[论文解读] An Approach Towards Physics Informed Lung Ultrasound Image Scoring Neural Network for Diagnostic Assistance in COVID-19

Mahesh Raveendranatha Panicker, Yale Tung‐Chen+5 more|arXiv (Cornell University)|Jun 13, 2021

COVID-19 diagnosis using AI参考文献 26被引用 5

一句话总结

该论文提出LUSNet，一种融合基于声波传播特征与灰度肺部超声（LUS）图像的物理信息深度学习框架，用于对五级严重程度的COVID-19肺部感染进行分类。通过在特征图上以无监督方式训练的U-Net架构，该模型在10名确诊COVID-19患者采集的5,000帧LUS图像上进行交叉验证，准确率达到97%，灵敏度为93%，特异性为98%。

ABSTRACT

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.

研究动机与目标

• 开发一种稳健的、基于物理信息的神经网络，用于对COVID-19患者肺部超声（LUS）图像进行自动评分。
• 通过将基于声波传播的特征与标准灰度LUS图像相结合，提升在低数据量条件下的诊断性能。
• 利用U-Net编码器-解码器架构在滑膜区域实现无监督特征学习。
• 通过将LUS图像分类为五级肺部感染严重程度，支持临床分诊与疾病进展监测。
• 通过利用基于物理的特征图，确保模型在不同采集设备和协议下的泛化能力。

提出的方法

• 从LUS图像中提取基于声波传播的特征，以突出显示滑膜区域，这是关键的解剖标志。
• 构建多通道输入，将灰度LUS图像与基于物理的特征图结合，以丰富输入表征。
• 使用声波特征图以无监督方式训练基于U-Net的神经网络（LUSNet），以学习滑膜区域的特征。
• 融合U-Net编码器与解码器的输出，生成用于分类的综合特征表征。
• 利用融合后的特征表征将LUS图像分类为五级肺部感染严重程度。
• 在10名确诊COVID-19患者从感染到康复期间采集的5,000帧LUS图像上应用五折交叉验证。

实验结果

研究问题

• RQ1 基于物理信息的声波传播特征是否能提升深度学习模型在低数据量LUS分类中的性能？
• RQ2 在声波特征图上采用无监督U-Net训练策略，在识别滑膜区域异常方面效果如何？
• RQ3 将灰度图像与基于物理的特征结合，能在多大程度上提升在多样化LUS采集环境下的分类准确率与鲁棒性？
• RQ4 该方法是否能在标注数据有限的临床环境中支持可靠的分诊与疾病进展监测？
• RQ5 在真实世界中异质性LUS视频数据中，模型在不同肺部感染严重程度水平下的表现如何？

主要发现

• 所提出的LUSNet在10名确诊COVID-19患者采集的5,000帧LUS图像上，五折交叉验证准确率达到97%。
• 记录到93%的灵敏度与98%的特异性，表明在检测感染肺部区域方面表现优异。
• 将基于物理的声波特征与灰度图像结合，显著提升了模型性能，优于仅使用灰度图像的基线模型。
• 在声波特征图上进行无监督预训练，即使在标注数据有限的情况下也能实现有效的特征学习。
• 由于采用了基于物理信息的特征提取，模型对LUS采集设备和协议的差异表现出强鲁棒性。
• 融合U-Net编码器与解码器输出，通过捕捉局部与上下文特征，提升了分类性能。