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|Title:||Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks||Authors:||Pfister, 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.||URI:||https://hdl.handle.net/10356/86177
|DOI:||http://dx.doi.org/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|
|Appears in Collections:||LKCMedicine Journal Articles|
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