We show that blood cells can be classified with high accuracy and high throughput by combining machine learning with time stretch quantitative phase imaging. Our diagnostic system captures quantitative phase images in a flow microscope at millions of frames per second and extracts multiple biophysical features from individual cells including morphological characteristics, light absorption and scattering parameters, and protein concentration. These parameters form a hyperdimensional feature space in which supervised learning and cell classification is performed. We show binary classification of T-cells against colon cancer cells, as well classification of algae cell strains with high and low lipid content. The label-free screening averts the negative impact of staining reagents on cellular viability or cell signaling. The combination of time stretch machine vision and learning offers unprecedented cell analysis capabilities for cancer diagnostics, drug development and liquid biopsy for personalized genomics.
Bahram Jalali, Claire L. Chen, and Ata Mahjoubfar, "Cell classification using big data analytics plus time stretch imaging
(Conference Presentation)," Proc. SPIE 9930, Biosensing and Nanomedicine IX, 99300D (Presented at SPIE Nanoscience + Engineering: August 28, 2016; Published: 3 November 2016); https://doi.org/10.1117/12.2239899.5164183655001.
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