Rapid identification of infectious pathogens can save lives and mitigate healthcare expenses. Yet the current turnaround time for microbial identification typically exceeds 24 hours, as the common methods require the cultivation of millions or more bacteria to detect the collective signal. In this study, we propose a hybrid framework of quantitative phase imaging and artificial neural network to facilitate rapid identification at an individual-cell level. Specifically, three-dimensional images of refractive index were acquired for individual bacteria, and an optimized artificial neural network determined the species based on the three-dimensional morphologies, securing 82.5% blind test accuracy at an individual-cell level.
We present a deep learning approach for the rapid resolution enhancement of optical diffraction tomography. Once our three-dimensional U-net-based convolutional neural network learns an image translation between raw tomograms and total-variation-regularized tomograms, the trained network can fill in the missing cone of a measured refractive index tomogram and improve its resolution within seconds. We demonstrate the feasibility and generalizability of our approach on various biological samples, including bacteria, WBC, and NIH3T3.
Cell instance segmentation is a critical task to perform for the quantitative analysis of 3D live-cell images. Existing studies mostly apply a region proposal-based approach to instance segmentation of microscopy images. However, they often fail to detect cells in 3D live-cell images, which have complicated and heterogeneous shapes, often closely linked to the neighborhood cells. A different approach based on point proposal methods is more robust in handling complex shapes than the box proposal. These methods take an image and a proposed point in the form of its location (x; y) as input and generate a mask for an object that includes the point. They also show that the model can improve the prediction by utilizing negative point proposals chosen from false-positive areas. In this paper, we propose a novel cell instance segmentation approach based on point proposal for 3D cell imaging. Different from existing work, however, our model utilizes the nuclei of cells as point proposal and employ them as positive and negative point proposals. We constructed the 3D NIH3T3 dataset for training and evaluation, and examine the proposed model qualitatively on three independently gathered cells; HeLa, A549, and MDA-MB-231. Our model exhibits superior quantitative results; moreover, compared to previous methods, it properly predicts cell lines, which are not even well-annotated during training.
Rapid, label-free, volumetric, and automated assessment in microscopy is necessary to assess the dynamic interactions between lymphocytes and their targets through the immunological synapse (IS) and the relevant immunological functions. However, attempts to realize the automatic tracking of IS dynamics have been stymied by the limitations of imaging techniques and computational analysis methods. Here, we demonstrate the automatic three-dimensional IS tracking by combining optical diffraction tomography and deep-learning-based segmentation. The proposed approach enables quantitative spatiotemporal analyses of IS regarding morphological and biochemical parameters related to its protein densities, offering a novel complementary method to fluorescence microscopy for studies in immunology.
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