Medical image segmentation; COVID-19; Challenge; deep learning; u-net
Résumé :
[en] 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.
Disciplines :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
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 ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience
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