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

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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.
Funding Information
  • Austrian Federal Ministry for Digital and Economic Affairs
  • Christian Doppler Forschungsgesellschaft
  • Croma-Pharma GmbH
  • Österreichische Nationalstiftung für Forschung, Technologie und Entwicklung

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