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Publication Detail
A comparison of the accuracy of statistical models of prostate motion trained using data from biomechanical simulations.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Comparative Study
  • Authors:
    Hu Y, van den Boom R, Carter T, Taylor Z, Hawkes D, Ahmed HU, Emberton M, Allen C, Barratt D
  • Publication date:
  • Pagination:
    262, 272
  • Journal:
    Prog Biophys Mol Biol
  • Volume:
  • Issue:
  • Status:
  • Country:
  • PII:
  • Language:
  • Keywords:
    Biomechanical Phenomena, Computer Simulation, Humans, Male, Models, Statistical, Motion, Prostate, Radiographic Image Enhancement, Reproducibility of Results, Sensitivity and Specificity
Statistical shape models (SSM) are widely used in medical image analysis to represent variability in organ shape. However, representing subject-specific soft-tissue motion using this technique is problematic for applications where imaging organ changes in an individual is not possible or impractical. One solution is to synthesise training data by using biomechanical modelling. However, for many clinical applications, generating a biomechanical model of the organ(s) of interest is a non-trivial task that requires a significant amount of user-interaction to segment an image and create a finite element mesh. In this study, we investigate the impact of reducing the effort required to generate SSMs and the accuracy with which such models can predict tissue displacements within the prostate gland due to transrectal ultrasound probe pressure. In this approach, the finite element mesh is based on a simplified geometric representation of the organs. For example, the pelvic bone is represented by planar surfaces, or the number of distinct tissue compartments is reduced. Such representations are much easier to generate from images than a geometrically accurate mesh. The difference in the median root-mean-square displacement error between different SSMs of prostate was <0.2 mm. We conclude that reducing the geometric complexity of the training model in this way made little difference to the absolute accuracy of SSMs to recover tissue displacements. The implication is that SSMs of organ motion based on simulated training data may be generated using simplified geometric representations, which are much more compatible with the time constraints of clinical workflows.
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