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Tuberculosis CT Image Analysis Using Image Features Extracted by 3D Autoencoder

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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12260))

Abstract

This paper presents an approach for the automated analysis of 3D Computed Tomography (CT) images based on the utilization of descriptors extracted using 3D deep convolutional autoencoder (AEC  [8]) networks. Both the common flow of AEC model application and a set of techniques for overcoming the lack of training samples are presented in this work. The described approach was used for accomplishing the two subtasks of the ImageCLEF 2019: Tuberculosis competition  [2, 5] and allowed to achieve the 2nd best performance in the TB Severity Scoring subtask and the 6th best performance in the TB CT Report subtask.

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Notes

  1. 1.

    Participant name was changed during competition.

  2. 2.

    https://www.imageclef.org/2019/medical/tuberculosis/.

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Acknowledgements

This study was partly supported by the National Institute of Allergy and Infectious Diseases, National Institutes of Health, U.S. Department of Health and Human Services, USA through the CRDF project DAA3–18-64818-1 “Year 7: Belarus TB Database and TB Portals”.

Author information

Authors and Affiliations

  1. United Institute of Informatics Problems, Minsk, Belarus

    Siarhei Kazlouski

Authors
  1. Siarhei Kazlouski
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Correspondence to Siarhei Kazlouski .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece

    Avi Arampatzis

  2. University of Amsterdam, Amsterdam, The Netherlands

    Evangelos Kanoulas

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Theodora Tsikrika

  4. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

  5. Faculty of Library, Information and Media Science, University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

    Christina Lioma

  7. Brown University, Providence, RI, USA

    Carsten Eickhoff

  8. LIMSI-CNRS, Orsay, France

    Aurélie Névéol

  9. Department of Information Engineering, University of Padova, Padua, Italy

    Linda Cappellato

  10. Department of Information Engineering, University of Padova, Padua, Italy

    Nicola Ferro

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Kazlouski, S. (2020). Tuberculosis CT Image Analysis Using Image Features Extracted by 3D Autoencoder. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2020. Lecture Notes in Computer Science(), vol 12260. Springer, Cham. https://doi.org/10.1007/978-3-030-58219-7_12

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  • DOI: https://doi.org/10.1007/978-3-030-58219-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58218-0

  • Online ISBN: 978-3-030-58219-7

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