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Interpretable Deep Biomarker for Serial Monitoring of Carotid Atherosclerosis Based on Three-Dimensional Ultrasound Imaging

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14225))

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Abstract

We developed an interpretable deep biomarker known as Siamese change biomarker generation network (SCBG-Net) to evaluate the effects of therapies on carotid atherosclerosis based on the vessel wall and plaque volume and texture features extracted from three-dimensional ultrasound (3DUS) images. To the best of our knowledge, SCBG-Net is the first deep network developed for serial monitoring of carotid plaque changes. SCBG-Net automatically integrates volume and textural features extracted from 3DUS to generate a change biomarker called AutoVT (standing for Automatic integration of Volume and Textural features) that is sensitive to dietary treatments. The proposed AutoVT improves the cost-effectiveness of clinical trials required to establish the benefit of novel treatments, thereby decreasing the period that new anti-atherosclerotic treatments are withheld from patients needing them. To facilitate the interpretation of AutoVT, we developed an algorithm to generate change biomarker activation maps (CBAM) localizing regions having an important effect on AutoVT. The ability to visualize locations with prominent plaque progression/regression afforded by CBAM improves the interpretability of the proposed deep biomarker. Improvement in interpretability would allow the deep biomarker to gain sufficient trust from clinicians for them to incorporate the model into clinical workflow.

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Acknowledgement

Dr. Chiu is grateful for the funding support from the Research Grant Council of HKSAR, China (Project nos. CityU 11203218, CityU 11205822). The authors thank Dr. J. David Spence for providing the 3D ultrasound images investigated in this study.

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Authors and Affiliations

  1. Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong, China

    Xueli Chen, Xinqi Fan & Bernard Chiu

  2. Department of Physics and Computer Science, Wilfrid Laurier University, Waterloo, ON, Canada

    Bernard Chiu

Authors
  1. Xueli Chen
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  2. Xinqi Fan
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  3. Bernard Chiu
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Corresponding author

Correspondence to Bernard Chiu .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Chen, X., Fan, X., Chiu, B. (2023). Interpretable Deep Biomarker for Serial Monitoring of Carotid Atherosclerosis Based on Three-Dimensional Ultrasound Imaging. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_29

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  • DOI: https://doi.org/10.1007/978-3-031-43987-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43986-5

  • Online ISBN: 978-3-031-43987-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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