Ultrasound is fast becoming an inevitable diagnostic tool for regular and continuous monitoring of the lung with the recent outbreak of COVID-19. In this work, a novel approach is presented to extract acoustic propagation-based features to automatically highlight the region below pleura, which is an important landmark in lung ultrasound (LUS). Subsequently, a multichannel input formed by using the acoustic physics-based feature maps is fused to train a neural network, referred to as LUSNet, to classify the LUS images into five classes of varying severity of lung infection to track the progression of COVID-19. In order to ensure that the proposed approach is agnostic to the type of acquisition, the LUSNet, which consists of a U-net architecture is trained in an unsupervised manner with the acoustic feature maps to ensure that the encoder-decoder architecture is learning features in the pleural region of interest. A novel combination of the U-net output and the U-net encoder output is employed for the classification of severity of infection in the lung. A detailed analysis of the proposed approach on LUS images over the infection to full recovery period of ten confirmed COVID-19 subjects shows an average five-fold cross-validation accuracy, sensitivity, and specificity of 97%, 93%, and 98% respectively over 5000 frames of COVID-19 videos. The analysis also shows that, when the input dataset is limited and diverse as in the case of COVID-19 pandemic, an aided effort of combining acoustic propagation-based features along with the gray scale images, as proposed in this work, improves the performance of the neural network significantly and also aids the labelling and triaging process.
翻译:随着最近COVID-19的爆发,超声波正在迅速成为定期和持续监测肺部的一个不可避免的诊断工具。在这项工作中,提出了一种新颖的方法,以提取基于声学的传播功能,自动突出胸膜下的区域,这是肺部超声波(LUS)的一个重要里程碑。随后,利用声学物理特征图形成的多通道投入被结合成一个神经网络,称为LUSNet,将LUS图像分为五类,其肺部感染严重程度不同,以跟踪COVID-19的演变。为了确保拟议的方法能够对采购类型即LUSNet进行分解,从而自动地突出胸膜下的区域,这是肺部超声波(LUSNet)下的一个重要里程碑。随后,使用声学物理特征图(LUS)绘制了一个多通道输入器,将LUS图像分为五类不同程度的肺部感染程度,对LUS-19图像的拟议方法进行了详细分析,对LUS-19级血压的精确度进行了未受精度进行了培训,对98-19级图像进行了精确度分析,同时,对98-19级的内位图像进行了平均速度分析,并全面恢复了CUVI。