Abstract
In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression.
Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated.
We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) Statistical Shape Models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).
After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods or machine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agostini V, Balestra G, Knaflitz M (2014) Segmentation and classification of gait cycles. IEEE Trans Neural Syst Rehabil Eng 22(5):946–952
Akbari Shandiz M (2015) Component placement in hip and knee replacement surgery: device development, imaging and biomechanics. Doctoral dissertation, University of Calgary
Akbari Shandiz M, Boulos P, Saevarsson SK, Ramm H, Fu CK, Miller S, Zachow S, Anglin C (2018) Changes in knee shape and geometry resulting from total knee arthroplasty. Proc Inst of Mech Eng H J Eng Med 232(1):67–79
Ambellan F, Tack A, Ehlke M, Zachow S (2019) Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative. Med Image Anal 52:109–118
Bergmann RA, Thompson SA, Afifi AK, Saadeh FA (1988) Compendium of human anatomic variation. Urban & Schwarzenberg. https://www.anatomyatlases.org
Bernard F, Salamanca L, Thunberg J, Tack A, Jentsch D, Lamecker H, Zachow S, Hertel F, Goncalves J, Gemmar P (2017) Shape-aware surface reconstruction from sparse 3D point-clouds. Med Image Anal 38:77–89
Bindernagel M, Kainmüller D, Seim H, Lamecker H, Zachow S, Hege HC (2011) An articulated statistical shape model of the human knee. In: Bildverarbeitung für die Medizin, pp 59–63
Boisvert J, Cheriet F, Pennec X, Labelle H, Ayache N (2008) Geometric variability of the scoliotic spine using statistics on articulated shape models. IEEE Trans Med Imaging 27(4):557–568
Bookstein FL (1986) Size and shape spaces for landmark data in two dimensions. Stat Sci 1(2):181–222
Bruse JL, Zuluaga MA, Khushnood A, McLeod K, Ntsinjana HN, Hsia TY, Taylor AM, Schievano S (2017) Detecting clinically meaningful shape clusters in medical image data: metrics analysis for hierarchical clustering applied to healthy and pathological aortic arches. IEEE Trans Biomed Eng 64(10):2373–2383
Davis RH, Twining CJ, Cootes TF, Waterton JC, Taylor CJ (2002) A minimum description length approach to statistical shape modelling. IEEE Trans Med Imaging 21:525–537
Dworzak J, Lamecker H, von Berg J, Klinder T, Lorenz C, Kainmüller D, Hege HC, Zachow S (2010) 3D reconstruction of the human rib cage from 2D projection images using a statistical shape model. Int J Comput Assist Radiol Surg 5(2):111–124
Ehlke M, Ramm H, Lamecker H, Hege HC, Zachow S (2013) Fast generation of virtual X-ray images for reconstruction of 3D anatomy. IEEE Trans Visual Comput Graph 19(12):2673–2682
Galloway F, Kahnt M, Ramm H, Worsley P, Zachow S, Nair P, Taylor M (2013) A large scale finite element study of a cementless osseointegrated tibial tray. J Biomech 46(11):1900–1906
Gerig G, Fishbaugh J, Sadeghi N (2016) Longitudinal modeling of appearance and shape and its potential for clinical use. Med Image Anal 33:114–121
German National Cohort. German federal and local state governments and the Helmholtz Association. https://nako.de/informationen-auf-englisch
Gomes J, Darsa L, Costa B, Velho L (1999) Warping and morphing of graphical objects. Morgan Kaufmann Publishers, San Francisco
Grewe CM, Zachow S (2016) Fully automated and highly accurate dense correspondence for facial surfaces. In: European conference on computer vision, pp 552–568
Griffiths I (2012) Choosing running shoes: the evidence behind the recommendations. http://www.sportspodiatryinfo.co.uk/choosing-running-shoes-the-evidence-behind-the-recommendations
Gundelwein L, Ramm H, Goubergrits L, Kelm M, Lamecker H (2018) 3D Shape analysis for coarctation of the Aorta. In: International workshop on shape in medical imaging, pp 73–77
Hochfeld M, Lamecker H, Thomale UW, Schulz M, Zachow S, Haberl H (2014) Frame-based cranial reconstruction. J Neurosurg Pediatr 13(3):319–323
The Osteoarthritis Initiative, National Institute of Health, USA. https://oai.nih.gov/
Ingraham L (2018) You might just be weird: the clinical significance of normal – and not so normal – anatomical variations. https://www.painscience.com/articles/anatomical-variation.php
Jones KL, Jones MC, Del Campo M (2013) Smith’s recognizable patterns of human malformation, 7th edn. Elsevier/Saunders, London
Kainmüller D, Lange T, Lamecker H (2007) Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: MICCAI workshop 3D segmentation in the clinic: a grand challenge, pp 109–116
Kainmüller D, Lamecker H, Zachow S, Hege HC (2009) An articulated statistical shape model for accurate hip joint segmentation. In: IEEE Engineering in medicine and biology society annual conference, pp 6345–6351
Kamer L, Noser H, Lamecker H, Zachow S, Wittmers A, Kaup T, Schramm A, Hammer B (2006) Three-dimensional statistical shape analysis – a useful tool for developing a new type of orbital implant? AO Development Institute, New Products Brochure 2/06, pp 20–21
Kendall DG, Barden D, Carne TK, Le H (2009) Shape and shape theory. Wiley, New York
Klinder T, Wolz R, Lorenz C, Franz A, Ostermann J (2008) Spine segmentation using articulated shape models. In: International conference on medical image computing and computer-assisted intervention, pp 227–234
Lamecker H (2008) Variational and statistical shape modeling for 3D geometry reconstruction. Doctoral dissertation, Freie Universität Berlin
Lamecker H, Zachow S (2016) Statistical shape modeling of musculoskeletal structures and its applications. In: Computational radiology for orthopaedic interventions. Springer, pp 1–23
Lamecker H, Lange T, Seebaß M (2002) A statistical shape model for the liver. In: International conference on medical image computing and computer-assisted intervention, pp 421–427
Lamecker H, Seebaß M, Hege HC, Deuflhard P (2004) A 3D statistical shape model of the pelvic bone for segmentation. In: Medical imaging 2004: image processing, vol. 5370, pp 1341–1352
Lamecker H, Zachow S, Haberl H, Stiller M (2005) Medical applications for statistical shape models. Computer Aided Surgery around the Head, Fortschritt-Berichte VDI – Biotechnik/Medizintechnik 17(258):1–61
Lamecker H, Wenckebach TH, Hege HC (2006a) Atlas-based 3D-shape reconstruction from X-ray images. In: IEEE 18th International conference on pattern recognition, pp 371–374
Lamecker H, Zachow S, Hege HC, Zockler M, Haberl H (2006b) Surgical treatment of craniosynostosis based on a statistical 3D-shape model: first clinical application. Int J Comput Assist Radiol Surg 1(Suppl 7):253–254
Moore KL (1989) Meaning of “normal”. Clin Anat 2(4):235–239
Mukhopadhyay A, Victoria OSM, Zachow S, Lamecker H (2016) Robust and accurate appearance models based on joint dictionary learning data from the osteoarthritis initiative. In: International workshop on patch-based techniques in medical imaging, pp 25–33
Nava-Yazdani E, Hege H-C, von Tycowicz C, Sullivan T (2018) A shape trajectories approach to longitudinal statistical analysis. Technical report, ZIB-report 18-42
Rybak J, Kuß A, Hans L, Zachow S, Hege HC, Lienhard M, Singer J, Neubert K, Menzel R (2010) The digital bee brain: integrating and managing neurons in a common 3D reference system. Front Syst Neurosci 4:1–30
Sañudo JR, Vázquez R, Puerta J (2003) Meaning and clinical interest of the anatomical variations in the 21st century. Eur J Anat 7(1):1–3
Seim H, Kainmüller D, Lamecker H, Bindernagel M, Malinowski J, Zachow S (2010) Model-based auto-segmentation of knee bones and cartilage in MRI data. In: MICCAI workshop medical image analysis for the clinic, pp 215–223
Study of Health in Pomerania. Forschungsverbund Community Medicine at Greifswald Medical School. http://www2.medizin.uni-greifswald.de/cm/fv/ship
Tack A, Zachow S (2019) Accurate automated volumetry of cartilage of the knee using convolutional neural networks: data from the osteoarthritis initiative. In: IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), (accepted for publication)
Tack A, Mukhopadhyay A, Zachow S (2018) Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative. Osteoarthr Cartil 26(5):680–688
Thompson DAW (1917) On growth and form. Cambridge University Press, Cambridge
Toga AW (1998) Brain warping. Elsevier, Amsterdam
van Kaick O, Zhang H, Hamarneh G, CohenOr D (2011) A survey on shape correspondence. Comput Graphics Forum 30(6):1681–1707
Vidal-Migallon I, Ramm H, Lamecker H (2015) Reconstruction of partial liver shapes based on a statistical 3D shape model. In: Shape symposium Delemont Switzerland, p 22
von Berg J, Dworzak J, Klinder T, Manke D Kreth A, Lamecker H, Zachow S, Lorenz C (2011) Temporal subtraction of chest radiographs compensating pose differences. In: Medical imaging 2011: image processing, 79620U
von 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
Wilson DAJ, Anglin C, Ambellan F, Grewe CM, Tack A, Lamecker H, Dunbar M, Zachow S (2017) Validation of three-dimensional models of the distal femur created from surgical navigation point cloud data for intraoperative and postoperative analysis of total knee arthroplasty. Int J Comput Assist Radiol Surg 12(12):2097–2105
Yao J (2002) A statistical bone density atlas and deformable medical image registration. Doctoral dissertation, Johns Hopkins University
Zachow S, Lamecker H, Elsholtz B, Stiller M (2005) Reconstruction of mandibular dysplasia using a statistical 3D shape model. In: Computer Assisted Radiology and Surgery (CARS), pp 1238–1243
Zachow S, Zilske M, Hege HC (2007) 3D reconstruction of individual anatomy from medical image data: Segmentation and geometry processing. In: Proceedings of the 25. ANSYS conference and CADFEM users’ meeting, ZIB Preprint 07-41 available at opus4.kobv.de/opus4-zib/files/1044/ZR_07_41.pdf
Zachow S, Kubiack K, Malinowski J, Lamecker H, Essig H, Gellrich NC (2010) Modellgestützte chirurgische Rekonstruktion komplexer Mittelgesichtsfrakturen. In: Proceedings of Biomedical Technology Conference (BMT), pp 107–108
SHIP (2019) Study of Health in Pomerania. Forschungsverbund Community Medicine at Greifswald Medical School. http://www2.medizin.uni-greifswald.de/cm/fv/ship
OAI (2019) The Osteoarthritis Initiative, National Institute of Health, USA. https://oai.nih.gov/
GNC (2019) German National Cohort. German federal and local state governments and the Helmholtz Association. https://nako.de/informationen-auf-englisch
https://opus4.kobv.de/opus4-zib/files/1044/ZR_07_41.pdf
Zachow S, Zilske M, Hege HC (2007) 3D reconstruction of individual anatomy from medical image data: Segmentation and geometry processing. In: Proceedings of the 25. ANSYS conference and CADFEM users’ meeting, ZIB Preprint 07-41 available at https://opus4.kobv.de/opus4-zib/files/1044/ZR_07_41.pdf
Acknowledgements
The authors gratefully acknowledge the financial support by the German research foundation (DFG) within the research center MATHEON (Germany´s Excellence Strategy – MATH+ : The Berlin Mathematics Research Center, EXC-2046/1 – project ID: 390685689), the German federal ministry of education and research (BMBF) within the research network on musculoskeletal diseases, grant no. 01EC1408B (Overload/PrevOP) and grant no. 01EC1406E (TOKMIS), the research program “Medical technology solutions for digital health care”, grant no. 13GW0208C (ArtiCardio), as well as the BMBF research campus MODAL.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ambellan, F., Lamecker, H., von Tycowicz, C., Zachow, S. (2019). Statistical Shape Models: Understanding and Mastering Variation in Anatomy. In: Rea, P. (eds) Biomedical Visualisation . Advances in Experimental Medicine and Biology, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-19385-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-19385-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19384-3
Online ISBN: 978-3-030-19385-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)