Two-Stage Mapping-Segmentation Framework for Delineating COVID-19 Infections from Heterogeneous CT Images

Tong Li, Zhuochen Wang, Yanbo Chen, Lichi Zhang, Yaozong Gao, Feng Shi, Dahong Qian, Qian Wang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Infection segmentation is essential for quantitative assessment in computer-aided management of COVID-19. However, clinical CT images are usually heterogeneous, which are reconstructed by different protocols with varying appearance and voxel spacing due to radiologist preference. Most existing infection segmentation models are only trained using specific types of CT images, which would undermine the performance when applied to other types of CT images. Therefore, it is highly desirable to construct a model that could be applied to heterogeneous CT images in the urgent COVID-19 applications. In this paper, we present a two-stage mapping-segmentation framework for delineating COVID-19 infections from CT images. To compensate for heterogeneity of CT images obtained from different imaging centers, we develop an image-level domain-adaptive process to transform all kinds of images into a target type, and then segment COVID-19 infections accordingly. Experiments show that the infection delineation performance based on our proposed method is superior to the model trained jointly using mixture of all types of images, and is also comparable to those models supervised by using only specific types of images (if applicable).

Original languageEnglish
Title of host publicationThoracic Image Analysis - Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsJens Petersen, Raúl San José Estépar, Alexander Schmidt-Richberg, Sarah Gerard, Bianca Lassen-Schmidt, Colin Jacobs, Reinhard Beichel, Kensaku Mori
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-13
Number of pages11
ISBN (Print)9783030624682
DOIs
Publication statusPublished - 2020
Event2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and Computer-Assisted Intervention Conference, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 82020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12502 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and Computer-Assisted Intervention Conference, MICCAI 2020
CountryPeru
CityLima
Period20/10/820/10/8

Keywords

  • COVID-19
  • Domain-adaption
  • Image segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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