In this article, we present a novel method for line artifacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a nonconvex regularization problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artifacts. Despite being nonconvex, the proposed technique is guaranteed to convergence through our proposed Cauchy proximal splitting (CPS) method, and accurately identifies both horizontal and vertical line artifacts in LUS images. To reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method, and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients.

中文翻译：

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通过非凸正则化检测COVID-19患者的肺部超声图像中的伪影

在本文中，我们提出了一种在COVID-19患者的肺部超声（LUS）图像中定量线伪影的新方法。我们将此公式化为一个非凸正则化问题，涉及稀疏性增强，基于柯西的罚函数和Radon逆变换。我们在Radon变换域中采用了一种简单的局部最大值检测技术，与线伪影的已知临床定义相关联。尽管不是凸面的，但通过我们提出的柯西近端分割（CPS）方法，可以保证所提出的技术收敛，并且可以准确地识别LUS图像中的水平和垂直线条伪影。为了减少错误和遗漏的检测次数，我们的方法包括两阶段验证机制，该机制在Radon和图像域中均执行。