Segmentation of trabecular bone microdamage in Xray microCT images using a two-step deep learning method.

Journal: Journal of the mechanical behavior of biomedical materials
PMID: 36327650

Abstract

INTRODUCTION: One of the current approaches to improve our understanding of osteoporosis is to study the development of bone microdamage under mechanical loading. The current practice for evaluating bone microdamage is to quantify damage volume from images of bone samples stained with a contrast agent, often composed of toxic heavy metals and requiring long tissue preparation. This work aims to evaluate the potential of linear microcracks detection and segmentation in trabecular bone samples using well-known deep learning models, namely YOLOv4 and Unet, applied on microCT images.

Authors

  • Rodrigue Caron
    Department of Mechanical Engineering, Polytechnique Montréal, Montréal, QC, Canada; Centre de recherche du CHU Sainte Justine, CHU Sainte Justine, Montréal, QC, Canada.
  • Irène Londono
    Centre de recherche du CHU Sainte Justine, CHU Sainte Justine, Montréal, QC, Canada.
  • Lama Seoud
    Centre de recherche du CHU Sainte Justine, CHU Sainte Justine, Montréal, QC, Canada; Institut de génie biomédical, Montréal, QC, Canada; Department of Computer Engineering and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada.
  • Isabelle Villemure
    Department of Mechanical Engineering, Polytechnique Montréal, Montréal, QC, Canada; Centre de recherche du CHU Sainte Justine, CHU Sainte Justine, Montréal, QC, Canada; Institut de génie biomédical, Montréal, QC, Canada. Electronic address: isabelle.villemure@polymtl.ca.

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