An anatomical region-based statistical shape model of the human femur

We present a workflow for producing a statistical shape model (SSM) of the femur with automatically defined regions resembling general anatomic features. Explicitly defined regions enforce correspondence of anatomical features, and allow the shapes of regions to be analysed independently if needed....

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Vydáno v:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Ročník 2; číslo 3; s. 176 - 185
Hlavní autoři: Zhang, Ju, Malcolm, Duane, Hislop-Jambrich, Jacqui, Thomas, C. David L., Nielsen, Poul M.F.
Médium: Journal Article
Jazyk:angličtina
japonština
Vydáno: Taylor & Francis 03.07.2014
Informa UK Limited
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ISSN:2168-1163, 2168-1171
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Abstract We present a workflow for producing a statistical shape model (SSM) of the femur with automatically defined regions resembling general anatomic features. Explicitly defined regions enforce correspondence of anatomical features, and allow the shapes of regions to be analysed independently if needed. A training set of manually segmented femur surfaces are partitioned according to Gaussian curvature. Partitioned regions across the training set are then grouped using mean-shift clustering to identify the most stable regions into which surfaces are divided. Reference piecewise parametric meshes are designed for and fitted to each region, and used to train regional SSMs through fitting-training iterations. Fitted region meshes are assembled into full femur meshes for training a whole femur region-based SSM (rSSM). Partitioning, clustering and shape modelling results are presented for 41 femurs. In comparison to a non-regional SSM, the rSSM was more efficient and correspondent in its approximation of unseen femurs.
AbstractList We present a workflow for producing a statistical shape model (SSM) of the femur with automatically defined regions resembling general anatomic features. Explicitly defined regions enforce correspondence of anatomical features, and allow the shapes of regions to be analysed independently if needed. A training set of manually segmented femur surfaces are partitioned according to Gaussian curvature. Partitioned regions across the training set are then grouped using mean-shift clustering to identify the most stable regions into which surfaces are divided. Reference piecewise parametric meshes are designed for and fitted to each region, and used to train regional SSMs through fitting-training iterations. Fitted region meshes are assembled into full femur meshes for training a whole femur region-based SSM (rSSM). Partitioning, clustering and shape modelling results are presented for 41 femurs. In comparison to a non-regional SSM, the rSSM was more efficient and correspondent in its approximation of unseen femurs.
Author Zhang, Ju
Malcolm, Duane
Nielsen, Poul M.F.
Thomas, C. David L.
Hislop-Jambrich, Jacqui
Author_xml – sequence: 1
  givenname: Ju
  surname: Zhang
  fullname: Zhang, Ju
  email: ju.zhang@auckland.ac.nz
  organization: Auckland Bioengineering Institute, University of Auckland
– sequence: 2
  givenname: Duane
  surname: Malcolm
  fullname: Malcolm, Duane
  organization: Auckland Bioengineering Institute, University of Auckland
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  surname: Hislop-Jambrich
  fullname: Hislop-Jambrich, Jacqui
  organization: Clinical Applications Research Center, Toshiba Medical
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  givenname: C. David L.
  surname: Thomas
  fullname: Thomas, C. David L.
  organization: The Melbourne Dental School, University of Melbourne, Melbourne
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  givenname: Poul M.F.
  surname: Nielsen
  fullname: Nielsen, Poul M.F.
  organization: Department of Engineering Science, University of Auckland
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Snippet We present a workflow for producing a statistical shape model (SSM) of the femur with automatically defined regions resembling general anatomic features....
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SubjectTerms femur morphology
model generation
musculoskeletal modelling
statistical modelling
statistical shape modelling
Title An anatomical region-based statistical shape model of the human femur
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