TY - JOUR T1 - Revealing architectural order with polarized light imaging and deep neural networks JF - bioRxiv DO - 10.1101/631101 SP - 631101 AU - Syuan-Ming Guo AU - Anitha Priya Krishnan AU - Jenny Folkesson AU - Ivan Ivanov AU - Bryant Chhun AU - Nathan Cho AU - Manuel Leonetti AU - Shalin B. Mehta Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/05/09/631101.abstract N2 - Architectural order across spatial and temporal scales is a defining characteristic of living systems. Polarization of light enables label-free imaging of sub-resolution order in diverse biological systems without perturbing their assembly dynamics or causing phototoxicity. However, identification of specific structures seen in these images has remained challenging. We report synergistic use of polarized light microscopy, reconstruction of complementary optical properties, and deep neural networks to identify ordered structures. We recover birefringence, orientation, brightfield, and degree of polarization contrasts simultaneously by using Stokes formalism to model image formation. We report computationally efficient U-Net architectures that exploit information in complementary contrasts and predict specific structures with high accuracy. We illustrate the performance of our models by predicting ordered F-actin and condensed DNA in morphological diverse components of a kidney tissue. Our open-source python software for reconstruction of optical properties and training the neural networks is available on GitHub. ER -