Related Experiment Video
Updated: May 25, 2026
Automated Joint Space Detection Improves Bone Segmentation Accuracy
Published on: November 28, 2025
Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy
Daniel F Malan1, Charl P Botha, Edward R Valstar
1Department of Orthopaedics, Leiden University Medical Center, J11-R, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. fmalan@medvis.org
Purpose:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
