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.

Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification

“…This makes it comparable to other CT imaging tools with its cost being significantly lower than those of the other two solutions. Moreover, LUS is radiation-free, easier to disinfect, and can be repeated even with small time intervals between two observations, while the same is not true for the other methodologies[ 7 , 15 ]. However, it has certain drawbacks, such as operator dependency and high expertise requirements, resulting in underutilisation, and it may not be useful for Covid-19 asymptomatic patients.…”

Automated Skin Lesion Classification on Ultrasound Images

“…This requirement is usually met in the case of "grand challenges" using popular lesion types, where anyone can test their algorithm on publicly available datasets. Recent examples of the use of DL in ultrasound-based lesion diagnosis are to be found in the classification of thyroid nodules [27,28], breast lesion differentiation [24] and lung ultrasound for the detection of COVID-19-associated lesions [36].…”

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

“…They achieved 95%, 61%, and 90% in terms of accuracy, specificity, and sensitivity, respectively. In [ 70 ], the authors used 2,392,963 frames extracted from 64 videos. These videos were aggregated with three different categories (COVID-19, healthy, and pneumonia).…”

“…We could notice the following: (1) only a few studies utilized ultrasound for COVID-19 detection from the previous table; (2) 80% of the studies used DL and pretrained models to classify the images; (3) studies extracted image frames from ultrasound videos; and (4) the best performance was obtained when using the pretrained model VGG followed by hidden layers trained on a large number of frames [ 70 ].…”

“…However, few US datasets are available. For example, in [ 70 ], the authors aggregated a dataset of 64 videos that were divided into 39 videos of COVID-19 and 15 videos of pneumonia, and 12 videos for healthy patients. Another dataset available at ( , access date: 17 February 2021; , access date: 16 February 2021) includes 1101 ultrasound images and is categorized as 650 images for COVID-19, 276 for bacterial pneumonia, and 171 for healthy cases.…”