Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations.

We present a new classification approach for live cells, integrating together the spatial and temporal fluctuation maps and the quantitative optical thickness map of the cell, as acquired by common-path quantitative-phase dynamic imaging and processed with a deep-learning framework. We demonstrate this approach by classifying between two types of cancer cell lines of different metastatic potential originating from the same patient. It is based on the fact that both the cancer-cell morphology and its mechanical properties, as indicated by the cell temporal and spatial fluctuations, change over the disease progression. We tested different fusion methods for inputting both the morphological optical thickness maps and the coinciding spatio-temporal fluctuation maps of the cells to the classifying network framework. We show that the proposed integrated triple-path deep-learning architecture improves over deep-learning classification that is based only on the cell morphological evaluation via its quantitative optical thickness map, demonstrating the benefit in the acquisition of the cells over time and in extracting their spatio-temporal fluctuation maps, to be used as an input to the classifying deep neural network.

Flipping interferometry with doubled imaging area.

We present a new external off-axis holographic module that doubles the acquired complex wavefront field of view, based on using both holographic flipping and multiplexing. In contrast to previous designs, this design does not require spatial filtering (no pinhole or lenses) to create the reference beam externally. In addition, the overlap area between the fields of view, as well as the off-axis angle between the sample and reference beams, can be fully controlled. As we demonstrate experimentally, this approach is useful for quantitative phase microscopy of extended stationary and dynamic samples, such as cancer cells during rapid flow and beating cardiomyocytes.