An Effective Deep Neural Network for Lung Lesions Segmentation From COVID-19 CT Images
Ieee Transactions on Industrial Informatics
; 17(9):6528-6538, 2021.
Article
in English
| Web of Science | ID: covidwho-1307656
ABSTRACT
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Type of study:
Experimental Studies
Language:
English
Journal:
Ieee Transactions on Industrial Informatics
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS