We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells’ motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

The noninvasive, fast acquisition of quantitative phase maps using digital holographic microscopy (DHM) allows tracking of rapid cellular motility on transparent substrates. On two‐dimensional surfaces in vitro, MDA‐MB‐231 cancer cells assume several morphologies related to the mode of migration and substrate stiffness, relevant to mechanisms of cancer invasiveness in vivo. The quantitative phase information from DHM may accurately classify adhesive cancer cell subpopulations with clinical relevance. To test this, cells from the invasive breast cancer MDA‐MB‐231 cell line were cultured on glass, tissue‐culture treated polystyrene, and collagen hydrogels, and imaged with DHM followed by epifluorescence microscopy after staining F‐actin and nuclei. Trends in cell phase parameters were tracked on the different substrates, during cell division, and during matrix adhesion, relating them to F‐actin features. Support vector machine learning algorithms were trained and tested using parameters from holographic phase reconstructions and cell geometric features from conventional phase images, and used to distinguish between elongated and rounded cell morphologies. DHM was able to distinguish between elongated and rounded morphologies of MDA‐MB‐231 cells with 94% accuracy, compared to 83% accuracy using cell geometric features from conventional brightfield microscopy. This finding indicates the potential of DHM to detect and monitor cancer cell morphologies relevant to cell cycle phase status, substrate adhesion, and motility. © 2017 International Society for Advancement of Cytometry

Quantitative phase imaging (QPI) is a label-free optical imaging technique that measures the phase delay introduced when a coherent laser beam travels through a thin transparent specimen, such as cells.¹ The uncorrected optical pathlength yields information about cell morphology and geometric thickness of the specimen along its phase projection, as well as fluctuations in the local index of refraction.² Reconstructed optical phase maps that result from QPI of cells possess phase texture in the variation of pixel values within the boundary of the cell,³ quantified through texture parameters.⁴ The pixel information from phase maps is relevant to cell structure and function. For example, a cell’s refractive index can reveal its total protein concentration,⁵ organization, and distribution of subcellular organelles, which are distinctive for certain biological phenotypes.⁶ Hence, a collection of quantitative parameters from optical phase maps constitutes a cell type’s phase signature, providing additional information beyond the cell shape.

Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to correlate optical phase parameters to motile behavior. Digital holographic microscopy (DHM) reconstructed phase maps of cells from two pairs of cancer and non‐cancer cell lines. Seventeen image parameters were extracted from each cell's phase map, used for linear support vector machine learning, and correlated to scratch wound closure and Boyden chamber chemotaxis. The classification accuracy was between 90% and 100% for the six pairwise cell line comparisons. Several phase parameters correlated with wound closure rate and chemotaxis across the four cell lines. The level of cell confluence in culture affected phase parameters in all cell lines tested. Results indicate that optical phase features of cell lines are a robust set of quantitative data of potential utility for phenotypic screening and prediction of motile behavior. © 2019 International Society for Advancement of Cytometry

Microscopy techniques, particularly the family of epifluorescence modalities, are workhorses of modern cell and molecular biology that enable microscale spatial insight. Intensity- and/or phase-based microscopy techniques such as brightfield, phase contrast, differential interference contrast, digital holography, Fourier ptychography, and optical diffraction tomography,^{1 – 7} among other modalities, have the potential to visualize the subcellular structure. However, these methods depend largely on light scattering that is defined by the internal structure-based index of refraction, which lacks biomolecular specificity. Fluorescence-based techniques, on the other hand, excite fluorophores, which act as labels to spatially localize biological molecules and structures within cells. These imaging techniques, especially fluorescence approaches, involve time-consuming preparation steps and costly reagents, introduce the possibility of signal bias due to photobleaching, and in time-lapse vital imaging, allow for possible misinterpretation of cell behavior due to gradual accumulation of sublethal damage from intense ultraviolet and other wavelengths used to excite molecular labels.⁸ Thus microscopy of cells is challenging due to the inherent trade-offs in sample preservation, image quality, and data acquisition time and the variability between labeling experiments.