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疫情中的快速人工智能解决方案——COVID-19-20肺部CT病变分割挑战赛

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

作者信息

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

机构信息

NVIDIA, Bethesda, MD, USA.

Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA.

出版信息

Res Sq. 2021 Jun 4:rs.3.rs-571332. doi: 10.21203/rs.3.rs-571332/v1.

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.

摘要

用于胸部计算机断层扫描(CT)中自动检测和量化新型冠状病毒肺炎(COVID-19)病变的人工智能(AI)方法可能在该疾病的监测和管理中发挥重要作用。我们组织了一项国际挑战赛,用于开发和比较针对此任务的AI算法,并提供公共数据和最先进的基准方法予以支持。获得委员会认证的放射科医生对来自两个来源(A和B)的295张公共图像进行注释,用于算法训练(n = 199,来源A)、验证(n = 50,来源A)和测试(n = 23,来源A;n = 23,来源B)。共有1096个注册团队,其中分别有225个和98个完成了验证和测试阶段。该挑战赛表明,不同团队可以快速设计出AI模型,这些模型有潜力测量疾病或促进及时且针对患者的干预措施。本文概述了2020年COVID-19肺部CT病变分割挑战赛及其主要成果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba6d/8183044/1afbebcfb583/nihpp-rs571332v1-f0001.jpg

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