Quantitative Morphological and Biochemical Feature Extraction and Supervised Learning and Identification
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Results: Representative Label-Free Lymphocyte Subtype 3D Phase Imaging Identification
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Conclusion
副本
This method provides an alternative to conventional fluorescence labeling and flow cytometry analysis procedure, which are time-consuming, costly, and incur the risk of altering the cellular function of the samples. The main advantage of this tech
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We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell types.