A Survey on COVID-19 Lesion Segmentation Techniques from Chest CT Images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020690" target="_blank" >RIV/62690094:18450/23:50020690 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-981-99-2680-0_50" target="_blank" >http://dx.doi.org/10.1007/978-981-99-2680-0_50</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-99-2680-0_50" target="_blank" >10.1007/978-981-99-2680-0_50</a>
Alternative languages
Result language
angličtina
Original language name
A Survey on COVID-19 Lesion Segmentation Techniques from Chest CT Images
Original language description
The COVID-19 pandemic had a catastrophic effect on almost every country, with a reported 6 million deaths by 2022. It is caused by an RNA virus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To date, there have been five variants of SARS-CoV-2, namely alpha, beta, gamma, delta, and omicron. Each of these variants can potentially infect more and more people and are highly contagious. COVID-19 affects almost all body organs, but its pulmonary involvement is the greatest. Most of the reported deaths have been due to pneumonia. CT-Scan is crucial in understanding the patient’s lung condition during and post-COVID. Radiologists found that lung lesions like ground glass opacity (GGO), consolidations, etc., indicate pneumonia. By analyzing the spread of these lesions in the chest CT image of COVID-19-infected patients, physicians could determine the lung condition and prescribe suitable treatments. The traditional methods of analyzing lesions are prone to manual error and inter-observer variations. Developing an automated system for lesion segmentation is essential for disease diagnosis and prognosis. This study presents an in-depth survey of various lesion segmentation techniques. All the state-of-the-art methods covered in this review paper have been described in detail, including their methodology, dataset used, and performance metrics. This survey will help accelerate the research in COVID-19 lesion segmentation since it will provide detailed insight into the pros and cons of every paper included in this study. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Networks and Systems
ISBN
978-981-9926-79-4
ISSN
2367-3370
e-ISSN
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Number of pages
8
Pages from-to
567-574
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Singapur
Event location
Ropar
Event date
Dec 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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