naa a22 4500 510807011 CHVBK 20180411083423.0 cr unu---uuuuu 180411e20130101xx s 000 0 eng 10.1007/s11548-012-0671-z doi (NATIONALLICENCE)springer-10.1007/s11548-012-0671-z Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy [Elektronische Daten] [Daniel Malan, Charl Botha, Edward Valstar] 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. The Author(s), 2012 Segmentation nationallicence Graph cut nationallicence Voxel classification nationallicence Osteolysis nationallicence Computed tomography nationallicence Malan Daniel Department of Orthopaedics, Leiden University Medical Center, J11-R, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands aut Botha Charl Department of Mediamatics, EEMCS, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands aut Valstar Edward Department of Orthopaedics, Leiden University Medical Center, J11-R, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands aut International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/1(2013-01-01), 63-74 1861-6410 8:1<63 2013 8 11548 https://doi.org/10.1007/s11548-012-0671-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-012-0671-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Malan Daniel Department of Orthopaedics, Leiden University Medical Center, J11-R, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands aut NATIONALLICENCE 700 1- Botha Charl Department of Mediamatics, EEMCS, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands aut NATIONALLICENCE 700 1- Valstar Edward Department of Orthopaedics, Leiden University Medical Center, J11-R, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/1(2013-01-01), 63-74 1861-6410 8:1<63 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer