Current Advances in Computational Lung Ultrasound Imaging: A Review.

Abstract

In the field of biomedical imaging, ultrasonography has become common practice, and used as an important auxiliary diagnostic tool with unique advantages, such as being non-ionising and often portable. This article reviews the state of the art in medical ultrasound image processing and in particular its applications in the examination of the lungs. First, we briefly introduce the basis of lung ultrasound examination. We focus on (i) the characteristics of lung ultrasonography, and (ii) its ability to detect a variety of diseases through the identification of various artefacts exhibiting on lung ultrasound images. We group medical ultrasound image computing methods into two categories: (1) model-based methods, and (2) data-driven methods. We particularly discuss inverse problem-based methods exploited in ultrasound image despeckling, deconvolution, and line artefacts detection for the former, whilst we exemplify various works based on deep/machine learning, which exploit various network architectures through supervised, weakly supervised, and unsupervised learning for the data-driven approaches.