Pfister Martin, Schützenberger Kornelia, Pfeiffenberger Ulrike, Messner Alina, Chen Zhe, Dos Santos Valentin Aranha, Puchner Stefan, Garhöfer Gerhard, Schmetterer Leopold, Gröschl Martin, Werkmeister René M
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
Christian Doppler Laboratory for Ocular and Dermal Effects of Thiomers, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
Biomed Opt Express. 2019 Feb 19;10(3):1315-1328. doi: 10.1364/BOE.10.001315. eCollection 2019 Mar 1.
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.
我们提出了一种基于光学相干断层扫描（OCT）和深度学习自动测定水凝胶皮内体积的系统。使用定制的OCT原型采集体积图像数据，该原型采用波长约为1310 nm、带宽为87 nm的非运动扫描激光器，在组织中提供约6.5μm的轴向分辨率。获取了包含皮内填充物和周围组织的10 mm×10 mm皮肤贴片的三维数据集。使用类似U-net架构的卷积神经网络，从100个OCT体积数据集的切片中进行训练，其中皮内填充物体积是手动标注的。使用六折交叉验证，平均准确率达到0.9938，杰卡德相似系数达到0.879。
