A shape-partitioned statistical shape model for highly deformed femurs using X-ray images

References

• Cootes TF, Taylor CJ, Cooper DH, et al. Active shape models-their training and application. Comput Vision Image Understanding. 1995;61(1):50–59.
   Web of Science ®Google Scholar
• Reyneke CJF, Lüthi M, Burdin V, et al. Review of 2-D/3-D reconstruction using statistical shape and intensity models and X-ray image synthesis: toward a unified framework. IEEE Rev Biomed Eng. 2019;12:269–286.
   PubMedGoogle Scholar
• Baka N, Kaptein BL, de Bruijne M, et al. 2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med Image Anal. 2011;15(6):840–850.
   PubMed Web of Science ®Google Scholar
• Chan CS, Edwards PJ, Hawkes DJ. Integration of ultrasound-based registration with statistical shape models for computer-assisted orthopedic surgery. Med Imag. 2003;5032:414–424.
   Google Scholar
• Fleute M, Lavallée S. Nonrigid 3-D/2-D registration of images using statistical models. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Heidelberg: Springer; 1999. p. 138–147.
   Google Scholar
• Benameur S, Mignotte M, Parent S, et al. 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput Med Imaging Graph. 2003;27(5):321–337.
   PubMed Web of Science ®Google Scholar
• Zheng G, Gollmer S, Schumann S, 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. Med Image Anal. 2009;13(6):883–899.
   PubMed Web of Science ®Google Scholar
• Rajamani KT, Styner MA, Talib H, et al. Statistical deformable bone models for robust 3D surface extrapolation from sparse data. Med Image Anal. 2007;11(2):99–109.
   PubMed Web of Science ®Google Scholar
• Sherouse GW, Novins K, Chaney EL. Computation of digitally reconstructed radiographs for use in radiotherapy treatment design. Int J Radiat Oncol Biol Phys. 1990;18(3):651–658.
   PubMed Web of Science ®Google Scholar
• Yao J. Assessing accuracy factors in deformable 2D/3D medical image registration using a statistical pelvis model. In Proceedings Ninth IEEE International Conference on Computer Vision. IEEE; 2003. p. 1329–1334.
   Google Scholar
• Yu W, Chu C, Tannast M, et al. Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images. Int J Comput Assist Radiol Surg. 2016;11(9):1673–1685.
   PubMed Web of Science ®Google Scholar
• Cerveri P, Sacco C, Olgiati G, et al. 2D/3D reconstruction of the distal femur using statistical shape models addressing personalized surgical instruments in knee arthroplasty: a feasibility analysis. Int J Med Robotics Comput Assist Surg. 2017;13(4):e1823.
   Web of Science ®Google Scholar
• Cerveri P, Belfatto A, Manzotti A. Predicting knee joint instability using a Tibio-femoral statistical shape model. Front Bioeng Biotechnol. 2020;8:253.
   PubMed Web of Science ®Google Scholar
• Youn K, Park MS, Lee J. Iterative approach for 3D reconstruction of the femur from un-calibrated 2D radiographic images. Med Eng Phys. 2017;50:89–95.
   PubMed Web of Science ®Google Scholar
• Kim H, Lee K, Lee D, et al. 3D reconstruction of leg bones from x-ray images using CNN-based feature analysis. In 2019 International Conference on Information and Communication Technology Convergence (ICTC). IEEE; 2019. p. 669–672.
   Google Scholar
• Kasten Y, Doktofsky D, Kovler I. End-to-end convolutional neural network for 3D reconstruction of knee bones from bi-planar X-ray images. In International Workshop on Machine Learning for Medical Image Reconstruction. Cham: Springer, 2020. p. 123–133.
   Google Scholar
• O’Connor J, Rutherford M, Hill J, et al. Statistical shape model based 2D–3D reconstruction of the proximal femur—influence of radiographic femoral orientation on reconstruction accuracy. In Computer Methods in Biomechanics and Biomedical Engineering. Springer, Cham, 2018. p. 153–160.
   Google Scholar
• O'Connor JD, Hill JC, Beverland DE, et al. Influence of preoperative femoral orientation on radiographic measures of femoral head height in total hip replacement. Clin Biomech. 2021;81:105247.
   PubMed Web of Science ®Google Scholar
• Blanc R, Reyes M, Seiler C, et al. Conditional variability of statistical shape models based on surrogate variables. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Heidelberg: Springer; 2009. p. 84–91.
   Google Scholar
• Zhang J, Malcolm D, Hislop-Jambrich J, et al. An anatomical region-based statistical shape model of the human femur. Comput Methods Biomech Biomed Eng Imag Vis. 2014;2(3):176–185.
   Google Scholar
• Lüthi M, Gerig T, Jud C, et al. Gaussian process morphable models. IEEE Trans Pattern Anal Mach Intell. 2018;40(8):1860–1873.
   PubMed Web of Science ®Google Scholar
• Fouefack JR, Borotikar B, Douglas TS, et al. Dynamic multi-object gaussian process models. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer; 2020. p. 755–764.
   Google Scholar
• Mitschke M, Navab N. Recovering the X-ray projection geometry for three-dimensional tomographic reconstruction with additional sensors: Attached camera versus external navigation system. Med Image Anal. 2003;7(1):65–78.
   PubMed Web of Science ®Google Scholar
• Tsai RY, Lenz RK. Real time versatile robotics hand/eye calibration using 3D machine vision. In Proceedings 1988 IEEE International Conference on Robotics and Automation. IEEE; 1988. p. 554–561.
   Google Scholar
• Karmarkar N. A new polynomial-time algorithm for linear programming. In Proceedings of the Sixteenth Annual ACM Symposium on Theory of Computing. New York (NY): Association for Computing Machinery; 1984. p. 302–311.
   Google Scholar
• Pieper S, Halle M, Kikinis R. 3D Slicer. In 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821). IEEE; 2004. p. 632–635.
   Google Scholar
• Efron B. Estimating the error rate of a prediction rule: improvement on cross-validation. J Am Stat Assoc. 1983;78(382):316–331.
   Web of Science ®Google Scholar
• Shefelbine SJ, Carter DR. Mechanobiological predictions of femoral anteversion in cerebral palsy. Ann Biomed Eng. 2004;32(2):297–305.
   PubMed Web of Science ®Google Scholar
• Van de Giessen M, Foumani M, Streekstra GJ, et al. Statistical descriptions of scaphoid and lunate bone shapes. J Biomech. 2010;43(8):1463–1469.
   PubMed Web of Science ®Google Scholar
• Baka N, Kaptein BL, Giphart JE, et al. Evaluation of automated statistical shape model based knee kinematics from biplane fluoroscopy. J Biomech. 2014;47(1):122–129.
   PubMed Web of Science ®Google Scholar
• Lianghui F, Gang HJ, Yang J, et al. Femur segmentation in X-ray image based on improved U-Net. In IOP Conference Series: Materials Science and Engineering. IOP Publishing. 2019;533(1):012061.
   Google Scholar
• Ding F, Leow WK, Howe TS. Automatic segmentation of femur bones in anterior-posterior pelvis x-ray images. In International Conference on Computer Analysis of Images and Patterns. Springer, Berlin, Heidelberg, 2007. p. 205–212.
   Google Scholar
• Chen Y, Ee X, Leow WK, et al. Automatic extraction of femur contours from hip x-ray images. International workshop on computer vision for. Biomed Image Appl. 2005;3765:200–209.
   Google Scholar
• Pilgram R, Walch C, Kuhn V, et al. Proximal femur segmentation in conventional pelvic X ray. Med Phys. 2008;35(6):2463–2472.
   PubMed Web of Science ®Google Scholar
• Wu J, Mahfouz MR. Robust x-ray image segmentation by spectral clustering and active shape model. J Med Imaging. 2016;3(3):034005.
   Web of Science ®Google Scholar