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.Comment: 20 pages, 8 figure