Allograft selection for distal femur through cutting contour registration
- 165 Downloads
Allograft reconstruction is an acceptable procedure for the recovery of normal anatomy after the bone tumor resection. During the past few years, several automated methods have been proposed to select the best anatomically matching allograft from the virtual donor bone bank. The surface-based automated method uses the contralateral healthy bone to obtain the normal surface shape of the diseased bone, which could achieve good matching of the defect and the selected allograft. However, the surface-based method focuses on the matching of the whole bone so that the matching of the contact surface between the allograft and the recipient bone may not be optimal. To deal with the above problem, we propose a cutting contour based method for the allograft selection. Cutting contour from the recipient bone could reflect the structural information of the defect and is seldom influenced by tumor. Thus the cutting contour can be used as the matching template to find the optimal alignment of the recipient bone and the allograft. The proposed method is validated using the data of distal femurs where bone transplantation is commonly performed. Experimental results show that the proposed method generally outperforms the surface-based method within modest extra time. Overall, our contour-based method is an effective complementary technique for allograft selection in the virtual bone bank.
KeywordsAutomated allograft selection Orthopedic oncology Contour extraction Contour registration Computer-aided surgery Patient specific instrument
The authors would like to thank the anonymous reviewers and the editor for their suggestions on improving the quality of this article. This work is partly supported by National Natural Science Foundation of China with Grant Nos. 61172125 and 61132007, and is partly supported by the Joint Fund of Civil Aviation Research by National Natural Science Foundation of China and Civil Aviation Administration of China with Grant No. U1533132.
- Chen Y, Medioni G (1991) Object modeling by registration of multiple range images. In: IEEE international conference on robotics and automation, pp 2724–2729Google Scholar
- de Vet HCW, Mokkink LB, Terwee CB, Hoekstra OS, Knol DL (2013) Clinicians are right not to like Cohen’s \(\kappa\). BMJ (Clinical research ed.) 346 (April), p f2125Google Scholar
- Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the KITTI vision Benchmark suite. In: Conference on computer vision and pattern recognition (CVPR)Google Scholar
- Lehmann EL, D’Abrera HJM (2006) Nonparametrics: statistical methods based on ranks. Springer, New YorkGoogle Scholar
- Ritacco LE, Espinoza OAA, Aponte-Tinao LA, Muscolo DL, de Quirós FG, Nozomu I (2009) Three-dimensional morphometric analysis of the distal femur: a validity method for allograft selection using a virtual bone bank. Stud Health Technol Inform 160(Pt 2):1287–1290Google Scholar