Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.
Denis V VoroninAnastasiia A KozlovaRoman A VerkhovskiiAlexey V ErmakovMikhail A MakarkinOlga A InozemtsevaDaniil N BratashovPublished in: International journal of molecular sciences (2020)
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.
Keyphrases
- blood flow
- flow cytometry
- deep learning
- circulating tumor cells
- case report
- primary care
- high resolution
- artificial intelligence
- machine learning
- convolutional neural network
- drug induced
- real time pcr
- papillary thyroid
- label free
- squamous cell carcinoma
- respiratory failure
- cell therapy
- lymph node metastasis
- extracorporeal membrane oxygenation
- patient reported outcomes
- cell free