A comparison of the accuracy of statistical models of prostate motion trained using data from biomechanical simulations

Hu, Yipeng, van den Boom, Rieneke, Carter, Timothy, Taylor, Zeike, Hawkes, David, Ahmed, Hashim Uddin, Emberton, Mark, Allen, Clare and Barratt, Dean (2010) A comparison of the accuracy of statistical models of prostate motion trained using data from biomechanical simulations. Progress in Biophysics & Molecular Biology, 103 2-3: 262-272.


Author Hu, Yipeng
van den Boom, Rieneke
Carter, Timothy
Taylor, Zeike
Hawkes, David
Ahmed, Hashim Uddin
Emberton, Mark
Allen, Clare
Barratt, Dean
Title A comparison of the accuracy of statistical models of prostate motion trained using data from biomechanical simulations
Journal name Progress in Biophysics & Molecular Biology   Check publisher's open access policy
ISSN 0079-6107
1873-1732
Publication date 2010-12
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1016/j.pbiomolbio.2010.09.009
Volume 103
Issue 2-3
Start page 262
End page 272
Total pages 11
Place of publication Oxford, England, U. K.
Publisher Pergamon
Language eng
Formatted abstract 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. © 2010 Elsevier Ltd.
Keyword Biomechanical modelling
Statistical shape models
Organ motion
Prostate
Image registration
Image-Guided Surgery
Surgical Simulation
Finite-Element
Mr-Images
Registration
Interventions
Deformation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: ERA 2012 Admin Only
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