On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning.

We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming.

Real-Time Stain-Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning.

We present a method for real-time visualization and automatic processing for detection and classification of untreated cancer cells in blood during stain-free imaging flow cytometry using digital holographic microscopy and machine learning in throughput of 15 cells per second. As a preliminary model for circulating tumor cells in the blood, following an initial label-free rapid enrichment stage based on the cell size, we applied our holographic imaging approach, providing the quantitative optical thickness profiles of the cells during flow. We automatically classified primary and metastatic colon cancer cells, where the two types of cancer cells were isolated from the same individual, as well as four types of blood cells.

Label-free discrimination and selection of cancer cells from blood during flow using holography-induced dielectrophoresis.

We present a method for label-free imaging and sorting of cancer cells in blood, which is based on a dielectrophoretic microfluidic chip and label-free interferometric phase microscopy. The chip used for imaging has been embedded with dielectrophoretic electrodes, and therefore it can be used to sort the cells based on the decisions obtained during the cell flow by the label-free quantitative imaging method. Hence, we obtained a real-time, automatic, label-free imaging flow cytometry with the ability to sort the cells during flow.