Accurate and rapid evaluation of dynamic immune status is critical to determine therapeutic modalities for sepsis patients, which is impeded by the limitations of conventional diagnostic tools. Here, we employ refractive index tomography to quantitatively assess the immune status of human monocytes in a label-free manner. Measurement of refractive index tomograms enabled quantifications of three-dimensional morphological parameters, which revealed a clear increment in lipid droplets content and intracellular inhomogeneities as the septic stage progresses. We leveraged these observations to engineer a deep-learning-based algorithm that predicts the immune status of monocytes, showing over 99 % blind test accuracy.
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.
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