Comparison between different screening strategies to determine the statistical shape model of the pelvises for implant design

BACKGROUND AND OBJECTIVES The statistical shape model (SSM) of numerous bones has been used to determine the anatomical representative of the population- or race-specific design for periarticular implants. Whether to include size- and profile-mismatched bones in the SSM calculation is debatable. Therefore, the objective of this study was to characterize the screening strategies for the mismatched bones to improve the SSM calculation. METHODS The bone database used in this study consisted of 20 pelvises. A systematic four-staged SSM calculation was used to evaluate the accuracy of the predicted SSM shape among the four size- and profile-screening strategies. Additionally, the surface-smoothing effects on the SSM results were investigated. Two comparison indices were used in terms of profile difference and surface smoothness. RESULTS Significant variations in size and profile existed for the collected bones. By normalizing the aspect ratio of all bones, exclusion of the size-mismatched bones reduced the maximum and root mean square (RMS) error values of the profile difference by 18.9% and 17.5%, respectively. After further excluding the profile-improper bones, normalization reduced the RMS profile difference by 24.1% compared with the non-normalized strategy. Exclusion of the size-improper bones for non-normalized strategy would have reduced the RMS profile difference by 15.4%. After smoothness, the RMS profile difference of SSM was only 6.1% higher than that of the non-smoothness SSM. CONCLUSIONS The four-stage calculation showed that the most favorable strategy was to normalize bones to the same aspect ratio and exclude improperly shaped bones. The model permitted inclusion of the original characteristics of the bones and preserved their shapes and excluded only significantly improper bones. After SSM calculation, the smoothed process provided satisfaction in quality with a statistically insignificant loss in bone morphology for population- or race-specific designs of implants.

[1]  Guoyan Zheng,et al.  3D reconstruction of a patient-specific surface model of the proximal femur from calibrated x-ray radiographs: a validation study. , 2009, Medical physics.

[2]  Nicholas Ayache,et al.  Generation of a statistical shape model with probabilistic point correspondences and the expectation maximization- iterative closest point algorithm , 2007, International Journal of Computer Assisted Radiology and Surgery.

[3]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[4]  Peng Li,et al.  Establishment of sequential software processing for a biomechanical model of mandibular reconstruction with custom-made plate , 2013, Comput. Methods Programs Biomed..

[5]  Yue Qi,et al.  Augmented reality patient-specific reconstruction plate design for pelvic and acetabular fracture surgery , 2013, International Journal of Computer Assisted Radiology and Surgery.

[6]  Beat Hammer,et al.  A Method for Assessing 3D Shape Variations of Fuzzy Regions and its Application on Human Bony Orbits , 2010, Journal of Digital Imaging.

[7]  Hansrudi Noser,et al.  3D statistical model of the pelvic ring – a CT‐based statistical evaluation of anatomical variation , 2018, Journal of anatomy.

[8]  Nils Reimers,et al.  Optimisation of orthopaedic implant design using statistical shape space analysis based on level sets , 2010, Medical Image Anal..

[9]  Mark Meyer,et al.  Implicit fairing of irregular meshes using diffusion and curvature flow , 1999, SIGGRAPH.

[10]  Terry S. Yoo,et al.  Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis , 2004 .

[11]  Prasanth B. Nair,et al.  Statistical modelling of the whole human femur incorporating geometric and material properties. , 2010, Medical engineering & physics.

[12]  Jenni M Buckley,et al.  Congruency of scapula locking plates: implications for implant design. , 2010, American journal of orthopedics.

[13]  Lizhuang Ma,et al.  A new feature-preserving mesh-smoothing algorithm , 2009, The Visual Computer.

[14]  A. A. Zadpoor,et al.  Statistical shape and appearance models of bones. , 2014, Bone.

[15]  Jeremy MG Taylor,et al.  Robust Statistical Modeling Using the t Distribution , 1989 .

[16]  Calvin R. Maurer,et al.  Statistical shape model generation using nonrigid deformation of a template mesh , 2005, SPIE Medical Imaging.

[17]  Willi A. Kalender,et al.  Building a statistical shape model of the pelvis , 2004, CARS.

[18]  B. Schmutz,et al.  Fit Assessment of Anatomic Plates for the Distal Medial Tibia , 2008, Journal of orthopaedic trauma.

[19]  Erwin Keeve,et al.  Computer-aided osteotomy design for harvesting autologous bone grafts in reconstructive surgery , 2001, SPIE Medical Imaging.

[20]  R. G. Richards,et al.  3D statistical modeling techniques to investigate the anatomy of the sacrum, its bone mass distribution, and the trans‐sacral corridors , 2014, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[21]  Hans-Peter Meinzer,et al.  Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..

[22]  H. J. Arnold Introduction to the Practice of Statistics , 1990 .

[23]  P. Rommens,et al.  Anatomic fit of six different radial head plates: comparison of precontoured low-profile radial head plates. , 2011, The Journal of hand surgery.

[24]  Z. Król,et al.  Virtual reconstruction of pelvic tumor defects based on a gender-specific statistical shape model , 2013, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[25]  H. Noser,et al.  Orbital form analysis: problems with design and positioning of precontoured orbital implants: a serial study using post-processed clinical CT data in unaffected orbits. , 2010, International journal of oral and maxillofacial surgery.

[26]  Frans Vos,et al.  A statistical description of the articulating ulna surface for prosthesis design , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[27]  Xavier Pennec,et al.  Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration , 2002, ECCV.

[28]  Franco Persiani,et al.  A CAD/CAM-prototyped anatomical condylar prosthesis connected to a custom-made bone plate to support a fibula free flap , 2012, Medical & Biological Engineering & Computing.

[29]  Zygmunt Wróbel,et al.  Reconstruction of the Pelvic Region Based on the Statistical Shape Modeling , 2010 .

[30]  Gabriel Taubin,et al.  A signal processing approach to fair surface design , 1995, SIGGRAPH.

[31]  Ju Zhang,et al.  Accuracy of femur reconstruction from sparse geometric data using a statistical shape model , 2017, Computer methods in biomechanics and biomedical engineering.

[32]  Guoyan Zheng,et al.  A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images , 2009, Medical Image Anal..

[33]  Hidetoshi Takahashi,et al.  Application of custom-made bioresorbable raw particulate hydroxyapatite/poly-L-lactide mesh tray with particulate cellular bone and marrow and platelet-rich plasma for a mandibular defect: evaluation of tray fit and bone quality in a dog model. , 2012, Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery.

[34]  Zhonglin Zhu,et al.  Construction of 3D human distal femoral surface models using a 3D statistical deformable model. , 2011, Journal of biomechanics.

[35]  Chuanbin Mao,et al.  3D printed personalized titanium plates improve clinical outcome in microwave ablation of bone tumors around the knee , 2017, Scientific Reports.

[36]  M J M Vasconcelos,et al.  Using Statistical Deformable Models to Reconstruct Vocal Tract Shape from Magnetic Resonance Images , 2010, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[37]  Andreas Petersik,et al.  A numeric approach for anatomic plate design. , 2018, Injury.