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Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge.
Roth, Holger R; Xu, Ziyue; Tor-Díez, Carlos; Sanchez Jacob, Ramon; 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.
Afiliação
  • Roth HR; NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany. Electronic address: hroth@nvidia.com.
  • Xu Z; NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany.
  • Tor-Díez C; Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, WA, DC, USA.
  • Sanchez Jacob R; Division of Diagnostic Imaging and Radiology, Children's National Hospital, WA,DC, USA.
  • Zember J; Division of Diagnostic Imaging and Radiology, Children's National Hospital, WA,DC, USA.
  • Molto J; Division of Diagnostic Imaging and Radiology, Children's National Hospital, WA,DC, USA.
  • Li W; NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany.
  • Xu S; Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
  • Turkbey B; Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
  • Turkbey E; Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
  • Yang D; NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany.
  • Harouni A; NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany.
  • Rieke N; NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany.
  • Hu S; School of Computer Science and Engineering, Northwestern Polytechnical University, China.
  • Isensee F; Applied Computer Vision Lab, Helmholtz Imaging , Heidelberg, Germany; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Tang C; Lynbrook High School, San Jose, CA, USA.
  • Yu Q; Shanghai Jiao Tong University, China.
  • Sölter J; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg.
  • Zheng T; School of Informatics, Nagoya University, Japan.
  • Liauchuk V; Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus.
  • Zhou Z; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, China.
  • Moltz JH; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
  • Oliveira B; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; 2Ai - School of Technology, IPCA
  • Xia Y; School of Computer Science and Engineering, Northwestern Polytechnical University, China.
  • Maier-Hein KH; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
  • Li Q; Shanghai Jiao Tong University, China.
  • Husch A; Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany.
  • Zhang L; School of Informatics, Nagoya University, Japan.
  • Kovalev V; Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus.
  • Kang L; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, China.
  • Hering A; Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany.
  • Vilaça JL; 2Ai - School of Technology, IPCA, Barcelos, Portugal.
  • Flores M; NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany.
  • Xu D; NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany.
  • Wood B; Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
  • Linguraru MG; Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, WA, DC, USA; School of Medicine and Health Sciences, George Washington University, WA, DC, USA.
Med Image Anal ; 82: 102605, 2022 11.
Article em En | MEDLINE | ID: mdl-36156419
ABSTRACT
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pandemias / COVID-19 Limite: Humans Idioma: En Revista: Med Image Anal Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pandemias / COVID-19 Limite: Humans Idioma: En Revista: Med Image Anal Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2022 Tipo de documento: Article