Convolutional Bayesian Models for Anatomical Landmarking on Multi-dimensional Shapes-Reference-Cited by-同舟云学术

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Convolutional Bayesian Models for Anatomical Landmarking on Multi-dimensional Shapes

Published:2020 Issue: Volume: Page:786-796
ISSN:0302-9743
Container-title:Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
language:
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Author:

Fan Yonghui,Wang Yalin

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-59719-1_76

Reference33 articles.

1. Al-Aifari, R., Daubechies, I., Lipman, Y.: Continuous procrustes distance between two surfaces. Commun. Pure Appl. Math. 66(6), 934–964 (2013)

2. Aubry, M., Schlickewei, U., Cremers, D.: The wave kernel signature: a quantum mechanical approach to shape analysis. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1626–1633. IEEE (2011)

3. Berline, N., Getzler, E., Vergne, M.: Heat Kernels and Dirac Operators. Springer, Heidelberg (2003)

4. Bonaretti, S., Seiler, C., Boichon, C., Reyes, M., Büchler, P.: Image-based vs. mesh-based statistical appearance models of the human femur: implications for finite element simulations. Medical Eng. Phys. 36(12), 1626–1635 (2014)

5. Boyer, D.M., Lipman, Y., Clair, E.S., Puente, J., Patel, B.A., Funkhouser, T., Jernvall, J., Daubechies, I.: Algorithms to automatically quantify the geometric similarity of anatomical surfaces. Proc. Natl. Acad. Sci. 108(45), 18221–18226 (2011)

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1. Geometry-Aware Hierarchical Bayesian Learning on Manifolds;2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2022-01

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