Zusammenfassung
Vertebral corner points are frequently used landmarks for a vast variety of orthopedic and trauma surgical applications. Algorithmic approaches that are designed to automatically detect them on 2D radiographs have to cope with varying image contrast, high noise levels, and superimposed soft tissue. To enforce an anatomically correct landmark configuration in presence of these limitations, this study investigates a shape constraint technique based on data-driven encodings of the spine geometry. A contractive PointNet autoencoder is used to map numerical landmark coordinate representations onto a low-dimensional shape manifold. A distance norm between prediction and ground truth encodings then serves as an additional loss term during optimization. The method is compared and evaluated on the SpineWeb16 dataset. Small improvements can be observed, recommending further analysis of the encoding design and composite cost function.
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References
Kordon F, Fischer P, Privalov M, et al. Multi-task localization and segmentation for x-ray guided planning in knee surgery. In: Shen D, Liu T, Peters TM, et al., editors. Proc MICCAI. Springer; 2019. p. 622–630.
Urschler M, Ebner T, Štern D. Integrating geometric configuration and appearance information into a unified framework for anatomical landmark localization. Med Image Anal. 2018;43:23–36.
Wu H, Bailey C, Rasoulinejad P, et al. Automatic landmark estimation for adolescent idiopathic scoliosis assessment using BoostNet. In: Descoteaux M, Maier-Hein L, Franz A, et al., editors. Proc MICCAI. Springer; 2017. p. 127–135.
Imran AAZ, Huang C, Tang H, et al.. Bipartite distance for shape-aware landmark detection in spinal x-ray images; 2020. arXiv:2005.14330v1 [eess.IV].
Oktay O, Ferrante E, Kamnitsas K, et al. Anatomically constrained neural networks (ACNN): Application to cardiac image enhancement and segmentation. IEEE Trans Med Imaging. 2018;37(2):384–395.
Nibali A, He Z, Morgan S, et al. Numerical coordinate regression with convolutional neural networks. CoRR. 2018;abs/1801.07372.
Newell A, Yang K, Deng J. Stacked hourglass networks for human pose estimation. In: Leibe B, Matas J, Sebe N, et al., editors. Proc ECCV. Springer; 2016. p. 483–499.
Charles RQ, Su H, Kaichun M, et al. PointNet: Deep learning on point sets for 3D classification and segmentation. In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit; 2017. p. 77–85.
Yu T, Kumar S, Gupta A, et al.. Gradient surgery for multi-task learning; 2020. arXiv:2001.06782v3 [cs.LG].
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© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Kordon, F., Maier, A., Kunze, H. (2021). Latent Shape Constraint for Anatomical Landmark Detection on Spine Radiographs. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_85
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DOI: https://doi.org/10.1007/978-3-658-33198-6_85
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