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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.

We present an analysis method that can automatically classify live cancer cells from cell lines based on a small data set of quantitative phase imaging data without cell staining. The method includes spatial image analysis to extract the cell phase spatial fluctuation map, derived from the quantitative phase map of the cell measured without cell labeling, thus without prior knowledge on the biomarker. The spatial fluctuations are indicative of the cell stiffness, where cancer cells change their stiffness as cancer progresses. In this paper, the quantitative phase spatial fluctuations are used as the basis for a deep-learning classifier for evaluating the cell metastatic potential. The spatial fluctuation analysis performed on the quantitative phase profiles before inputting them to the neural network was proven to increase the classification results in comparison to inputting the quantitative phase profiles directly, as done so far. We classified between primary and metastatic cancer cells and obtained 92.5% accuracy, in spite of using a small training set, demonstrating the method potential for objective automatic clinical diagnosis of cancer cells in vitro.

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