Revealing architectural order with quantitative label-free imaging and deep neural networks

Methods for imaging architecture of biological systems are currently not as scalable as genomic, transcriptomic, or proteomic technologies, because they rely on labels instead of intrinsic signatures. Label-free visualization of diverse biological structures is feasible with phase and polarization of light. However, distinguishing structures from information-dense label-free images is challenging. Recent advances in deep learning can distinguish structures from label-free images based on their shape. Here, we report joint use of polarization resolved imaging, reconstruction of optical properties, and deep neural networks for scalable analysis of complex structures. We reconstruct quantitative three-dimensional density, anisotropy, and orientation from polarization- and depth-resolved images. We report a computationally efficient vari-ant of U-Net architecture to predict 3D fluorescent structure from its density, anisotropy, and orientation. We evaluate the performance of our models by predicting anisotropic F-actin and isotropic nuclei. We report label-free prediction of myelination in human brain tissue sections and demonstrate the model’s ability to rescue inconsistent labeling. We anticipate that the proposed approach will enable quantitative analysis of architectural order across scales of organelles to tissues.