.
Google Scholar | Crossref | Medline10. Rueb, KF, Stachura, DL. Using flow cytometry to detect and quantitate altered blood formation in the developing zebrafish. J Vis Exp. 2021;170:61035.
Google Scholar11. Carpenter, AE, Jones, TR, Lamprecht, MR, et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 2006;7:R100.
Google Scholar | Crossref | Medline | ISI12. Falk, T, Mai, D, Bensch, R, et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Meth. 2019;16:67-70.
Google Scholar | Crossref | Medline13. Berrun, A, Harris, E, Stachura, DL. Isthmin 1 (Ism1) is required for normal hematopoiesis in developing zebrafish. PLoS ONE. 2018;13:e0196872.
Google Scholar | Crossref | Medline14. Westerfield, M. The Zebrafish Book. A Guide for the Laboratory Use of Zebrafish (Danio rerio). 5th ed. Eugene, OR: University of Oregon Press; 2007.
Google Scholar15. Casado-García, Á, Domínguez, C, García-Domínguez, M, et al. CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks. BMC Bioinformatics. 2019;20:323.
Google Scholar | Crossref | Medline16. Redmon, J, Divvala, S, Girshick, R, et al. You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, USA, 27-30 June. Piscataway, NJ: IEEE; 2016:779-788.
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