Abstract
The paper discusses the initial results obtained for the generation of canonical 3D models of anatomical parts, built on real patient data. 3D canonical models of anatomy are key elements in a computer-assisted diagnosis; for instance, they can support pathology detection, semantic annotation of patient-specific 3D reconstructions, quantification of pathological markers. Our approach is focused on carpal bones and on the elastic analysis of 3D reconstructions of these bones, which are segmented from MRI scans, represented as 0-genus triangle meshes, and parameterized on the sphere. The original method [8] relies on a set of sparse correspondences, defined as matching vertices. For medical applications, it is desirable to constrain the mean shape generation to set-up the correspondences among a larger set of anatomical landmarks, including vertices, lines, and areas. Preliminary results are discussed and future development directions are sketched.
Chapter PDF
Similar content being viewed by others
References
Banerjee, I., Agibetov, A., Catalano, C., Patané, G., Spagnuolo, M.: Semantic annotation of patient-specific 3D anatomical models. In: IEEE Proceedings of the International Conference on Cyberworlds (in press, 2015)
Banerjee, I., Catalano, C.E., Robbiano, F., Spagnuolo, M.: Accessing and representing knowledge in the medical field: visual and lexical modalities. In: 3D Multiscale Physiological Human, pp. 297–316. Springer, London (2014)
Blume, A., Chun, W., Kogan, D., Kokkevis, V., Weber, N., Petterson, R.W., Zeiger, R.: Google body: 3D human anatomy in the browser. In: ACM SIGGRAPH 2011 Talks, p. 19. ACM (2011)
Glaunès, J., Vaillant, M., Miller, M.: Landmark matching via large deformation diffeomorphisms on the sphere. Journal of Mathematical Imaging and Vision 20(1–2), 179–200 (2004)
Jermyn, I.H., Kurtek, S., Klassen, E., Srivastava, A.: Elastic shape matching of parameterized surfaces using square root normal fields. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 804–817. Springer, Heidelberg (2012)
Kurtek, S., Klassen, E., Ding, Z., Srivastava, A.: A novel Riemannian framework for shape analysis of 3D objects. In: IEEE CVPR, pp. 1625–1632 (2010)
Kurtek, S., Klassen, E., Gore, J.C., Ding, Z., Srivastava, A.: Elastic geodesic paths in shape space of parameterized surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(9), 1717–1730 (2012)
Kurtek, S., Srivastava, A., Klassen, E., Laga, H.: Landmark-guided elastic shape analysis of spherically-parameterized surfaces. Comper Graphics Forum 32, 429–438 (2013)
Moore, D.C., Crisco, J.J., Trafton, G.T., Leventhal, E.: A digital database of wrist bone anatomy and carpal kinematics. Journal of Biomechanics 40(11), 2537–2542 (2007)
Qualter, J., Sculli, F., Oliker, A., Napier, Z., Lee, S., Garcia, J., Frenkel, S., Harnik, V., Triola, M.: The biodigital human: a web-based 3D platform for medical visualization and education. Studies in Health Technology and Informatics 173, 359–361 (2011)
Rajamani, T.K., Styner, A.M., Talib, H., Zheng, G., Nolte, L.P., Ballester, A.: Statistical deformable bone models for robust 3d surface extrapolation from sparse data. Medical Image Analysis 11(2), 99–109 (2007)
Subburaj, K., Ravi, B., Agarwal, M.: Automated identification of anatomical landmarks on 3d bone models reconstructed from ct scan images. Computerized Medical Imaging and Graphics 33(5), 359–368 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Banerjee, I., Laga, H., Patanè, G., Kurtek, S., Srivastava, A., Spagnuolo, M. (2015). Generation of 3D Canonical Anatomical Models: An Experience on Carpal Bones. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-23222-5_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23221-8
Online ISBN: 978-3-319-23222-5
eBook Packages: Computer ScienceComputer Science (R0)
