ArticleBiomedical optics express2019
Parallel deep neural networks for endoscopic OCT image segmentation.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- Longitudinal deep network for consistent OCT layer segmentation.Biomedical optics express · 2023Article
- Direct Visualization and Quantitative Imaging of Small Airway Anatomy In Vivo Using Deep Learning Assisted Diffractive OCT.IEEE transactions on bio-medical engineering · 2022Article
- Connectivity-based deep learning approach for segmentation of the epithelium inBiomedical optics express · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Tissue self-attention network for the segmentation of optical coherence tomography images on the esophagus.Biomedical optics express · 2021Article
- Structured layer surface segmentation for retina OCT using fully convolutional regression networks.Medical image analysis · 2021Article
- Deep OCT image compression with convolutional neural networks.Biomedical optics express · 2020Article
- Adversarial convolutional network for esophageal tissue segmentation on OCT images.Biomedical optics express · 2020Article
- Real-time colorectal cancer diagnosis using PR-OCT with deep learning.Theranostics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
We report parallel-trained deep neural networks for automated endoscopic OCT image segmentation feasible even with a limited training data set. These U-Net-based deep neural networks were trained using a modified dice loss function and manual segmentations of ultrahigh-resolution cross-sectional images collected by an 800 nm OCT endoscopic system. The method was tested on
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.