期刊
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷 21, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/ijms21072323
关键词
imaging flow cytometry; cell sorting; deep learning; circulating tumor cells; cell labeling; liquid biopsy; flow cytometry data analysis
资金
- RUSSIAN SCIENCE FOUNDATION [18-19-00354]
- Russian Science Foundation [18-19-00354] Funding Source: Russian Science Foundation
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.
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