ArticleBiomedical optics express2019
Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- High-resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study.Journal of biophotonics · 2024Article
- Automated detection and growth tracking of 3D bio-printed organoid clusters using optical coherence tomography with deep convolutional neural networks.Frontiers in bioengineering and biotechnology · 2023Article
- Automatic Segmentation of Laser-Induced Injury OCT Images Based on a Deep Neural Network Model.International journal of molecular sciences · 2022Article
- Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images.Annals of the New York Academy of Sciences · 2021Article
- Cutaneous optical coherence tomography for longitudinal volumetric assessment of intradermal volumes in a mouse model.Scientific reports · 2020Article
- Deep learning segmentation for optical coherence tomography measurements of the lower tear meniscus.Biomedical optics express · 2020Article
- Application of machine learning in ophthalmic imaging modalities.Eye and vision (London, England) · 2020Review
- Comparison of optical coherence tomography and high frequency ultrasound imaging in mice for the assessment of skin morphology and intradermal volumes.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a system for automatic determination of the intradermal volume of hydrogels based on optical coherence tomography (OCT) and deep learning. Volumetric image data was acquired using a custom-built OCT prototype that employs an akinetic swept laser at ~1310 nm with a bandwidth of 87 nm, providing an axial resolution of ~6.5 μm in tissue. Three-dimensional data sets of a 10 mm × 10 mm skin patch comprising the intradermal filler and the surrounding tissue were acquired. A convolutional neural network using a u-net-like architecture was trained from slices of 100 OCT volume data sets where the dermal filler volume was manually annotated. Using six-fold cross-validation, a mean accuracy of 0.9938 and a Jaccard similarity coefficient of 0.879 were achieved.
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Registered trials
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