Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Department of Dermatology, Duke University Medical Center, Durham, NC, USA.
Ultrason Imaging. 2021 Jul;43(4):167-174. doi: 10.1177/01617346211014138. Epub 2021 May 11.
Correctly calculating skin stiffness with ultrasound shear wave elastography techniques requires an accurate measurement of skin thickness. We developed and compared two algorithms, a thresholding method and a deep learning method, to measure skin thickness on ultrasound images. Here, we also present a framework for weakly annotating an unlabeled dataset in a time-effective manner to train the deep neural network. Segmentation labels for training were proposed using the thresholding method and validated with visual inspection by a human expert reader. We reduced decision ambiguity by only inspecting segmentations at the center A-line. This weak annotation approach facilitated validation of over 1000 segmentation labels in 2 hours. A lightweight deep neural network that segments entire 2D images was designed and trained on this weakly-labeled dataset. Averaged over six folds of cross-validation, segmentation accuracy was 57% for the thresholding method and 78% for the neural network. In particular, the network was better at finding the distal skin margin, which is the primary challenge for skin segmentation. Both algorithms have been made publicly available to aid future applications in skin characterization and elastography.
正确地利用超声剪切波弹性成像技术计算皮肤硬度需要精确测量皮肤厚度。我们开发并比较了两种算法，一种是阈值法，另一种是深度学习法，以测量超声图像上的皮肤厚度。在这里，我们还提出了一个框架，以便以有效的方式对未标记的数据集进行弱注释，从而训练深度神经网络。使用阈值法提出了用于训练的分割标签，并通过人工专家读者的视觉检查进行了验证。我们通过仅检查中心 A 线处的分割来减少决策的模糊性。这种弱注释方法可以在 2 小时内验证超过 1000 个分割标签。设计了一个轻量级的深度神经网络，可以对整个 2D 图像进行分割，并在这个弱标记数据集上进行训练。在六次交叉验证的平均值中，阈值法的分割准确率为 57%，神经网络的分割准确率为 78%。特别是，该网络在寻找皮肤远端边界方面表现更好，这是皮肤分割的主要挑战。这两种算法都已公开发布，以帮助未来在皮肤特征描述和弹性成像方面的应用。
