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.
| Original language | English |
|---|---|
| Article number | 102605 |
| Journal | Medical Image Analysis |
| Volume | 82 |
| ISSN | 1361-8415 |
| DOIs | |
| Publication status | Published - 11.2022 |
Funding
We would like to thank all participants for contributing to the challenge and this paper. We would also like to thank Stephanie Harmon, Maxime Blain, Michael Kassin, Nicole Varble, and Amel Amalou who were part of the organizing committee. AH & JS would like to acknowledge the Luxembourg National Research Fund, FNR. Finally, we would like to thank NVIDIA for providing GPU prizes to the challenge winners. All authors contributed expertise and edited the contents of this article. The final manuscript was approved by all authors.
