Abstract
Accurate cephalometric landmark detection is a crucial step in orthodontic diagnosis and therapy planning. However, existing deep learning-based methods lack the ability to explicitly model the complex dependencies among visual features and landmarks. Therefore, they fail to adaptively encode the landmark’s global structure constraint into the representation of visual concepts and suffer from large biases in landmark localization. In this work, we propose CephalFormer, which exploits the correlations between visual concepts and landmarks to provide meaningful guidance for accurate 2D and 3D cephalometric landmark detection. CephalFormer explores local-global anatomical contents in a coarse-to-fine fashion and consists of two stages: (1) a new efficient Transformer-based architecture for coarse landmark localization; (2) a novel paradigm based on self-attention to represent visual clues and landmarks in one coherent feature space for fine-scale landmark detection. We evaluated CephalFormer on two public cephalometric landmark detection benchmarks and a real-patient dataset consisting of 150 skull CBCT volumes. Experiments show that CephalFormer significantly outperforms the state-of-the-art methods, demonstrating its generalization capability and stability to naturally handle both 2D and 3D scenarios under a unified framework.
Y. Jiang and Y. Li—Equally contributed.
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Acknowledgements
This research was partially supported by the National Natural Science Foundation of China under Grant 61972343, the National Major Scientific Research Instrument Development Project under Grant 81827804, and the Key Research and Development Program of Zhejiang Province under Grant 2021C03032.
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Jiang, Y., Li, Y., Wang, X., Tao, Y., Lin, ., Lin, H. (2022). CephalFormer: Incorporating Global Structure Constraint into Visual Features for General Cephalometric Landmark Detection. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_22
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