Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound
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 manifestatio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2021.612561/full |
| _version_ | 1818950067977977856 |
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| author | Conor McDermott Maciej Łącki Ben Sainsbury Jessica Henry Mihail Filippov Carlos Rossa |
| author_facet | Conor McDermott Maciej Łącki Ben Sainsbury Jessica Henry Mihail Filippov Carlos Rossa |
| author_sort | Conor McDermott |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T09:12:42Z |
| format | Article |
| id | doaj.art-8ae26dfde2de47a582078a51458e5fda |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-12-20T09:12:42Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-8ae26dfde2de47a582078a51458e5fda2022-12-21T19:45:31ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2021-03-01410.3389/fdata.2021.612561612561Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung UltrasoundConor McDermott0Maciej Łącki1Ben Sainsbury2Jessica Henry3Mihail Filippov4Carlos Rossa5Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON, CanadaFaculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON, CanadaMarion Surgical, Toronto, ON, CanadaMarion Surgical, Toronto, ON, CanadaMarion Surgical, Toronto, ON, CanadaFaculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON, CanadaThe 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.https://www.frontiersin.org/articles/10.3389/fdata.2021.612561/fullCOVID-19lung ultrasoundimage processingmachine learningdiagnosissegmentation |
| spellingShingle | Conor McDermott Maciej Łącki Ben Sainsbury Jessica Henry Mihail Filippov Carlos Rossa Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound Frontiers in Big Data COVID-19 lung ultrasound image processing machine learning diagnosis segmentation |
| title | Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound |
| title_full | Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound |
| title_fullStr | Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound |
| title_full_unstemmed | Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound |
| title_short | Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound |
| title_sort | sonographic diagnosis of covid 19 a review of image processing for lung ultrasound |
| topic | COVID-19 lung ultrasound image processing machine learning diagnosis segmentation |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2021.612561/full |
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