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

Journal

FRONTIERS IN BIG DATA
Volume 4, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fdata.2021.612561

Keywords

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

Funding

  1. Natural Sciences and Engineering Research Council of Canada (NSERC) [ALLRP 550307-20]
  2. Cette recherche a ete menee en collaboration avec Marion Surgical et a ete financee par le Conseil de recherches en sciences naturelles et en genie du Canada (CRSNG) [ALLRP 550307-20]
  3. Marion Surgical

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The increasing cases of COVID-19 worldwide highlight the need for tools to aid in early diagnosis and monitoring of patients. Lung ultrasound examination has been shown to be effective in detecting COVID-19 manifestations and is suited for routine use due to its portable and disinfectable nature. Challenges still remain with image interpretation, but the use of lung ultrasound imaging shows promise in diagnosing and monitoring COVID-19 cases.
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.

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