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Automatic Paraspinal Muscle Segmentation in Patients with Lumbar Pathology Using Deep Convolutional Neural Network

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (MICCAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11765))

Abstract

Recent evidence suggests an association between low back pain (LBP) and changes in lumbar paraspinal muscle morphology and composition (i.e., fatty infiltration). Quantitative measurements of muscle cross-sectional areas (CSAs) from MRI scans are commonly used to examine the relationship between paraspinal muscle characters and different lumbar conditions. The current investigation primarily uses manual segmentation that is time-consuming, laborious, and can be inconsistent. However, no automatic MRI segmentation algorithms exist for pathological data, likely due to the complex paraspinal muscle anatomy and high variability in muscle composition among patients. We employed deep convolutional neural networks using U-Net+CRF-RNN with multi-data training to automatically segment paraspinal muscles from T2-weighted MRI axial slices at the L4-L5 and L5-S1 spinal levels and achieved averaged Dice score of 93.9\(\%\) and mean boundary distance of 1 mm. We also demonstrate the application using the segmentation results to reveal tissue characteristics of the muscles in relation to age and sex.

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Acknowledgment

This work was supported by CIHR, CFI, NSERC and BrainsCAN, as well as the Seventh Framework Programme (Health-2007-2013, grant agreement NO: 201626: GENODISC) and Canada Reearch Chairs program. We acknowledge the support of NVIDIA Corporation and thank Dr. Yingli Lu for his help.

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Authors and Affiliations

  1. Robarts Research Institute, Western University, London, Canada

    Wenyao Xia, Terry M. Peters & Yiming Xiao

  2. Health, Kinesiology and Applied Physiology, Concordia University, Montreal, Canada

    Maryse Fortin

  3. PERFORM Centre, Concordia University, Montreal, Canada

    Maryse Fortin & Hassan Rivaz

  4. Department of Kinesiology, Western University, London, Canada

    Joshua Ahn

  5. Electrical and Computer Engineering, Concordia University, Montreal, Canada

    Hassan Rivaz

  6. School of Physical Therapy and Western’s Bone and Joint Institute, Western University, London, Canada

    Michele C. Battié

Authors
  1. Wenyao Xia
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  2. Maryse Fortin
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  3. Joshua Ahn
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  4. Hassan Rivaz
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  5. Michele C. Battié
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  6. Terry M. Peters
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  7. Yiming Xiao
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Corresponding author

Correspondence to Wenyao Xia .

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dinggang Shen

  2. University of Georgia, Athens, GA, USA

    Tianming Liu

  3. Western University, London, ON, Canada

    Terry M. Peters

  4. Yale University, New Haven, CT, USA

    Lawrence H. Staib

  5. University of Strasbourg, Illkirch, France

    Caroline Essert

  6. United Imaging Intelligence, Shanghai, China

    Sean Zhou

  7. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Pew-Thian Yap

  8. Western University, London, ON, Canada

    Ali Khan

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Xia, W. et al. (2019). Automatic Paraspinal Muscle Segmentation in Patients with Lumbar Pathology Using Deep Convolutional Neural Network. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_36

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  • DOI: https://doi.org/10.1007/978-3-030-32245-8_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32244-1

  • Online ISBN: 978-3-030-32245-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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