Abstract
Landmark localization is an important step in quantifying craniomaxillofacial (CMF) deformities and designing treatment plans of reconstructive surgery. However, due to the severity of deformities and defects (partially missing anatomy), it is difficult to automatically and accurately localize a large set of landmarks simultaneously. In this work, we propose two cascaded networks for digitizing 60 anatomical CMF landmarks in cone-beam computed tomography (CBCT) images. The first network is a U-Net that outputs heatmaps for landmark locations and landmark features extracted with a local attention mechanism. The second network is a graph convolution network that takes the features extracted by the first network as input and determines whether each landmark exists via binary classification. We evaluated our approach on 50 sets of CBCT scans of patients with CMF deformities and compared them with state-of-the-art methods. The results indicate that our approach can achieve an average detection error of 1.47 mm with a false positive rate of 19%, outperforming related methods.
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References
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lang, Y., et al.: Automatic detection of craniomaxillofacial anatomical landmarks on CBCT images using 3D mask R-CNN. In: Zhang, D., Zhou, L., Jie, B., Liu, M. (eds.) GLMI 2019. LNCS, vol. 11849, pp. 130–137. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35817-4_16
Lian, C., Liu, M., Zhang, J., Shen, D.: Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Trans. Pattern Anal. Mach. Intell. (2018)
Lian, C., Zhang, J., Liu, M., Zong, X., Hung, S.C., Lin, W., Shen, D.: Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7t MR images. Med. Image Anal. 46, 106–117 (2018)
Payer, C., Štern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230–238. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_27
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Wang, X., Yang, X., Dou, H., Li, S., Heng, P.A., Ni, D.: Joint segmentation and landmark localization of fetal femur in ultrasound volumes. In: 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 1–5. IEEE (2019)
Wu, Y., He, K.: Group normalization. In: Proceedings of The European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Xia, J.J., Gateno, J., Teichgraeber, J.F.: New clinical protocol to evaluate craniomaxillofacial deformity and plan surgical correction. J. Oral Maxillofac. Surg. 67(10), 2093–2106 (2009)
Yuan, P., et al.: Design, development and clinical validation of computer-aided surgical simulation system for streamlined orthognathic surgical planning. Int. J. Comput. Assist. Radiol. Surg. 12(12), 2129–2143 (2017). https://doi.org/10.1007/s11548-017-1585-6
Zhang, D., Wang, J., Noble, J.H., Dawant, B.M.: Headlocnet: deep convolutional neural networks for accurate classification and multi-landmark localization of head CTS. Med. Image Anal. 61, 101659 (2020)
Zhang, J., et al.: Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 720–728. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_81
Zhang, L., Singh, V., Qi, G.J., Chen, T.: Cascade attention machine for occluded landmark detection in 2D X-Ray angiography. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 91–100. IEEE (2019)
Zhu, M., Shi, D., Zheng, M., Sadiq, M.: Robust facial landmark detection via occlusion-adaptive deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3486–3496 (2019)
Acknowledgment
This work was supported in part by NIH grants (R01 DE022676, R01 DE027251 and R01 DE021863).
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Lang, Y. et al. (2020). Automatic Localization of Landmarks in Craniomaxillofacial CBCT Images Using a Local Attention-Based Graph Convolution Network. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12264. Springer, Cham. https://doi.org/10.1007/978-3-030-59719-1_79
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DOI: https://doi.org/10.1007/978-3-030-59719-1_79
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