论文标题
使用深神经网络对经胸膜超声心动图图像质量的自动评估
Automated Assessment of Transthoracic Echocardiogram Image Quality Using Deep Neural Networks
论文作者
论文摘要
二维超声心动图中的标准视图已经建立了良好，但是获得的图像的质量高度依赖于操作员的技能，并主观评估。这项研究旨在通过定义一组新的特定领域质量指标来为超声心动图图像质量提供客观评估管道。因此，可以自动化图像质量评估以增强临床测量，解释和实时优化。我们已经开发了深层神经网络，用于对超声心动图框架的自动评估，这些评估是从11,262名成年患者中随机采样的。私有超声心动图数据集由33,784帧组成，以前在2010年至2020年之间获得。深度学习方法用于提取时空特征，并根据平均绝对错误评估了图像质量指标。我们的质量指标涵盖了解剖学和病理元素，以分别提供多元评估评分，以分别为解剖学可见性，清晰度，深度增益和预先理解性提供。
Standard views in two-dimensional echocardiography are well established but the quality of acquired images are highly dependent on operator skills and are assessed subjectively. This study is aimed at providing an objective assessment pipeline for echocardiogram image quality by defining a new set of domain-specific quality indicators. Consequently, image quality assessment can thus be automated to enhance clinical measurements, interpretation, and real-time optimization. We have developed deep neural networks for the automated assessment of echocardiographic frame which were randomly sampled from 11,262 adult patients. The private echocardiography dataset consists of 33,784 frames, previously acquired between 2010 and 2020. Deep learning approaches were used to extract the spatiotemporal features and the image quality indicators were evaluated against the mean absolute error. Our quality indicators encapsulate both anatomical and pathological elements to provide multivariate assessment scores for anatomical visibility, clarity, depth-gain and foreshortedness, respectively.
