Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy

  • Daniel F. MalanEmail author
  • Charl P. Botha
  • Edward R. Valstar
Open Access
Original Article



Automated patient-specific image-based segmentation of tissues surrounding aseptically loose hip prostheses is desired. For this we present an automated segmentation pipeline that labels periprosthetic tissues in computed tomography (CT). The intended application of this pipeline is in pre-operative planning.


Individual voxels were classified based on a set of automatically extracted image features. Minimum-cost graph cuts were computed on the classification results. The graph-cut step enabled us to enforce geometrical containment constraints, such as cortical bone sheathing the femur’s interior. The solution’s novelty lies in the combination of voxel classification with multilabel graph cuts and in the way label costs were defined to enforce containment constraints.


The segmentation pipeline was tested on a set of twelve manually segmented clinical CT volumes. The distribution of healthy tissue and bone cement was automatically determined with sensitivities greater than 82% and pathological fibrous interface tissue with a sensitivity exceeding 73%. Specificity exceeded 96% for all tissues.


The addition of a graph-cut step improved segmentation compared to voxel classification alone. The pipeline described in this paper represents a practical approach to segmenting multitissue regions from CT.


Segmentation Graph cut Voxel classification Osteolysis Computed tomography 



This research is supported by the Dutch Technology Foundation STW, which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs (project number LKG 7943). The authors sincerely thank Professor Rob Nelissen for verifying the correctness of the manually segmented ground truth and Noeska Smit for the re-segmentation of several of the data sets for evaluating inter-observer variability.We furthermore thank David Tax and Marco Loog from the pattern recognition group at Delft University of Technology for their helpful input.

Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.


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Copyright information

© The Author(s) 2012

Open AccessThis is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Daniel F. Malan
    • 1
    • 2
    Email author
  • Charl P. Botha
    • 2
    • 3
  • Edward R. Valstar
    • 1
    • 4
  1. 1.Department of OrthopaedicsLeiden University Medical CenterLeidenThe Netherlands
  2. 2.Department of Mediamatics, EEMCSDelft University of TechnologyDelftThe Netherlands
  3. 3.Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
  4. 4.Department of Biomechanical EngineeringDelft University of TechnologyDelftThe Netherlands

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