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Allograft Selection for Transepiphyseal Tumor Resection Around the Knee Using Three-Dimensional Surface Registration

Bou Sleiman, Habib ; Ritacco, Lucas ; Aponte-Tinao, Luis ; Muscolo, Domingo ; Nolte, Lutz-Peter ; Reyes, Mauricio

In: Annals of Biomedical Engineering, 2011, vol. 39, no. 6, p. 1720-1727

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    Titre
    • Allograft Selection for Transepiphyseal Tumor Resection Around the Knee Using Three-Dimensional Surface Registration
    Auteur
    • Bou Sleiman, Habib. Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland
    • Ritacco, Lucas. Hospital Italiano de Buenos Aires, Gascón 450, Buenos Aires, Argentina
    • Aponte-Tinao, Luis. Hospital Italiano de Buenos Aires, Gascón 450, Buenos Aires, Argentina
    • Muscolo, Domingo. Hospital Italiano de Buenos Aires, Gascón 450, Buenos Aires, Argentina
    • Nolte, Lutz-Peter. Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland
    • Reyes, Mauricio. Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Annals of Biomedical Engineering, 2011, vol. 39, no. 6, p. 1720-1727. Springer US; http://www.springer-ny.com
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:318189
    Summary
    Transepiphyseal tumor resection is a common surgical procedure in patients with malignant bone tumors. The aim of this study is to develop and validate a computer-assisted method for selecting the most appropriate allograft from a cadaver bone bank. Fifty tibiae and femora were 3D reconstructed from computed tomography (CT) images. A transepiphyseal resection was applied to all of them in a virtual environment. A tool was developed and evaluated that compares each metaphyseal piece against all other bones in the data bank. This is done through a template matching process, where the template is extracted from the contralateral healthy bone of the same patient. The method was validated using surface distance metrics and statistical tests comparing it against manual methods. The developed algorithm was able to accurately detect the bone segment that best matches the patient's anatomy. The automatic method showed improvement over the manual counterpart. The proposed method also substantially reduced computation time when compared to state-of-the-art methods as well as the manual selection. Our findings suggest that the accuracy, robustness, and speed of the developed method are suitable for clinical trials and that it can be readily applied for preoperative allograft selection