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Title: Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks
Authors: Pfister, Martin
Schützenberger, Kornelia
Pfeiffenberger, Ulrike
Messner, Alina
Chen, Zhe
Puchner, Stefan
Garhöfer, Gerhard
Schmetterer, Leopold
Gröschl, Martin
Werkmeister, René M.
Aranha dos Santos, Valentin
Keywords: Convolutional Neural Network
Optical Coherence Tomography
Issue Date: 2019
Source: Pfister, M., Schützenberger, K., Pfeiffenberger, U., Messner, A., Chen, Z., Aranha dos Santos, V., . . . Werkmeister, R. M. (2019). Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks. Biomedical Optics Express, 10(3), 1315-1328. doi:10.1364/BOE.10.001315
Series/Report no.: Biomedical Optics Express
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 10×10 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.
DOI: 10.1364/BOE.10.001315
Rights: © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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