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In this work we seek to demonstrate acceptable classification performance for classifiers trained using augmented training data of synthetic cells imaged by simulated holography and evaluated using experimentally collected holographic data. In particular, we utilize experimentally collected DHM phase maps derived from the MDA-MB-231 breast cancer cell and immortalized human gingival fibroblast (GIE) cell lines. Automated segmentation was performed using a floodfill clustering approach and experimental feature distributions were used to generate statistically random synthetic cell realizations for each class.

ML is useful for classifying cells based on features derived from quantitative phase images. However, classifying cells with graded phenotype remains challenging. To address this, several ML algorithms were trained and validated on reconstructed phase images.

Fluorescence microscopy plays a vital role in understanding the subcellular structures of living cells. However, it requires considerable effort in sample preparation related to chemical fixation, staining, cost, and time. To reduce those factors, we present a virtual fluorescence staining method based on deep neural networks (VirFluoNet) to transform fluorescence images of molecular labels into other molecular fluorescence labels in the same field-of-view. To achieve this goal, we develop and train a conditional generative adversarial network (cGAN) to perform digital fluorescence imaging demonstrated on human osteosarcoma U2OS cell fluorescence images captured under Cell Painting staining protocol. A detailed comparative analysis is also conducted on the performance of the cGAN network between predicting fluorescence channels based on phase contrast or based on another fluorescence channel using human breast cancer MDA-MB-231 cell line as a test case. In addition, we implement a deep learning model to perform autofocusing on another human U2OS fluorescence dataset as a preprocessing step to defocus an out-focus channel in U2OS dataset. A quantitative index of image prediction error is introduced based on signal pixel-wise spatial and intensity differences with ground truth to evaluate the performance of prediction to high-complex and throughput fluorescence. This index provides a rational way to perform image segmentation on error signals and to understand the likelihood of mis-interpreting biology from the predicted image. In total, these findings contribute to the utility of deep learning image regression for fluorescence microscopy datasets of biological cells, balanced against savings of cost, time, and experimental effort. Furthermore, the approach introduced here holds promise for modeling the internal relationships between organelles and biomolecules within living cells., 20 pages, 9 figures

Optical phase signatures obtained from a telecentric DHM system input to machine learning algorithms classify cell lines of mesenchymal and epithelial morphologies with high accuracy, leading to sensitive and reproducible phenotypic profiling of cell lines.

A telecentric DHM system and a support vector machine-based classifier were developed to investigate the effects of cytokeratin 19 knockout on the morphology and phase features of MDA-MB-231 breast cancer cells.

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.