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Joint segmentation and detection of COVID-19 via a sequential region generation network.

Authors
  • Wu, Jipeng1, 2, 3
  • Zhang, Shengchuan1, 2
  • Li, Xi4
  • Chen, Jie3, 5
  • Xu, Haibo6
  • Zheng, Jiawen1
  • Gao, Yue7
  • Tian, Yonghong3
  • Liang, Yongsheng3
  • Ji, Rongrong1, 2, 3
  • 1 Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, 361005, China. , (China)
  • 2 National Institute for Data Science in Health and Medicine, Xiamen University, 361005, China. , (China)
  • 3 Peng Cheng Laboratory, 518055, China. , (China)
  • 4 Peking University Shenzhen Hospital, 518038, China. , (China)
  • 5 School of Electronic and Computer Engineering, Peking University, 518055, China. , (China)
  • 6 Department of Radiology, Zhongnan hospital of Wuhan university, 430064, China. , (China)
  • 7 School of Software, Tsinghua University, 100084, China. , (China)
Type
Published Article
Journal
Pattern Recognition
Publisher
Elsevier
Publication Date
Oct 01, 2021
Volume
118
Pages
108006–108006
Identifiers
DOI: 10.1016/j.patcog.2021.108006
PMID: 34002101
Source
Medline
Keywords
Language
English
License
Unknown

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. © 2021 Published by Elsevier Ltd.

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