Title: Pretrained 2D U-Net models for COVID-19 CT Lung and Infection Segmentation

Type Software Ma, Jun, Wang, Yixin, An, Xingle, Ge, Cheng, Yu, Ziqi (2020): Pretrained 2D U-Net models for COVID-19 CT Lung and Infection Segmentation. Zenodo. Software. https://zenodo.org/record/3870441

Authors: Ma, Jun (Department of Mathematics, Nanjing University of Science and Technology) ; Wang, Yixin (Institute of Computing Technology, Chinese Academy of Sciences;University of Chinese Academy of Sciences) ; An, Xingle (China Electronics Cloud Brain (Tianjin) Technology CO., LTD) ; Ge, Cheng (Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology) ; Yu, Ziqi (Institute of Science and Technology for Brain-inspired Intelligence, Fudan University) ;

Links

• Item record in Zenodo
• Digital object URL

Summary

We provide 45 trained 2D U-Net baseline models for COVID-19 CT Lung and Infection Segmentation benchmark (https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark).

The implementation is based on nnU-Net that is an out-of-the-box segmentation tool for 3D biomedical image data. 

Instructions for how to use the models are provided at https://github.com/MIC-DKFZ/nnUNet

Ground truth can be download at http://doi.org/10.5281/zenodo.3757476

More information

• DOI: 10.5281/zenodo.3870441

Subjects

• COVID-19, Infection Segmentation, Lung

Dates

• Publication date: 2020
• Issued: May 31, 2020

Notes

Rights

• https://creativecommons.org/licenses/by/4.0/legalcode Creative Commons Attribution 4.0 International
• info:eu-repo/semantics/openAccess Open Access

Format

electronic resource

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