A Survey on COVID-19 Lesion Segmentation Techniques from Chest CT Images

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Survey on COVID-19 Lesion Segmentation Techniques from Chest CT Images

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Survey on COVID-19 Lesion Segmentation Techniques from Chest CT Images

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Networks and Systems

ISBN

978-981-9926-79-4

ISSN

2367-3370

e-ISSN

—

Počet stran výsledku

8

Strana od-do

567-574

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Singapur

Místo konání akce

Ropar

Datum konání akce

19. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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