Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System

Runmin CONG, Yumo ZHANG, Ning YANG, Haisheng LI, Xueqi ZHANG, Ruochen LI, Zewen CHEN, Yao ZHAO, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

47 Citations (Scopus)

Abstract

The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. Thanks to the development of deep learning technology, some deep learning solutions for lung infection segmentation have been proposed. However, due to the scattered distribution, complex background interference and blurred boundaries, the accuracy and completeness of the existing models are still unsatisfactory. To this end, we propose a boundary guided semantic learning network (BSNet) in this paper. On the one hand, the dual-branch semantic enhancement module that combines the top-level semantic preservation and progressive semantic integration is designed to model the complementary relationship between different high-level features, thereby promoting the generation of more complete segmentation results. On the other hand, the mirror-symmetric boundary guidance module is proposed to accurately detect the boundaries of the lesion regions in a mirror-symmetric way. Experiments on the publicly available dataset demonstrate that our BSNet outperforms the existing state-of-the-art competitors and achieves a real-time inference speed of 44 FPS. The code and results of our BSNet can be found from the link of https://github.com/rmcong/BSNet.
Original languageEnglish
Pages (from-to)376-386
Number of pages11
JournalIEEE Transactions on Consumer Electronics
Volume68
Issue number4
Early online date9 Sept 2022
DOIs
Publication statusPublished - Nov 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1975-2011 IEEE.

Funding

This work was supported in part by the National Key Research and Development Program of China under Grant 2021ZD0112100; in part the Beijing Nova Program under Grant Z201100006820016; in part by the National Natural Science Foundation of China under Grant 62002014, Grant U1936212, Grant 62120106009, and Grant 61877002; in part by the Beijing Natural Science Foundation under Grant 4222013; in part by the Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA); in part by the Hong Kong GRFRGC General Research Fund under Grant 11209819 (CityU 9042816) and Grant 11203820 (CityU 9042598); in part by the Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety (Beijing Technology and Business University) under Grant BTBD-2021KF02; in part by the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology under Grant 2020QNRC001; in part by the CAAI-Huawei MindSpore Open Fund; in part by the Scientific Research Program of Beijing Municipal Education Commission under Grant KZ202110011017; in part by the Beijing Natural Science Foundation and Fengtai Rail Transit Frontier Research Joint Fund under Grant L191009; and in part by the Dr Cong's Project of in part by the Fundamental Research Funds for the Central Universities under Grant 2022JBMC002.

Keywords

  • boundary guided semantic learning
  • COVID-19
  • CT image
  • infection segmentation

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