Abstract
Medical ultrasound technology is widely used in routine clinical applications such as disease diagnosis and treatment as well as other applications like real-time monitoring of human tongue shapes and motions as visual feedback in second language training. Due to the low-contrast characteristic and noisy nature of ultrasound images, it might require expertise for non-expert users to recognize tongue gestures. Manual tongue segmentation is a cumbersome, subjective, and error-prone task. Furthermore, it is not a feasible solution for real-time applications. In the last few years, deep learning methods have been used for delineating and tracking tongue dorsum. Deep convolutional neural networks (DCNNs), which have shown to be successful in medical image analysis tasks, are typically weak for the same task on different domains. In many cases, DCNNs trained on data acquired with one ultrasound device, do not perform well on data of varying ultrasound device or acquisition protocol. Domain adaptation is an alternative solution for this difficulty by transferring the weights from the model trained on a large annotated legacy dataset to a new model for adapting on another different dataset using fine-tuning. In this study, after conducting extensive experiments, we addressed the problem of domain adaptation on small ultrasound datasets for tongue contour extraction. We trained a U-net network comprises of an encoder-decoder path from scratch, and then with several surrogate scenarios, some parts of the trained network were fine-tuned on another dataset as the domain-adapted networks. We repeat scenarios from target to source domains to find a balance point for knowledge transfer from source to target and vice versa. The performance of new fine-tuned networks was evaluated on the same task with images from different domains.
Abstract (translated)
医学超声技术广泛应用于疾病诊断和治疗等常规临床应用，以及其他诸如实时监测人类舌头形状和运动作为第二语言训练中的视觉反馈等应用。由于超声图像的低对比度特性和噪声特性，非专家用户可能需要专业知识来识别舌头手势。手动分词是一项繁琐、主观和容易出错的任务。此外，对于实时应用来说，这不是一个可行的解决方案。近年来，人们采用深度学习的方法来描绘和跟踪舌背。深卷积神经网络（DCNNS）在医学图像分析任务中取得了成功，但在不同的领域中，对于相同的任务来说，其性能通常较弱。在许多情况下，对一个超声设备采集的数据进行培训的DCNN在不同超声设备或采集协议的数据上表现不佳。域适应是解决这一难题的另一种解决方案，方法是将权重从在大型带注释的遗留数据集上训练的模型转移到新模型，以便使用微调适应另一个不同的数据集。在这项研究中，在进行了大量的实验之后，我们解决了小超声波数据集在舌轮廓提取中的领域适应性问题。我们从零开始训练一个由编码器-解码器路径组成的U-NET网络，然后在几个代理场景中，训练网络的某些部分在另一个数据集上作为适应域的网络进行微调。我们重复从目标域到源域的场景，以找到从源域到目标域的知识转移平衡点，反之亦然。对来自不同域的图像在同一任务上的性能进行了评估。
URL
https://arxiv.org/abs/1906.04301
