Automated segmentation of dermal fillers in OCT images of mice using convolutional neural networks.

Biomed Opt Express 2019 Mar 19;10(3):1315-1328. Epub 2019 Feb 19.

Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.

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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http://dx.doi.org/10.1364/BOE.10.001315DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6420291PMC
March 2019
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