Jun Ma1, Yixin Wang2, Xingle An3, Cheng Ge4, Ziqi Yu5, Jianan Chen6, Qiongjie Zhu7, Guoqiang Dong7, Jian He7, Zhiqiang He8, Tianjia Cao3, Yuntao Zhu9, Ziwei Nie9, Xiaoping Yang9. 1. Department of Mathematics, Nanjing University of Science and Technology, Nanjing, 210094, P. R. China. 2. Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100190, P. R. China. 3. China Electronics Cloud Brain (Tianjin) Technology CO., Ltd, Tianjin, 300309, P. R. China. 4. Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, 213001, P. R. China. 5. Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, P. R. China. 6. Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada. 7. Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, P. R. China. 8. Lenovo Ltd., Beijing, 100094, P. R. China. 9. Department of Mathematics, Nanjing University, Nanjing, 210093, P. R. China.
Abstract
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.
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.
Authors: Arnab Kumar Mondal; Arnab Bhattacharjee; Parag Singla; A P Prathosh Journal: IEEE J Transl Eng Health Med Date: 2021-12-08 Impact factor: 3.316
Authors: Myeongkyun Kang; Kyung Soo Hong; Philip Chikontwe; Miguel Luna; Jong Geol Jang; Jongsoo Park; Kyeong Cheol Shin; Sang Hyun Park; June Hong Ahn Journal: J Korean Med Sci Date: 2021-02-01 Impact factor: 2.153
Authors: Qi Dou; Tiffany Y So; Meirui Jiang; Quande Liu; Varut Vardhanabhuti; Georgios Kaissis; Zeju Li; Weixin Si; Heather H C Lee; Kevin Yu; Zuxin Feng; Li Dong; Egon Burian; Friederike Jungmann; Rickmer Braren; Marcus Makowski; Bernhard Kainz; Daniel Rueckert; Ben Glocker; Simon C H Yu; Pheng Ann Heng Journal: NPJ Digit Med Date: 2021-03-29