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5 March 2021 White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS)
Michael J. Fanous, Gabriel Popescu, Krishnarao Tangella, Nahil Sobh
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Proceedings Volume 11653, Quantitative Phase Imaging VII; 1165311 (2021) https://doi.org/10.1117/12.2584464
Event: SPIE BiOS, 2021, Online Only
Abstract
In this study, we used spatial light interference microscopy (SLIM), an ultrasensitive QPI method, and deep learning, to first generate a virtually-stained micrograph of a blood smear. This approach of combining label-free QPI data with deep learning to infer chemical specificity has been recently developed in our laboratory and is referred to as PICS [Nat. Comm., in press]. Next, we applied a computational semantic segmentation to identify and delineate the white blood cells. Lastly, we ran a classification model on the leukocytes to identify their type and condition. PICS renders synthetically stained blood smears rapidly, at a reduced cost of sample preparation, and provides quantitative clinical information. We validated this approach by successfully creating computationally stained micrographs and classified the leukocytes into five cell classes, with 92% accuracy.
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Michael J. Fanous, Gabriel Popescu, Krishnarao Tangella, and Nahil Sobh "White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS)", Proc. SPIE 11653, Quantitative Phase Imaging VII, 1165311 (5 March 2021); https://doi.org/10.1117/12.2584464
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KEYWORDS
Blood
Microscopy
Phase imaging
Photonic integrated circuits
Digital holography
Flow cytometry
Tomography
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