Joint segmentation and detection of COVID-19 via a sequential region generation network. (October 2021)

• Record Type: Journal Article
• Authors: Wu, Jipeng; Xu, Haibo; Zhang, Shengchuan; Li, Xi; Chen, Jie; Zheng, Jiawen; Gao, Yue; Tian, Yonghong; Liang, Yongsheng; Ji, Rongrong
• Is Part Of: Pattern recognition. Volume 118(2021)
• Publication Date: 2021-10
• Subjects: COVID-19 -- Segmentation -- Detection -- Context enhancement -- Edge loss; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108006
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Joint segmentation and detection framework for COVID-19. Context enhancement module for semantic segmentation. Context enhancement at multiple resolutions. Optimize segmentation and detection results through post-processing. Obtained state-of-the-art results for COVID-19 segmentation and detection. Abstract: The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.