Improved U-Net and its application in COVID-19 image segmentation(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第8期
- Page:
- 1041-1048
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Improved U-Net and its application in COVID-19 image segmentation
- Author(s):
- GU Guohao; LONG Yingwen; JI Mingming
- School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
- Keywords:
- Keywords: U-Net corona virus disease 2019 image segmentation recurrent ResNet self-attention mechanism
- PACS:
- R318；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.08.022
- Abstract:
- Abstract: Computed tomography (CT) has become one of the most important steps in the detection of the corona virus disease 2019 (COVID-19). Aiming at the cumbersome problem of manually segmenting the ground-glass opacity area in the chest CT image, a U-shaped network model with recurrent ResNet and self-attention is proposed to realize the automatic segmentation in the lung CT images of COVID-19 patients and assist doctors in diagnosis. The improved U-Net with recurrent ResNet and self-attention is introduced to enhance the capture of feature information, thereby improving the accuracy of segmentation. The segmentation experiment on the public data set show that the Dice coefficient, sensitivity and specificity of the proposed algorithm reach 85.36%, 76.64% and 76.25%, respectively. Compared with other algorithms, the proposed algorithm has better segmentation performance.
Last Update: 2022-09-05