Voxel classification of periprosthetic tissues in clinical competed tomography of loosened hip prostheses

  • Authors:

  • D. F. Malan;C. P. Botha;R. G. H. H. Nelissen;E. R. Valstar

  • Affiliations:

  • Department of Orthopaedics and Leiden University Medical Centre, Leiden, The Netherlands and Department of Mediamatics Delft University of Technology, The Netherlands;Leiden University Medical Centre, Leiden, The Netherlands and Department of Mediamatics Delft University of Technology, The Netherlands and Department of Radiology;Department of Orthopaedics and Leiden University Medical Centre, Leiden, The Netherlands;Department of Orthopaedics and Leiden University Medical Centre, Leiden, The Netherlands and Department of Biomechanical Engineering, Faculty 3ME, Delft University

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

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Abstract

We present an automated algorithm which classifies periprosthetic tissues in CT scans of patients with loosened hip prostheses. To our knowledge this is the first application of CT voxel classification to periprosthetic tissues of the hip. We use several image features including multi-scale image intensity, multi-scale image gradient and distance metrics. Seven classifier types were trained using five manually segmented clinical CT datasets, and their classification performance compared to manual segmentations using a leave-one-out scheme. Using this technique we are able to correctly segment the majority of each of the six tissue categories, in spite of low bone densities, metal-induced CT imaging artefacts and inter-patient and inter-scan variation. Our automated classifier forms a pragmatic first step towards eventual automatic tissue segmentation.