We present sensing time-lapse morphogenesis of living bone cells under micro-Fluidic Shear Stress (FSS) by digital holographic (DH) microscopy. To remove the effect of aberrations on quantitative measurement, we propose...

Platinum chemosensitivity detection plays a vital role during endometrial cancer treatment because chemotherapy responses have profound influences on patient's prognosis. Although several methods can be used to detect drug resistance characteristics, studies on detecting drug sensitivity based on dynamic and quantitative phase imaging of cancer cells are rare. In this study, digital holographic microscopy was applied to distinguish drug‐resistant and nondrug‐resistant endometrial cancer cells. Based on the reconstructed phase images, temporal evolutions of cell height (CH), cell projected area (CPA) and cell volume were quantitatively measured. The results show that change rates of CH and CPA were significantly different between drug‐resistant and nondrug‐resistant endometrial cancer cells. Furthermore, the results demonstrate that morphological characteristics have the potential to be utilized to distinguish the drug sensitivity of endometrial cancer cells, and it may provide new perspectives to establish optical methods to detect drug sensitivity and guide chemotherapy in endometrial cancer.

Digital holographic microscopy (DHM) as a label-free quantitative imaging tool has been widely used to investigate the morphology of living cells dynamically. In the offaxis DHM, the spatial filtering in the frequency spectrum of the hologram is vital to the quality of the reconstructed images. In this paper, we propose an adaptive spatial filtering approach based on convolutional neural networks (CNN) to automatically extracts the optimal shape of frequency components. For achieving robust and precise recognition performance, the net model is trained by using the tens of thousands of frequency spectrums with a variety of specimens and imaging conditions. The experimental results demonstrate that the trained network produce an adaptive spatial filtering window which can accurately select the frequency components of the object term and eliminate the frequency components of the interference terms, especially the coherent noise that overlaps with the object term in the spatial frequency domain. We find that the proposed approach has a fast, robust, and outstanding frequency filtering capability without any manual intervention and initial input parameters compared to previous techniques. Furthermore, the applicability of the proposed method in off-axis DHM for dynamic analysis is demonstrated by real-time monitoring the morphologic changes of living MLO-Y4 cells that are constantly subject to Fluid Shear Stress (FSS).

Cytoskeletons such as F-actin have different distributions in different cell parts and they are the cause of different degrees of cell collapse when the F-actin is disrupted. It is challenging to use conventional methods such as fluorescence microscopy and atomic force microscopy to conduct real-time and three-dimensional observations on the dynamic processes at different cell parts due to the slow measuring speed and the need for live-cell staining. In this study, the morphological variations of different bone cell parts caused by F-actin disruption are dynamically measured by using digital holographic microscopy (DHM). We separately analyze local parameters (cell height and cell width) and global parameters (cell projected area and cell volume) of cells to address variations of specific cell areas and quantify the changing process of the whole cell. We found significant differences in temporal variations of both local and global cell parameters between the cell body and cell process, which is consistent with the qualitative observation by fluorescence staining. Our study not only validates the unique ability of DHM to simultaneously investigate the dynamic process at different cell parts, but also provides sufficient experimental bases for exploring the mechanism for F-actin disruption.

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