Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models

Med Image Comput Comput Assist Interv. 2013;16(Pt 2):190-7. doi: 10.1007/978-3-642-40763-5_24.

Abstract

Segmentation of the femur and pelvis is a prerequisite for patient-specific planning and simulation for hip surgery. Accurate boundary determination of the femoral head and acetabulum is the primary challenge in diseased hip joints because of deformed shapes and extreme narrowness of the joint space. To overcome this difficulty, we investigated a multi-stage method in which the hierarchical hip statistical shape model (SSM) is initially utilized to complete segmentation of the pelvis and distal femur, and then the conditional femoral head SSM is used under the condition that the regions segmented during the previous stage are known. CT data from 100 diseased patients categorized on the basis of their disease type and severity, which included 200 hemi-hips, were used to validate the method, which delivered significantly increased segmentation accuracy for the femoral head.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Arthrography / methods*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Effect Modifier, Epidemiologic
  • Female
  • Hip Joint / diagnostic imaging*
  • Hip Joint / surgery
  • Humans
  • Joint Diseases / diagnostic imaging*
  • Models, Anatomic*
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Radiographic Image Enhancement / methods
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Tomography, X-Ray Computed / methods*