Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation

doi:10.1002/mp.14676

	Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation
	Ma, Jun 1; Wang, Yixin 2; An, Xingle 3; Ge, Cheng 4; Yu, Ziqi 5; Chen, Jianan 6; Zhu, Qiongjie 7; Dong, Guoqiang 7; He, Jian 7; He, Zhiqiang 8; Cao, Tianjia 3; Zhu, Yuntao 9; Nie, Ziwei 9; Yang, Xiaoping 9
	2021-02-06
发表期刊	MEDICAL PHYSICS
ISSN	0094-2405
页码	14
摘要	Purpose Accurate segmentation of lung and infection in COVID-19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, for example, few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results Based on the state-of-the-art network, we provide more than 40 pretrained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. Conclusions To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.
关键词	COVID‐ 19 CT domain generalization few‐ shot learning knowledge transfer lung and infection segmentation
DOI	10.1002/mp.14676
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[11531005] ; National Natural Science Foundation of China[11971229]
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000615528300001
出版者	WILEY
引用统计	被引频次：126[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16208
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ma, Jun; Yang, Xiaoping
作者单位	1.Nanjing Univ Sci & Technol, Dept Math, Nanjing 210094, Peoples R China 2.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.China Elect Cloud Brain Tianjin Technol CO Ltd, Tianjin 300309, Peoples R China 4.Jiangsu Univ Technol, Inst Bioinformat & Med Engn, Changzhou 213001, Jiangsu, Peoples R China 5.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China 6.Univ Toronto, Dept Med Biophys, Toronto, ON M5G 1L7, Canada 7.Nanjing Univ, Med Sch, Affiliated Hosp, Dept Radiol,Nanjing Drum Tower Hosp, Nanjing 210008, Peoples R China 8.Lenovo Ltd, Beijing 100094, Peoples R China 9.Nanjing Univ, Dept Math, Nanjing 210093, Peoples R China
推荐引用方式 GB/T 7714	Ma, Jun,Wang, Yixin,An, Xingle,et al. Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation[J]. MEDICAL PHYSICS,2021:14.
APA	Ma, Jun.,Wang, Yixin.,An, Xingle.,Ge, Cheng.,Yu, Ziqi.,...&Yang, Xiaoping.(2021).Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.MEDICAL PHYSICS,14.
MLA	Ma, Jun,et al."Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation".MEDICAL PHYSICS (2021):14.