Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound

Abstract

The sustained increase in new cases of COVID-19 across the world and potential for subsequent outbreaks call for new tools to assist health professionals with early diagnosis and patient monitoring. Growing evidence around the world is showing that lung ultrasound examination can detect manifestations of COVID-19 infection. Ultrasound imaging has several characteristics that make it ideally suited for routine use: small hand-held systems can be contained inside a protective sheath, making it easier to disinfect than X-ray or computed tomography equipment; lung ultrasound allows triage of patients in long term care homes, tents or other areas outside of the hospital where other imaging modalities are not available; and it can determine lung involvement during the early phases of the disease and monitor affected patients at bedside on a daily basis. However, some challenges still remain with routine use of lung ultrasound. Namely, current examination practices and image interpretation are quite challenging, especially for unspecialized personnel. This paper reviews how lung ultrasound (LUS) imaging can be used for COVID-19 diagnosis and explores different image processing methods that have the potential to detect manifestations of COVID-19 in LUS images. Then, the paper reviews how general lung ultrasound examinations are performed before addressing how COVID-19 manifests itself in the images. This will provide the basis to study contemporary methods for both segmentation and classification of lung ultrasound images. The paper concludes with a discussion regarding practical considerations of lung ultrasound image processing use and draws parallels between different methods to allow researchers to decide which particular method may be best considering their needs. With the deficit of trained sonographers who are working to diagnose the thousands of people afflicted by COVID-19, a partially or totally automated lung ultrasound detection and diagnosis tool would be a major asset to fight the pandemic at the front lines.

Keywords: COVID-19; classification; diagnosis; image processing; lung ultrasound; machine learning; segmentation.

FIGURE 1

(A): Shows the four types of lines found in LUS images. A-lines are shown in blue, B-lines are yellow, C-lines red, and the pleural line is green. (B): 14 anatomical scanning locations for LUS diagnosis. From left to right are scanning locations on the back [with the vertical paravertebral line, spine of shoulder blade (upper horizontal line) and interior angle of shoulder blade (lower horizontal line)], sides (showing the mid-axillary lines on the left and right sides and internipple line), and front of a torso (showing the internipple line).

FIGURE 2

A flow chart representing a normal work flow for LUS images processing. The flowchart has two parallel components, illustrating that typically the stages of image collection, processing, segmentation, and classifications are performed in a linear fashion. However, the parallel component is to illustrate that some neural network methods can be trained in order to handle the entire process (from collection to classification) as one black-box solution.