Deep learning-based holographic polarization microscopy

Abstract

Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected from two or more light paths in different states of polarization, which lead to relatively complex optical designs, high system costs or experienced technicians being required. Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase recovered hologram, while only requiring the addition of one polarizer/analyzer pair to an existing holographic imaging system. Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized light microscope (SCPLM). Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution. To demonstrate the efficacy of this method, we tested it by imaging various birefringent samples including e.g., monosodium urate (MSU) and triamcinolone acetonide (TCA) crystals. Our method achieves similar results to SCPLM both qualitatively and quantitatively, and due to its simpler optical design and significantly larger field-of-view, this method has the potential to expand the access to polarization microscopy and its use for medical diagnosis in resource limited settings.

arXiv comment20 pages, 8 figures

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• Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
• Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
• Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
• Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
• Lensless polarimetric coded ptychography for high-resolution, high-throughput gigapixel birefringence imaging on a chip
• Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning
• eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging
• Tensorial tomographic Fourier Ptychography with applications to muscle tissue imaging