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Segmentation of Multiple Knee Bones from CT for Orthopedic Knee Surgery Planning

  • Dijia Wu
  • Michal Sofka
  • Neil Birkbeck
  • S. Kevin Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Patient-specific orthopedic knee surgery planning requires precisely segmenting from 3D CT images multiple knee bones, namely femur, tibia, fibula, and patella, around the knee joint with severe pathologies. In this work, we propose a fully automated, highly precise, and computationally efficient segmentation approach for multiple bones. First, each bone is initially segmented using a model-based marginal space learning framework for pose estimation followed by non-rigid boundary deformation. To recover shape details, we then refine the bone segmentation using graph cut that incorporates the shape priors derived from the initial segmentation. Finally we remove overlap between neighboring bones using multi-layer graph partition. In experiments, we achieve simultaneous segmentation of femur, tibia, patella, and fibula with an overall accuracy of less than 1mm surface-to-surface error in less than 90s on hundreds of 3D CT scans with pathological knee joints.

Keywords

Segmentation Result Initial Segmentation Statistical Shape Model Bone Segmentation Pairwise Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dijia Wu
    • 1
  • Michal Sofka
    • 1
  • Neil Birkbeck
    • 1
  • S. Kevin Zhou
    • 1
  1. 1.Imaging & Computer VisionSiemens Corporate TechnologyPrincetonUSA

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