Rapid femur modeling method based on statistical shape model_Journal of Biomedical Engineering

Authors：

ZHANG Zhiwei ¹ , CHEN Zhenxian ² , ZHANG Zhifeng ³ , WANG Caimei ⁴ ,  JIN Zhongmin ^1,5

1. International Machinery Center, Xi’an Jiaotong University, Xi’an 710049, P. R. China;
2. School of Mechanical Engineering, Chang’an University, Xi’an 710064, P. R. China;
3. The Second Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010050, P. R. China;
4. Beijing AK Medical Co., LTD, Beijing 102299, P. R. China;
5. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China;

Corresponding author：

JIN Zhongmin, Email: zmjin@xjtu.edu.cn

Keywords：

Bone geometry; Three-dimensional reconstruction; Statistical shape model; Rapid modeling

DOI：

10.7507/1001-5515.202111005

Video：

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The geometric bone model of patients is an important basis for individualized biomechanical modeling and analysis, formulation of surgical planning, design of surgical guide plate, and customization of artificial joint. In this study, a rapid three-dimensional (3D) reconstruction method based on statistical shape model was proposed for femur. Combined with the patient plain X-ray film data, rapid 3D modeling of individualized patient femur geometry was realized. The average error of 3D reconstruction was 1.597–1.842 mm, and the root mean square error was 1.453–2.341 mm. The average errors of femoral head diameter, cervical shaft angle, offset distance and anteversion angle of the reconstructed model were 0.597 mm, 1.163°, 1.389 mm and 1.354°, respectively. Compared with traditional modeling methods, the new method could achieve rapid 3D reconstruction of femur more accurately in a shorter time. This paper provides a new technology for rapid 3D modeling of bone geometry, which is helpful to promote rapid biomechanical analysis for patients, and provides a new idea for the selection of orthopedic implants and the rapid research and development of customized implants.

Citation： ZHANG Zhiwei, CHEN Zhenxian, ZHANG Zhifeng, WANG Caimei, JIN Zhongmin. Rapid femur modeling method based on statistical shape model. Journal of Biomedical Engineering, 2022, 39(5): 862-869. doi: 10.7507/1001-5515.202111005 Copy

1.	吴琪, 朱建非, 席文明. 人工全髋关节置换假体设计的新思路. 医药与保健, 2014(12): 18-19.
2.	Mangesh D, Abhaykumar K. Effect of geometric parameters in the design of customized hip implants. J Med Eng Technol, 2017, 41(6): 429-436.
3.	陈夕辉, 柴伟, 高永昌, 等. DDH患者全髋关节置换中股骨偏心距对骨肌多体动力学和接触力学的影响. 医用生物力学, 2019, 34(3): 225-231.
4.	Luo C Q, Wu X D, Wan Y F, et al. Femoral stress changes after total hip arthroplasty with the ribbed prosthesis: A Finite Element Analysis. Biomed Res Int, 2020, 2020(8): 1-8.
5.	俞颖豪, 赵继军, 刘冬铖, 等. 数字图像术前规划辅助单髁置换对固定平台假体摆位的临床指导意义. 中国组织工程研究, 2021, 25(21): 3324-3331.
6.	彭小星, 赵刚, 吴琳琳. CT 和 X 线诊断老年股骨头坏死的临床价值. 中国老年学杂志, 2018, 38(13): 3183-3185.
7.	Markelj P, Tomaževič D, Likar B, et al. A review of 3D/2D registration methods for image-guided interventions. Med Image Anal, 2012, 16(3): 642-661.
8.	Pietro C, Costanza S, Gianluca O, 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 Robot, 2017, 13(4): e1832.
9.	Antonio M, Carlo R, Yary V, et al. Statistical Shape Model: comparison between ICP and CPD algorithms on medical applications. IJIDeM, 2021, 15(1): 85-89.
10.	Shui W Y, Zhou M Q, Steve M, et al. A computerized craniofacial reconstruction method for an unidentified skull based on statistical shape models. Multimed Tools APPL, 2020, 79(35-36): 25589-25611.
11.	Fahad P M, Tomoyuki M, Hiroshi T, et al. Construction of 3-D humeral head statistical shape model in CT images. Appl Sci, 2020, 10(16): 5591.
12.	Meynen A, Matthews H, Nauwelaers N, et al. Accurate reconstructions of pelvic defects and discontinuities using statistical shape models. Comput Methods Biomech Biomed Engin, 2020, 23(13): 1026-1033.
13.	Asvadi A, Dardenne G, Troccaz J, et al. Bone surface reconstruction and clinical features estimation from sparse landmarks and statistical shape models: A feasibility study on the femur. Med Eng Phys, 2021, 2021(95): 30-38.
14.	Daniel N, Ko S T, Anthony M J, et al. Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling. Gait Posture, 2020, 77(6): 269-275.
15.	Keating T C, Leong N, Beck E C, et al. Evaluation of statistical shape modeling in quantifying femoral morphologic differences between symptomatic and nonsymptomatic hips in patients with unilateral femoroacetabular impingement syndrome. Arthrosc Sports Med Rehabil, 2020, 2(2): e91-e95.
16.	Allison L C, Colin R S, Michael F V, et al. The effect of articular geometry features identified using statistical shape modelling on knee biomechanics. Med Eng Phys, 2019, 66: 47-55.
17.	Chandreshwar R, Clare K F, Paul J R, et al. A statistical finite element model of the knee accounting for shape and alignment variability. Med Eng Phys, 2013, 35(10): 1450-1456.
18.	Barratt D C, Chan C S K, Edwards P J, et al. Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging. Med Image Anal, 2008, 12(3): 358-374.
19.	Grant T M, Diamond L E, Pizzolato C, et al. Development and validation of statistical shape models of the primary functional bone segments of the foot. PeerJ, 2020, 8: e8397.
20.	Schumann S, Tannast M, Nolte Lutz P, et al. Validation of statistical shape model based reconstruction of the proximal femur-A morphology study. Med Eng Phys, 2010, 32(6): 638-644.
21.	Sarkalkan N, Weinans H, Zadpoor A A. Statistical shape and appearance models of bones. Bone, 2014, 60: 129-140.
22.	Perronne L, Haehnel O, Chevret S, et al. How is quality of life after total hip replacement related to the reconstructed anatomy? A study with low-dose stereoradiography. Diagn Interv Imaging, 2021, 102(2): 101-107.
23.	DE P E, Atzory F, Ferguson S J, et al. Contact force path in total hip arthroplasty: effect of cup medialisation in a whole-body simulation. Hip Int, 2021, 31(5): 624-631.
24.	Birnbaum K, Prescher A, Niethard F U. Hip centralizing forces of the iliotibial tract within various femoral neck angle. J Pediatr Orthop B, 2010, 19(2): 140-149.
25.	Duncan J S, Gerig G, Tang T S Y, et al. 2D/3D deformable registration using a hybrid atlas. Med Image Comput Comput Assist Interv, 2005, 8(2): 223-230.
26.	Shiode R, Kabashima M, Hiasa Y, et al. 2D-3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks. Sci Rep, 2021, 11(1): 1-12.
27.	Chaibi Y, Cresson T, Aubert B, et al. Fast 3D reconstruction of the lower limb using a parametric model and statistical inferences and clinical measurements calculation from biplanar X-rays. Comput Methods Biomech Biomed Engin, 2012, 15(5): 457-466.
28.	Zheng G Y, Lutz P N, Stephen J F. Scaled, patient-specific 3D vertebral model reconstruction based on 2D lateral fluoroscopy. Int J Comput Assist Radiol Surg, 2011, 6(3): 351-366.
29.	Baka N, Kaptein B L, 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.
30.	Said B, Max M, Stefan P, et al. 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput Med Imaging Graph, 2003, 27(5): 321-337.
31.	Yu W M, Chu C W, Moritz T, 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.
32.	Shun M, Wang Z J, Rui L. A CNN regression approach for real-time 2D/3D registration. IEEE Trans Med Imaging, 2016, 35(5): 1352-1363.
33.	Yang Y M, Rueckert D, Bull A M J. Predicting the shapes of bones at a joint: application to the shoulder. Comput Methods Biomech Biomed Engin, 2008, 11(1): 19-30.
34.	Jürgen D, Thomas G, Marcel L, et al. Error-controlled model approximation for Gaussian process morphable models. J Math Imaging Vis, 2019, 61(4): 443-457.
35.	Larsen R, Nielsen M, Sporring J, et al. Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and point distribution model. Med Image Comput Comput Assist Interv, 2006, 9(1): 25-32.
36.	Besl P J, McKay N D. A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell, 1992, 14(2): 239-256.
37.	王宾, 刘林, 侯榆青, 等. 应用改进迭代最近点方法的三维心脏点云配准. 光学精密工程, 2020, 28(2): 474-484.
38.	Jie B, Han B, Yao B, et al. Automatic virtual reconstruction of maxillofacial bone defects assisted by ICP (iterative closest point) algorithm and normal people database. Clin Oral Investig, 2021, 25(9): 1-10.
39.	崔琪. 一种基于K_D tree迭代的股骨骨折碎片复位方法. 哈尔滨: 哈尔滨理工大学, 2021.
40.	Luthi M, Gerig T, Jud C, et al. Gaussian process morphable models. IEEE Trans Pattern Anal Mach Intell, 2018, 40(8): 1860-1873.
41.	Pollock D S G. Wiener-Kolmogorov filtering, frequency-selective filtering, and polynomial regression. Economet Theor, 2007, 23(1): 71-88.

1. 吴琪, 朱建非, 席文明. 人工全髋关节置换假体设计的新思路. 医药与保健, 2014(12): 18-19.
2. Mangesh D, Abhaykumar K. Effect of geometric parameters in the design of customized hip implants. J Med Eng Technol, 2017, 41(6): 429-436.
3. 陈夕辉, 柴伟, 高永昌, 等. DDH患者全髋关节置换中股骨偏心距对骨肌多体动力学和接触力学的影响. 医用生物力学, 2019, 34(3): 225-231.
4. Luo C Q, Wu X D, Wan Y F, et al. Femoral stress changes after total hip arthroplasty with the ribbed prosthesis: A Finite Element Analysis. Biomed Res Int, 2020, 2020(8): 1-8.
5. 俞颖豪, 赵继军, 刘冬铖, 等. 数字图像术前规划辅助单髁置换对固定平台假体摆位的临床指导意义. 中国组织工程研究, 2021, 25(21): 3324-3331.
6. 彭小星, 赵刚, 吴琳琳. CT 和 X 线诊断老年股骨头坏死的临床价值. 中国老年学杂志, 2018, 38(13): 3183-3185.
7. Markelj P, Tomaževič D, Likar B, et al. A review of 3D/2D registration methods for image-guided interventions. Med Image Anal, 2012, 16(3): 642-661.
8. Pietro C, Costanza S, Gianluca O, 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 Robot, 2017, 13(4): e1832.
9. Antonio M, Carlo R, Yary V, et al. Statistical Shape Model: comparison between ICP and CPD algorithms on medical applications. IJIDeM, 2021, 15(1): 85-89.
10. Shui W Y, Zhou M Q, Steve M, et al. A computerized craniofacial reconstruction method for an unidentified skull based on statistical shape models. Multimed Tools APPL, 2020, 79(35-36): 25589-25611.
11. Fahad P M, Tomoyuki M, Hiroshi T, et al. Construction of 3-D humeral head statistical shape model in CT images. Appl Sci, 2020, 10(16): 5591.
12. Meynen A, Matthews H, Nauwelaers N, et al. Accurate reconstructions of pelvic defects and discontinuities using statistical shape models. Comput Methods Biomech Biomed Engin, 2020, 23(13): 1026-1033.
13. Asvadi A, Dardenne G, Troccaz J, et al. Bone surface reconstruction and clinical features estimation from sparse landmarks and statistical shape models: A feasibility study on the femur. Med Eng Phys, 2021, 2021(95): 30-38.
14. Daniel N, Ko S T, Anthony M J, et al. Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling. Gait Posture, 2020, 77(6): 269-275.
15. Keating T C, Leong N, Beck E C, et al. Evaluation of statistical shape modeling in quantifying femoral morphologic differences between symptomatic and nonsymptomatic hips in patients with unilateral femoroacetabular impingement syndrome. Arthrosc Sports Med Rehabil, 2020, 2(2): e91-e95.
16. Allison L C, Colin R S, Michael F V, et al. The effect of articular geometry features identified using statistical shape modelling on knee biomechanics. Med Eng Phys, 2019, 66: 47-55.
17. Chandreshwar R, Clare K F, Paul J R, et al. A statistical finite element model of the knee accounting for shape and alignment variability. Med Eng Phys, 2013, 35(10): 1450-1456.
18. Barratt D C, Chan C S K, Edwards P J, et al. Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging. Med Image Anal, 2008, 12(3): 358-374.
19. Grant T M, Diamond L E, Pizzolato C, et al. Development and validation of statistical shape models of the primary functional bone segments of the foot. PeerJ, 2020, 8: e8397.
20. Schumann S, Tannast M, Nolte Lutz P, et al. Validation of statistical shape model based reconstruction of the proximal femur-A morphology study. Med Eng Phys, 2010, 32(6): 638-644.
21. Sarkalkan N, Weinans H, Zadpoor A A. Statistical shape and appearance models of bones. Bone, 2014, 60: 129-140.
22. Perronne L, Haehnel O, Chevret S, et al. How is quality of life after total hip replacement related to the reconstructed anatomy? A study with low-dose stereoradiography. Diagn Interv Imaging, 2021, 102(2): 101-107.
23. DE P E, Atzory F, Ferguson S J, et al. Contact force path in total hip arthroplasty: effect of cup medialisation in a whole-body simulation. Hip Int, 2021, 31(5): 624-631.
24. Birnbaum K, Prescher A, Niethard F U. Hip centralizing forces of the iliotibial tract within various femoral neck angle. J Pediatr Orthop B, 2010, 19(2): 140-149.
25. Duncan J S, Gerig G, Tang T S Y, et al. 2D/3D deformable registration using a hybrid atlas. Med Image Comput Comput Assist Interv, 2005, 8(2): 223-230.
26. Shiode R, Kabashima M, Hiasa Y, et al. 2D-3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks. Sci Rep, 2021, 11(1): 1-12.
27. Chaibi Y, Cresson T, Aubert B, et al. Fast 3D reconstruction of the lower limb using a parametric model and statistical inferences and clinical measurements calculation from biplanar X-rays. Comput Methods Biomech Biomed Engin, 2012, 15(5): 457-466.
28. Zheng G Y, Lutz P N, Stephen J F. Scaled, patient-specific 3D vertebral model reconstruction based on 2D lateral fluoroscopy. Int J Comput Assist Radiol Surg, 2011, 6(3): 351-366.
29. Baka N, Kaptein B L, 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.
30. Said B, Max M, Stefan P, et al. 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput Med Imaging Graph, 2003, 27(5): 321-337.
31. Yu W M, Chu C W, Moritz T, 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.
32. Shun M, Wang Z J, Rui L. A CNN regression approach for real-time 2D/3D registration. IEEE Trans Med Imaging, 2016, 35(5): 1352-1363.
33. Yang Y M, Rueckert D, Bull A M J. Predicting the shapes of bones at a joint: application to the shoulder. Comput Methods Biomech Biomed Engin, 2008, 11(1): 19-30.
34. Jürgen D, Thomas G, Marcel L, et al. Error-controlled model approximation for Gaussian process morphable models. J Math Imaging Vis, 2019, 61(4): 443-457.
35. Larsen R, Nielsen M, Sporring J, et al. Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and point distribution model. Med Image Comput Comput Assist Interv, 2006, 9(1): 25-32.
36. Besl P J, McKay N D. A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell, 1992, 14(2): 239-256.
37. 王宾, 刘林, 侯榆青, 等. 应用改进迭代最近点方法的三维心脏点云配准. 光学精密工程, 2020, 28(2): 474-484.
38. Jie B, Han B, Yao B, et al. Automatic virtual reconstruction of maxillofacial bone defects assisted by ICP (iterative closest point) algorithm and normal people database. Clin Oral Investig, 2021, 25(9): 1-10.
39. 崔琪. 一种基于K_D tree迭代的股骨骨折碎片复位方法. 哈尔滨: 哈尔滨理工大学, 2021.
40. Luthi M, Gerig T, Jud C, et al. Gaussian process morphable models. IEEE Trans Pattern Anal Mach Intell, 2018, 40(8): 1860-1873.
41. Pollock D S G. Wiener-Kolmogorov filtering, frequency-selective filtering, and polynomial regression. Economet Theor, 2007, 23(1): 71-88.

Journal of Biomedical Engineering

Rapid femur modeling method based on statistical shape model

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