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Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (MICCAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12907))

Abstract

Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quantify the disease and even help predict the outcome. The availability of longitudinal CT series may also result in an efficient and effective method to reliably assess the progression of COVID-19, monitor the healing process and the response to different therapeutic strategies. In this paper, we propose a new framework to identify infection at a voxel level (identification of healthy lung, consolidation, and ground-glass opacity) and visualize the progression of COVID-19 using sequential low-dose non-contrast CT scans. In particular, we devise a longitudinal segmentation network that utilizes the reference scan information to improve the performance of disease identification. Experimental results on a clinical longitudinal dataset collected in our institution show the effectiveness of the proposed method compared to the static deep neural networks for disease quantification.

S. T. Kim and L. Goli—Equally Contributd.

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Notes

  1. 1.

    https://coronavirus.jhu.edu/map.html.

  2. 2.

    https://polyaxon.com/.

  3. 3.

    https://github.com/lilygoli/longitudinalCOVID.

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Acknowledgement

This paper was funded by the Bavarian Research Foundation (BFS) under grant agreement AZ-1429-20C. We would like to thank NVIDIA for the GPU donation.

Author information

Authors and Affiliations

  1. Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany

    Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Nassir Navab & Thomas Wendler

  2. Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Korea

    Seong Tae Kim

  3. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Leili Goli

  4. Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany

    Egon Burian & Rickmer Braren

  5. Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA

    Nassir Navab

Authors
  1. Seong Tae Kim
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  2. Leili Goli
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  3. Magdalini Paschali
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  4. Ashkan Khakzar
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  5. Matthias Keicher
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  6. Tobias Czempiel
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  7. Egon Burian
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  8. Rickmer Braren
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  9. Nassir Navab
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  10. Thomas Wendler
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Corresponding author

Correspondence to Seong Tae Kim .

Editor information

Editors and Affiliations

  1. Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands

    Marleen de Bruijne

  2. University of Basel, Allschwil, Switzerland

    Philippe C. Cattin

  3. Inria Nancy Grand Est, Villers-lès-Nancy, France

    Stéphane Cotin

  4. ICube, Université de Strasbourg, CNRS, Strasbourg,, France

    Nicolas Padoy

  5. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  6. Tencent Jarvis Lab, Shenzhen, China

    Yefeng Zheng

  7. ICube, Université de Strasbourg, CNRS, Strasbourg, France

    Caroline Essert

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Kim, S.T. et al. (2021). Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_26

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  • DOI: https://doi.org/10.1007/978-3-030-87234-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87233-5

  • Online ISBN: 978-3-030-87234-2

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