Abstract
3D anatomical landmarks play an important role in health research. Their automated prediction/localization thus becomes a vital task. In this paper, we introduce a deformation method for 3D anatomical landmarks prediction. It utilizes a source model with anatomical landmarks which are annotated by clinicians and deforms this model non-rigidly to match the target model. Two constraints are presented in the optimization, which are responsible for alignment and smoothness, respectively. Experiments are performed on our dataset and the results demonstrate the robustness of our method and show that it yields better performance than the previous techniques in most cases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
O’Neil, A.Q., et al.: Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)
Zheng, Y., Liu, D., Georgescu, B., Nguyen, H., Comaniciu, D.: 3D deep learning for efficient and robust landmark detection in volumetric data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 565–572. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_69
Zhang, J., Liu, M., Shen, D.: Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE Trans. Image Process. 26(10), 4753–4764 (2017)
Bier, B., et al.: X-ray-transform invariant anatomical landmark detection for pelvic trauma surgery. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 55–63. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_7
Fang, S., Raghavan, R., Richtsmeier, J.T.: Volume morphing methods for landmark-based 3D image deformation. In: Medical Imaging 1996: Image Processing, vol. 2710. International Society for Optics and Photonics (1996)
Sumner, R.W., Schmid, J., Pauly, M.: Embedded deformation for shape manipulation. In: ACM SIGGRAPH 2007 Papers, p. 80-es (2007)
Lu, X., et al.: Unsupervised articulated skeleton extraction from point set sequences captured by a single depth camera. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)
Alansary, A., et al.: Evaluating reinforcement learning agents for anatomical landmark detection. Medical Image Anal. 53, 156–164 (2019)
Ghesu, F.C., Georgescu, B., Grbic, S., Maier, A.K., Hornegger, J., Comaniciu, D.: Robust multi-scale anatomical landmark detection in incomplete 3D-CT Data. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 194–202. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_23
Ebner, T., Stern, D., Donner, R., Bischof, H., Urschler, M.: Towards automatic bone age estimation from MRI: localization of 3D anatomical landmarks. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 421–428. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_53
Subburaj, K., Ravi, B., Agarwal, M.: Automated identification of anatomical landmarks on 3D bone models reconstructed from CT scan images. Comput. Med. Imaging Graph. 33(5), 359–368 (2009)
Baek, S.-Y., et al.: Automated bone landmarks prediction on the femur using anatomical deformation technique. Comput.-Aided Design 45(2), 505–510 (2013)
Li, H., et al.: Robust single-view geometry and motion reconstruction. ACM Trans. Graph. (ToG) 28(5), 1–10 (2009)
Lu, X., et al.: 3D articulated skeleton extraction using a single consumer-grade depth camera. Comput. Vision Image Underst. 188, 102792 (2019)
Yao, Y., et al.: Quasi-Newton solver for robust non-rigid registration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Holland, P.W., Welsch, R.E.: Robust regression using iteratively reweighted least-squares. Commun. Stat.-Theory Methods 6(9), 813–827 (1977)
Boyd, S., Parikh, N., Chu, E.: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Now Publishers Inc. (2011)
Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2262–2275 (2010)
Dai, H., et al.: A 3D morphable model of craniofacial shape and texture variation. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
Wand, M., et al.: Efficient reconstruction of nonrigid shape and motion from real-time 3D scanner data. ACM Trans. Graph. (TOG) 28(2), 1–15 (2009)
Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611. International Society for Optics and Photonics (1992)
Myronenko, A., Song, X.: On the closed-form solution of the rotation matrix arising in computer vision problems. arXiv preprint arXiv:0904.1613 (2009)
Jacobson, A., et al.: libigl: a simple C++ geometry processing library (2018)
Guennebaud, G., Jacob, B., et al.: Eigen v3 (2010)
Cignoni, P., et al.: MeshLab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fung, S., Lu, X., Mykolaitis, M., Razzak, I., Kostkevičius, G., Ozerenskis, D. (2023). Anatomical Landmarks Localization for 3D Foot Point Clouds. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-031-30111-7_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30110-0
Online ISBN: 978-3-031-30111-7
eBook Packages: Computer ScienceComputer Science (R0)