Your browser doesn't support javascript.
COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.
Ma, Lu; Song, Shuni; Guo, Liting; Tan, Wenjun; Xu, Lisheng.
  • Ma L; School of Science Northeastern University Shenyang China.
  • Song S; Guangdong Peizheng College Guangzhou China.
  • Guo L; College of Medicine and Biological Information Engineering Northeastern University Shenyang China.
  • Tan W; School of Computer Science and Engineering Northeastern University Shenyang China.
  • Xu L; Key Laboratory of Medical Image Computing Ministry of Education Shenyang Liaoning China.
Int J Imaging Syst Technol ; 33(1): 6-17, 2023 Jan.
Article in English | MEDLINE | ID: covidwho-2242952
ABSTRACT
Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content-aware pre-activated residual UNet (CAPA-ResUNet), was proposed for segmenting COVID-19 lesions from CT slices. In this network, the pre-activated residual block was used for down-sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID-19 Lung CT Lesion Segmentation Challenge-2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA-ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content-aware residual UNet (CARes-UNet). The code is available at https//github.com/malu108/LungInfectionSeg.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Int J Imaging Syst Technol Year: 2023 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Int J Imaging Syst Technol Year: 2023 Document Type: Article