Automated Segmentation of Dermal Fillers in OCT Images of Mice Using Convolutional Neural Networks

Overview

Journal Biomed Opt Express

Specialty Radiology

Date 2019 Mar 21

PMID 30891348

Citations 8

Authors

Martin Pfister

Kornelia Schutzenberger

Ulrike Pfeiffenberger

Alina Messner

Zhe Chen

Valentin Aranha Dos Santos

Stefan Puchner

Gerhard Garhofer

Leopold Schmetterer

Martin Groschl

Rene M Werkmeister

Affiliations

Soon will be listed here.

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 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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