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

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 similar to 1310 nm with a bandwidth of 87 nm, providing an axial resolution of similar to 6.5 mu m in tissue. Three-dimensional data sets of a 10 mm x 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. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement