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.

使用超声波剪切波弹性成像技术正确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（阈值方法和深度学习方法）来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，用于以有效时间的方式弱注释未标记的数据集来训练深度神经网络。使用阈值方法提出了用于训练的分割标签，并由人类专家阅读器通过目视检查进行验证。我们通过仅检查中心 A 线的分段来减少决策模糊性。这种弱注释方法有助于在 2 小时内验证 1000 多个分割标签。在这个弱标记数据集上设计并训练了一个分割整个二维图像的轻量级深度神经网络。平均超过六倍的交叉验证，阈值方法的分割精度为 57%，神经网络的分割精度为 78%。特别是，该网络更擅长找到远端皮肤边缘，这是皮肤分割的主要挑战。这两种算法均已公开发布，以帮助未来在皮肤表征和弹性成像方面的应用。