Abstract
Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection of rare pathogenic objects in patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer development, various microorganisms and parasites in the blood during acute blood infections. All of these rare diagnostic objects can be detected and identified very rapidly to save a patient’s life. This review outlines the main techniques of visualization of rare objects in the blood flow, methods for extraction of such objects from the blood flow for further investigations and new approaches to identify the objects automatically with the modern deep learning methods.
Highlights
The problem of detection and extraction of rare objects from the blood flow arises in a number of situations
The current state of the art in this field is defined by the progress in cell imaging and sorting techniques, sample enrichment, and separation along with the new approaches for automatization of data analysis based on machine learning and deep learning methods
Most flow cytometers employ imaging systems based on charged coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) sensors, which have a number of differences [37]
Summary
The problem of detection and extraction of rare objects from the blood flow arises in a number of situations. This includes the search for the very rare circulating tumor cells (CTCs) at early stages of cancer development by liquid biopsy [1,2], the detection of microorganisms during acute blood infections to determine its strain very rapidly [3], early detection of malaria parasites including in vivo [4,5] and other pathogenic states that impose high risks to human life and well-being. The current state of the art in this field is defined by the progress in cell imaging and sorting techniques, sample enrichment, and separation along with the new approaches for automatization of data analysis based on machine learning and deep learning methods. We review the modern methods and approaches used for flow cytometer design, cell labeling, their viability evaluation, and cell sorting along with other methods to separate cell subpopulations and the automatic approaches for following data analysis based on machine learning and deep learning methods
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