ArticleBiomedical optics express2020
Lymphatic vessel segmentation in optical coherence tomography by adding U-Net-based CNN for artifact minimization.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 8 citations in OpenAlex.
- Deep learning-based label-free imaging of lymphatics and aqueous veins in the eye using optical coherence tomography.Scientific reports · 2024Article
- Lymphography method based on time-autocorrelated optical coherence tomography.Biomedical optics express · 2022Article
- Human Umbilical Mesenchymal Stem Cell Xenografts Repair UV-Induced Photokeratitis in a Rat Model.Biomedicines · 2022Article
- In Vivo Longitudinal Tracking of Lymphangiogenesis and Angiogenesis in Cutaneous Melanoma Mouse Model Using Multifunctional Optical Coherence Tomography.JID innovations : skin science from molecules to population health · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lymphatic system branches throughout the body to transport bodily fluid and plays a key immune-response role. Optical coherence tomography (OCT) is an emerging technique for the noninvasive and label-free imaging of lymphatic capillaries utilizing low scattering features of the lymph fluid. Here, the proposed lymphatic segmentation method combines U-Net-based CNN, a Hessian vesselness filter, and a modified intensity-thresholding to search the nearby pixels based on the binarized Hessian mask. Compared to previous approaches, the method can extract shapes more precisely, and the segmented result contains minimal artifacts, achieves the dice coefficient of 0.83, precision of 0.859, and recall of 0.803.
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Registered trials
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