Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation
文献类型：期刊论文
| 作者 | Ma, Jun1; Wang, Yixin2; An, Xingle3; Ge, Cheng4; Yu, Ziqi5; Chen, Jianan6; Zhu, Qiongjie7; Dong, Guoqiang7; He, Jian7; He, Zhiqiang8 |
| 刊名 | MEDICAL PHYSICS
 |
| 出版日期 | 2021-02-06 |
| 页码 | 14 |
| ISSN号 | 0094-2405 |
| 关键词 | COVID‐ 19 CT domain generalization few‐ shot learning knowledge transfer lung and infection segmentation |
| DOI | 10.1002/mp.14676 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| 出版者 | WILEY |
| WOS记录号 | WOS:000615528300001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16207]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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