Abstract
Quantifying shape variations in articulated joints is of utmost interest to understand the underlying joint biomechanics and associated clinical symptoms. For joint comparisons and analysis, the relative positions of the bones can confound subsequent analysis. Clinicians design specific image acquisition protocols to neutralize the individual pose variations. However, recent studies have shown that even specific acquisition protocols fail to achieve consistent pose. The individual pose variations are largely attributed to the day-to-day functioning of the patient, such as gait during walk, as well as interactions between specific morphologies and joint alignment. This paper presents a novel two-step method to neutralize such patient-specific variations while simultaneously preserving the inherent relationship of the articulated joint. The resulting shape models are then used to discover clinically relevant shape variations in a population of hip joints.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Thanks to Dr. Tokunaga and Keisuke Uemura for data collection and providing this dataset.
References
Agrawal, P., Whitaker, R.T., Elhabian, S.Y.: Learning deep features for automated placement of correspondence points on ensembles of complex shapes. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 185–193. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_22
Audenaert, E., Van den Eynde, J., de Almeida, D., Steenackers, G., Vandermeulen, D., Claes, P.: Separating positional noise from neutral alignment in multicomponent statistical shape models. Bone Rep. 12, 100243 (2020)
Beaulé, P.E., et al.: Unravelling the hip pistol grip/cam deformity: origins to joint degeneration. J. Orthop. Res.® 36(12), 3125–3135 (2018)
Bhalodia, R., Dvoracek, L.A., Ayyash, A.M., Kavan, L., Whitaker, R., Goldstein, J.A.: Quantifying the severity of metopic craniosynostosis: a pilot study application of machine learning in craniofacial surgery. J. Craniofac. Surg. 31(3), 697–701 (2020)
Bindernagel, M., Kainmueller, D., Seim, H., Lamecker, H., Zachow, S., Hege, H.C.: An articulated statistical shape model of the human knee. In: Bildverarbeitung für die Medizin 2011, pp. 59–63. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19335-4_14
Bossa, M.N., Olmos, S.: Statistical model of similarity transformations: building a multi-object pose. In: CVPRW 2006, p. 59. IEEE (2006)
Bossa, M.N., Olmos, S.: Multi-object statistical pose+shape models. In: ISBI, pp. 1204–1207 (2007)
Bryan, R., Nair, P.B., Taylor, M.: Use of a statistical model of the whole femur in a large scale, multi-model study of femoral neck fracture risk. J. Biomech. 42(13), 2171–2176 (2009)
Cates, J., et al.: Computational shape models characterize shape change of the left atrium in atrial fibrillation. Clin. Med. Insights Cardiol. 8, 99–109 (2014). CMC-S15710
Cates, J., Elhabian, S., Whitaker, R.: Shapeworks: particle-based shape correspondence and visualization software. In: Statistical Shape and Deformation Analysis, pp. 257–298. Elsevier (2017)
Cates, J., Fletcher, P.T., Styner, M., Hazlett, H.C., Whitaker, R.: Particle-based shape analysis of multi-object complexes. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 477–485. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85988-8_57
Cates, J., Fletcher, P.T., Styner, M., Shenton, M., Whitaker, R.: Shape modeling and analysis with entropy-based particle systems. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 333–345. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73273-0_28
Datar, M., Lyu, I., Kim, S.H., Cates, J., Styner, M.A., Whitaker, R.: Geodesic distances to landmarks for dense correspondence on ensembles of complex shapes. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 19–26. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_3
Fouefack, J.R., Alemneh, T., Borotikar, B., Burdin, V., Douglas, T.S., Mutsvangwa, T.: Statistical shape-kinematics models of the skeletal joints: application to the shoulder complex. In: EMBC, pp. 4815–4818 (2019)
Fouefack, J.R., Borotikar, B., Douglas, T.S., Burdin, V., Mutsvangwa, T.E.M.: Dynamic multi-object Gaussian process models: a framework for data-driven functional modelling of human joints (2020)
Galloway, F., et al.: A large scale finite element study of a cementless osseointegrated tibial tray. J. Biomech. 46(11), 1900–1906 (2013)
Goparaju, A., et al.: On the evaluation and validation of off-the-shelf statistical shape modeling tools: a clinical application. In: Reuter, M., et al. (eds.) ShapeMI 2018. LNCS, vol. 11167, pp. 14–27. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04747-4_2
Gorczowski, K., et al.: Statistical shape analysis of multi-object complexes. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)
Gower, J.C.: Generalized procrustes analysis. Psychometrika 40(1), 33–51 (1975). https://doi.org/10.1007/BF02291478
Harris, M.D., Datar, M., Whitaker, R.T., Jurrus, E.R., Peters, C.L., Anderson, A.E.: Statistical shape modeling of cam femoroacetabular impingement. J. Orthop. Res. 31(10), 1620–1626 (2013)
Kainmueller, D., Lamecker, H., Zachow, S., Hege, H.C.: An articulated statistical shape model for accurate hip joint segmentation. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6345–6351. IEEE (2009)
Kozic, N., et al.: Optimisation of orthopaedic implant design using statistical shape space analysis based on level sets. Med. Image Anal. 14(3), 265–275 (2010)
Smoger, L.M.: Statistical modeling to characterize relationships between knee anatomy and kinematics. J. Orthop. Res.® 33(11), 1620–1630 (2015)
Uemura, K., Atkins, P.R., Maas, S.A., Peters, C.L., Anderson, A.E.: Three-dimensional femoral head coverage in the standing position represents that measured in vivo during gait. Clin. Anat. 31(8), 1177–1183 (2018)
Wang, D., Shi, L., Griffith, J.F., Qin, L., Yew, D.T., Riggs, C.M.: Comprehensive surface-based morphometry reveals the association of fracture risk and bone geometry. J. Orthop. Res. 30(8), 1277–1284 (2012)
Zhao, Z., Taylor, W.D., Styner, M., Steffens, D.C., Krishnan, K.R.R., MacFall, J.R.: Hippocampus shape analysis and late-life depression. PLoS ONE 3(3), e1837 (2008)
Funding Acknowledgments
This work was supported by the National Institutes of Health under grant numbers NIBIB-U24EB029011, NIAMS-R01AR076120, NHLBI-R01HL135568, NIBIB-R01EB016701, and NIGMS-P41GM103545. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Agrawal, P., Mozingo, J.D., Elhabian, S.Y., Anderson, A.E., Whitaker, R.T. (2020). Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints. In: Reuter, M., Wachinger, C., Lombaert, H., Paniagua, B., Goksel, O., Rekik, I. (eds) Shape in Medical Imaging. ShapeMI 2020. Lecture Notes in Computer Science(), vol 12474. Springer, Cham. https://doi.org/10.1007/978-3-030-61056-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-61056-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61055-5
Online ISBN: 978-3-030-61056-2
eBook Packages: Computer ScienceComputer Science (R0)
