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Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comp...

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Autores principales: Roth, Holger R., Xu, Ziyue, Diez, Carlos Tor, Jacob, Ramon Sanchez, Zember, Jonathan, Molto, Jose, Li, Wenqi, Xu, Sheng, Turkbey, Baris, Turkbey, Evrim, Yang, Dong, Harouni, Ahmed, Rieke, Nicola, Hu, Shishuai, Isensee, Fabian, Tang, Claire, Yu, Qinji, Sölter, Jan, Zheng, Tong, Liauchuk, Vitali, Zhou, Ziqi, Moltz, Jan Hendrik, Oliveira, Bruno, Xia, Yong, Maier-Hein, Klaus H., Li, Qikai, Husch, Andreas, Zhang, Luyang, Kovalev, Vassili, Kang, Li, Hering, Alessa, Vilaça, João L., Flores, Mona, Xu, Daguang, Wood, Bradford, Linguraru, Marius George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183044/
https://www.ncbi.nlm.nih.gov/pubmed/34100010
http://dx.doi.org/10.21203/rs.3.rs-571332/v1
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author Roth, Holger R.
Xu, Ziyue
Diez, Carlos Tor
Jacob, Ramon Sanchez
Zember, Jonathan
Molto, Jose
Li, Wenqi
Xu, Sheng
Turkbey, Baris
Turkbey, Evrim
Yang, Dong
Harouni, Ahmed
Rieke, Nicola
Hu, Shishuai
Isensee, Fabian
Tang, Claire
Yu, Qinji
Sölter, Jan
Zheng, Tong
Liauchuk, Vitali
Zhou, Ziqi
Moltz, Jan Hendrik
Oliveira, Bruno
Xia, Yong
Maier-Hein, Klaus H.
Li, Qikai
Husch, Andreas
Zhang, Luyang
Kovalev, Vassili
Kang, Li
Hering, Alessa
Vilaça, João L.
Flores, Mona
Xu, Daguang
Wood, Bradford
Linguraru, Marius George
author_facet Roth, Holger R.
Xu, Ziyue
Diez, Carlos Tor
Jacob, Ramon Sanchez
Zember, Jonathan
Molto, Jose
Li, Wenqi
Xu, Sheng
Turkbey, Baris
Turkbey, Evrim
Yang, Dong
Harouni, Ahmed
Rieke, Nicola
Hu, Shishuai
Isensee, Fabian
Tang, Claire
Yu, Qinji
Sölter, Jan
Zheng, Tong
Liauchuk, Vitali
Zhou, Ziqi
Moltz, Jan Hendrik
Oliveira, Bruno
Xia, Yong
Maier-Hein, Klaus H.
Li, Qikai
Husch, Andreas
Zhang, Luyang
Kovalev, Vassili
Kang, Li
Hering, Alessa
Vilaça, João L.
Flores, Mona
Xu, Daguang
Wood, Bradford
Linguraru, Marius George
author_sort Roth, Holger R.
collection PubMed
description Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.
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spelling pubmed-81830442021-06-08 Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge Roth, Holger R. Xu, Ziyue Diez, Carlos Tor Jacob, Ramon Sanchez Zember, Jonathan Molto, Jose Li, Wenqi Xu, Sheng Turkbey, Baris Turkbey, Evrim Yang, Dong Harouni, Ahmed Rieke, Nicola Hu, Shishuai Isensee, Fabian Tang, Claire Yu, Qinji Sölter, Jan Zheng, Tong Liauchuk, Vitali Zhou, Ziqi Moltz, Jan Hendrik Oliveira, Bruno Xia, Yong Maier-Hein, Klaus H. Li, Qikai Husch, Andreas Zhang, Luyang Kovalev, Vassili Kang, Li Hering, Alessa Vilaça, João L. Flores, Mona Xu, Daguang Wood, Bradford Linguraru, Marius George Res Sq Article Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020. American Journal Experts 2021-06-04 /pmc/articles/PMC8183044/ /pubmed/34100010 http://dx.doi.org/10.21203/rs.3.rs-571332/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Roth, Holger R.
Xu, Ziyue
Diez, Carlos Tor
Jacob, Ramon Sanchez
Zember, Jonathan
Molto, Jose
Li, Wenqi
Xu, Sheng
Turkbey, Baris
Turkbey, Evrim
Yang, Dong
Harouni, Ahmed
Rieke, Nicola
Hu, Shishuai
Isensee, Fabian
Tang, Claire
Yu, Qinji
Sölter, Jan
Zheng, Tong
Liauchuk, Vitali
Zhou, Ziqi
Moltz, Jan Hendrik
Oliveira, Bruno
Xia, Yong
Maier-Hein, Klaus H.
Li, Qikai
Husch, Andreas
Zhang, Luyang
Kovalev, Vassili
Kang, Li
Hering, Alessa
Vilaça, João L.
Flores, Mona
Xu, Daguang
Wood, Bradford
Linguraru, Marius George
Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge
title Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge
title_full Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge
title_fullStr Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge
title_full_unstemmed Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge
title_short Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge
title_sort rapid artificial intelligence solutions in a pandemic - the covid-19-20 lung ct lesion segmentation challenge
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183044/
https://www.ncbi.nlm.nih.gov/pubmed/34100010
http://dx.doi.org/10.21203/rs.3.rs-571332/v1
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