Ultrasound imaging is a commonly used modality for several diagnostic and therapeutic procedures. However, the diagnosis by ultrasound relies heavily on the quality of images assessed manually by sonographers, which diminishes the objectivity of the diagnosis and makes it operator-dependent. The supervised learning-based methods for automated quality assessment require manually annotated datasets, which are highly labour-intensive to acquire. These ultrasound images are low in quality and suffer from noisy annotations caused by inter-observer perceptual variations, which hampers learning efficiency. We propose an UnSupervised UltraSound image Quality assessment Network, US2QNet, that eliminates the burden and uncertainty of manual annotations. US2QNet uses the variational autoencoder embedded with the three modules, pre-processing, clustering and post-processing, to jointly enhance, extract, cluster and visualize the quality feature representation of ultrasound images. The pre-processing module uses filtering of images to point the network's attention towards salient quality features, rather than getting distracted by noise. Post-processing is proposed for visualizing the clusters of feature representations in 2D space. We validated the proposed framework for quality assessment of the urinary bladder ultrasound images. The proposed framework achieved 78% accuracy and superior performance to state-of-the-art clustering methods.

翻译：超声成像是一种常用于多种诊断和治疗流程的模态。然而，超声诊断高度依赖于超声技师手动评估的图像质量，这削弱了诊断的客观性，并使其依赖于操作者。基于监督学习的自动质量评估方法需要人工标注的数据集，而这类数据集的获取极其费时费力。这些超声图像质量较低，且由于观察者间感知差异导致的标注噪声进一步损害了学习效率。我们提出了一种无监督超声图像质量评估网络US2QNet，该网络消除了人工标注的负担与不确定性。US2QNet采用变分自编码器，并嵌入预处理、聚类和后处理三个模块，以协同增强、提取、聚类和可视化超声图像的质量特征表示。预处理模块通过图像滤波将网络注意力聚焦于显著质量特征，避免被噪声干扰。后处理模块则用于在二维空间中可视化特征表示的聚类结果。我们在膀胱超声图像质量评估任务上验证了所提框架，其准确率达78%，性能优于当前最先进的聚类方法。