期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 9, 页码 6528-6538出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3059023
关键词
COVID-19; Lesions; Computed tomography; Three-dimensional displays; Lung; Feature extraction; Image segmentation; Conditional random field; COVID-19; data augmentation; deep network; lung lesions segmentation
类别
资金
- National Natural Science Foundation of China [61701022]
- National Key Research and Development Program [2017YFB1002804, 2017YFB1401203]
- Beijing Natural Science Foundation [7182158]
- Fundamental Research Funds for the Central Universities [FRF-DF-20-05]
- Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing
This article presents a new automatic segmentation method for lung lesions from COVID-19 CT images based on a 3-D network, which was evaluated through experiments to have high performance.
Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this article provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: First, the proposed region of interest extraction implements patch mechanism strategy to satisfy the applicability of 3-D network and remove irrelevant background. Second, 3-D network is established to extract spatial features, where 3-D attention model promotes network to enhance target area. Then, to improve the convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and conditional random field are applied to realize data resampling and binary segmentation. This method was assessed with some comparative experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.
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