ArticleACS photonics2020
Deep Learning-Based Holographic Polarization Microscopy.
Article in ACS photonics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Polarized Light Microscopy for Quantitative Assessment of Sarcomere-Scale Structural Order in Cardiac Tissue.Journal of biophotonics · 2026Article
- FLIPs: Genetically encoded molecular biosensors for functional imaging of cell signaling by linear dichroism microscopy.Science advances · 2026Article
- Physical Interpretation of Diffractive Optical Networks for High-Dimensional Vortex Mode Sorting.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- On-Chip Polarization Light Microscopy.Biosensors · 2025Article
- A Novel Polarized Light Microscope for the Examination of Birefringent Crystals in Synovial Fluid.Gout, urate, and crystal deposition disease · 2024Article
- Off-axis digital lensless holographic microscopy based on spatially multiplexed interferometry.Journal of biomedical optics · 2024Article
- Capturing cell morphology dynamics with high temporal resolution using single-shot quantitative phase gradient imaging.Journal of biomedical optics · 2024Article
- Roadmap on computational methods in optical imaging and holography [invited].Applied physics. B, Lasers and optics · 2024Review
- Ptychographic lens-less birefringence microscopy using a mask-modulated polarization image sensor.Scientific reports · 2023Article
- Polarization-sensitive intensity diffraction tomography.Light, science & applications · 2023Article
- Deep learning-enabled virtual histological staining of biological samples.Light, science & applications · 2023Review
- Review
- Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization.Light, science & applications · 2022Article
- Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network.Light, science & applications · 2022Article
- Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry.Biosensors · 2022Article
- Comprehensive deep learning model for 3D color holography.Scientific reports · 2022Article
- Coal Wall and Roof Segmentation in the Coal Mine Working Face Based on Dynamic Graph Convolution Neural Networks.ACS omega · 2021Article
- Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data.Light, science & applications · 2021Article
- Multiscale Assay of Unlabeled Neurite Dynamics Using Phase Imaging with Computational Specificity.ACS sensors · 2021Article
- Calcium pyrophosphate crystal size and characteristics.Osteoarthritis and cartilage open · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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 inline lensfree 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, for example, monosodium urate and triamcinolone acetonide 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.