Lynch JT, Schneider MT, Perriman DM, Scarvell JM, Pickering MR, Asikuzzaman M, Galvin CR, Besier TF, Smith PN (2019) Statistical shape modelling reveals large and distinct subchondral bony differences in osteoarthritic knees. J Biomech 93:177–184. https://doi.org/10.1016/j.jbiomech.2019.07.003
Klem N-R, Kent P, Smith A, Dowsey M, Fary R, Schütze R, O’Sullivan P, Choong P, Bunzli S (2020) Satisfaction after total knee replacement for osteoarthritis is usually high, but what are we measuring? A systematic review. Osteoarthr Cartil Open 2(1):100032. https://doi.org/10.1016/j.ocarto.2020.100032
Article PubMed PubMed Central Google Scholar
Fan X, Zhu Q, Tu P, Joskowicz L, Chen X (2022) A review of advances in image-guided orthopedic surgery. Phys Med Biol. https://doi.org/10.1088/1361-6560/acaae9
Article PubMed PubMed Central Google Scholar
Malyavko A, Cohen JS, Fuller SI, Agarwal AR, Golladay GJ, Thakkar SC (2023) Reduced early revision surgery and medical complications in computer-assisted knee arthroplasty compared with non-computer-assisted arthroplasty. JAAOS-J Am Acad Orthopa Surg 31(2):87–96. https://doi.org/10.5435/jaaos-d-22-00596
Picard F, Deep K, Jenny JY (2016) Current state of the art in total knee arthroplasty computer navigation. Knee Surg Sports Traumatol Arthrosc 24:3565–3574. https://doi.org/10.1007/s00167-016-4337-1
Markelj P, Tomaževič D, Likar B, Pernuš F (2012) A review of 3d/2d registration methods for image-guided interventions. Med Image Anal 16(3):642–661. https://doi.org/10.1016/j.media.2010.03.005
Simon D, O’toole R, Blackwell M, Morgan F, DiGioia A, Kanade T (1995) Accuracy validation in image-guided orthopaedic surgery. In: Proceedings of the 2nd international symposium on medical robotics and computer assisted surgery, vol 6. Citeseer, pp 185–192
Luo TD, Martensson N, Howard JL, Stevens D, McIsaac KA, Lanting BA (2025) Identifying sources of error in computer-navigated total knee arthroplasty using sensitivity analyses in knee models. J Arthroplasty. https://doi.org/10.1016/j.arth.2025.02.061
Schlatterer B, Linares J-M, Chabrand P, Sprauel J-M, Argenson J-N (2014) Influence of the optical system and anatomic points on computer-assisted total knee arthroplasty. Orthop Traumatol Surg Res 100(4):395–402. https://doi.org/10.1016/j.otsr.2013.12.029
Brin YS, Livshetz I, Antoniou J, Greenberg-Dotan S, Zukor DJ (2010) Precise landmarking in computer assisted total knee arthroplasty is critical to final alignment. J Orthop Res 28(10):1355–1359. https://doi.org/10.1002/jor.21139
Yau W, Leung A, Chiu K, Tang W, Ng T (2005) Intraobserver errors in obtaining visually selected anatomic landmarks during registration process in nonimage-based navigation-assisted total knee arthroplasty: a cadaveric experiment. J Arthroplast 20(5):591–601. https://doi.org/10.1016/j.arth.2005.02.011
Amanatullah DF, Di Cesare PE, Meere PA, Pereira GC (2013) Identification of the landmark registration safe zones during total knee arthroplasty using an imageless navigation system. J Arthroplast 28(6):938–942. https://doi.org/10.1016/j.arth.2012.12.013
Davis ET, Pagkalos J, Gallie PA, Macgroarty K, Waddell JP, Schemitsch EH (2014) Defining the errors in the registration process during imageless computer navigation in total knee arthroplasty: a cadaveric study. J Arthroplast 29(4):698–701. https://doi.org/10.1016/j.arth.2013.06.034
Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59. https://doi.org/10.1006/cviu.1995.1004
Boutillon A, Salhi A, Burdin V, Borotikar B (2022) Anatomically parameterized statistical shape model: explaining morphometry through statistical learning. IEEE Trans Biomed Eng 69(9):2733–2744. https://doi.org/10.1109/tbme.2022.3152833
Gheflati, B., Mirzaei, M., Rottoo, S., & Rivaz, H. (2025). Leveraging deep learning for nonlinear shape representation in anatomically parameterized statistical shape models. International Journal of Computer Assisted Radiology and Surgery, 1–12
Dupraz I, Bollinger A, Deckx J, Schierjott RA, Utz M, Jacobs M (2022) Using statistical shape models to optimize tka implant design. Appl Sci 12(3):1020. https://doi.org/10.3390/app12031020
Baka N, Kaptein BL, Bruijne M, Walsum T, Giphart J, Niessen WJ, Lelieveldt BP (2011) 2d–3d shape reconstruction of the distal femur from stereo x-ray imaging using statistical shape models. Med Image Anal 15(6):840–850. https://doi.org/10.1016/j.media.2011.04.001
Rajamani KT, Styner MA, Talib H, Zheng G, Nolte LP, Ballester MAG (2007) Statistical deformable bone models for robust 3d surface extrapolation from sparse data. Med Image Anal 11(2):99–109. https://doi.org/10.1016/j.media.2006.05.001
Tycowicz C, Ambellan F, Mukhopadhyay A, Zachow S (2018) An efficient Riemannian statistical shape model using differential coordinates: With application to the classification of data from the osteoarthritis initiative. Med Image Anal 43:1–9. https://doi.org/10.1016/j.media.2017.09.004
Cerveri P, Belfatto A, Manzotti A (2020) Predicting knee joint instability using a tibio-femoral statistical shape model. Front Bioeng Biotechnol 8:253. https://doi.org/10.3389/fbioe.2020.00253
Article PubMed PubMed Central Google Scholar
Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239. https://doi.org/10.1109/iccv.1999.791245
Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL, Group BDC, et al (2011) Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54(1):313–327. https://doi.org/10.1016/j.neuroimage.2010.07.033
Zhang J, Hislop-Jambrich J, Besier TF (2016) Predictive statistical models of baseline variations in 3-d femoral cortex morphology. Med Eng Phys 38(5):450–457. https://doi.org/10.1016/j.medengphy.2016.02.003
Fitzpatrick JM, West JB (2001) The distribution of target registration error in rigid-body point-based registration. IEEE Trans Med Imaging 20(9):917–927. https://doi.org/10.1109/42.952729
Besl PJ, McKay ND (1992) Method for registration of 3-d shapes. In: Sensor fusion IV: control paradigms and data structures, vol 1611. Spie, pp 586–606
Liu C, Song Y, Ma X, Sun T (2023) Accurate and robust registration method for computer-assisted high tibial osteotomy surgery. Int J Comput Assist Radiol Surg 18(2):329–337. https://doi.org/10.1007/s11548-022-02720-1
Granger S, Pennec X, Roche A (2001) Rigid point-surface registration using an em variant of icp for computer guided oral implantology. In: Medical image computing and computer-assisted intervention—MICCAI 2001: 4th international conference Utrecht, The Netherlands, October 14–17, 2001 Proceedings, vol 4. Springer, pp 752–761. https://doi.org/10.1007/3-540-45468-3_90
Fitzpatrick CK, Maag C, Clary CW, Metcalfe A, Langhorn J, Rullkoetter PJ (2016) Validation of a new computational 6-dof knee simulator during dynamic activities. J Biomech 49(14):3177–3184. https://doi.org/10.1016/j.jbiomech.2016.07.040
Pavia JM (2015) Testing goodness-of-fit with the kernel density estimator: Gofkernel. J Stat Softw 66:1–27. https://doi.org/10.32614/cran.package.gofkernel
Yang J, Li H, Campbell D, Jia Y (2015) Go-icp: a globally optimal solution to 3d icp point-set registration. IEEE Trans Pattern Anal Mach Intell 38(11):2241–2254. https://doi.org/10.1109/tpami.2015.2513405
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