ArticleNPJ digital medicine2021
Synthetic polarization-sensitive optical coherence tomography by deep learning.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
13 citing papers in PubMed, 24 citations in OpenAlex.
- Polarisation-Sensitive Optical Coherence Tomography for Surgical Margin Evaluation and Diagnosis of Excised Canine Cutaneous and Subcutaneous Tumours.Veterinary and comparative oncology · 2026Article
- Label-free mid-infrared dichroism-sensitive photoacoustic microscopy for histostructural analysis of engineered heart tissues.Light, science & applications · 2026Article
- From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?Diagnostics (Basel, Switzerland) · 2025Review
- MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation.Scientific reports · 2025Article
- Evaluation of retinal pigment epithelium changes in serous pigment epithelial detachment using synthesized multi-contrast polarization-sensitive optical coherence tomography.Scientific reports · 2025Article
- Synthetic polarization-sensitive optical coherence tomography using contrastive unpaired translation.Scientific reports · 2024Article
- Inspiring a convergent engineering approach to measure and model the tissue microenvironment.Heliyon · 2024Review
- Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine.Journal of personalized medicine · 2023Review
- Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation.Biomedical optics express · 2023Article
- Synthesizing the degree of polarization uniformity from non-polarization-sensitive optical coherence tomography signals using a neural network.Biomedical optics express · 2023Article
- Automated analysis framework forBiomedical optics express · 2023Article
- Applications of Optical Coherence Tomography in the Diagnosis of Enamel Defects.Diagnostics (Basel, Switzerland) · 2022Article
- Esophageal optical coherence tomography image synthesis using an adversarially learned variational autoencoder.Biomedical optics express · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.
Identifiers
What OpenQuestion holds
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.