Deep learning-based holographic polarization microscopy

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 re...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2020-07
Main Authors: Liu, Tairan, de Haan, Kevin, Bai, Bijie, Rivenson, Yair, Luo, Yi, Wang, Hongda, Karalli, David, Fu, Hongxiang, Zhang, Yibo, FitzGerald, John, Ozcan, Aydogan
Format: Article
Language:English
Subjects:
Artificial neural networks
Birefringence
Deep learning
Diagnostic software
Diagnostic systems
Feature extraction
Field of view
Image reconstruction
Machine learning
Medical imaging
Microscopy
Neural networks
Optical design
Optical microscopy
Phase distribution
Polarization
Polarized light
Polarizers
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2007.00741