Label-Free 3D Quantitative Imaging of Live Cells
A method for a real-time visualization and automatic processing for detection and classification of untouched and unstained cells during stain-free imaging flow cytometry and digital pathology using digital holographic microscopy and machine learning.
Most approaches dealing with the identification and characterization of specific cell types rely on using unique antigens or contrast agents. The attachment of markers to the cell organelles can cause unwanted chemical interactions that might change the cell characteristics and, as a result, damage the validity of the measurement. Furthermore, the staining protocol create variabilities between different labs.
Stain-free imaging methods for the identification of different cell types overcome these problems by enabling noninvasive measurements of the cell based on the cell intrinsic properties, without using exogenous contrast agents. One of these methods is interferometric phase microscopy (IPM).
IPM measures the quantitative topographic profile of the cell, accounting for both, its 3D morphology and contents. IPM enables visualizing cells and part of the inner cellular organelles without the use of exogenous contrast agents, as if the cells are chemically stained, and furthermore, it enables calculating new cellular parameters that have not been available to clinicians so far, such as the cellular volume and dry mass.
We have shown that using deep-learning classification a whole-new-world of classification tasks become available, such as single cell cancer grading and automatic white blood cell classification.
• Flow cytometry
• Digital pathology
Several patents on the interferometric phase microscopy modules, on the digital processing and other related methods