Abstract
Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anatomical landmarks in cephalometric X-ray images. We train the deep encoder-decoder model for landmark detection, which combines global landmark configuration with local high-resolution feature responses. The proposed framework is based on a 2-stage u-net, regressing the multi-channel heatmaps for landmark detection. In this framework, we embed attention mechanism with global stage heatmaps, guiding the local stage inferring, to regress the local heatmap patches in a high resolution. Besides, an Expansive Exploration strategy is applied to improve robustness while inferring, expanding the searching scope without increasing model complexity. We have evaluated the proposed framework in the most widely-used public dataset of landmark detection in cephalometric X-ray images. With less computation and manually tuning, the proposed framework achieves state-of-the-art results.
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References
Ching-Wei, W., et al.: Evaluation and comparison of anatomical landmark detection methods for cephalometric X-ray images: a grand challenge. IEEE TMI 34, 1890–1900 (2015)
Wang, C.W., et al.: A benchmark for comparison of dental radiography analysis algorithms. Med. Image Anal. 31, 63 (2016)
Ibragimov, B., et al.: Computerized cephalometry by game theory with shape-and appearance-based landmark refinement. In: ISBI (2015)
Lindner, C., et al.: Fully automatic cephalometric evaluation using random forest regression-voting. In: ISBI (2015)
Lindner, C., et al.: Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms. Sci. Rep. 6, 33581 (2016)
Jiao, Z., et al.: A deep feature based framework for breast masses classification. Neurocomputing 197, 221–231 (2016)
Jiao, Z., et al.: Deep convolutional neural networks for mental load classification based on EEG data. Pattern Recogn. 76, 582–595 (2018)
Hu, Y., et al.: Mammographic mass detection based on saliency with deep features. In: ICIMCS, pp. 292–297. ACM (2016)
Yang, D., et al.: Asymmetry Analysis with sparse autoencoder in mammography. In: ICIMCS, pp. 287–291. ACM (2016)
Lee, H., et al.: Cephalometric landmark detection in dental x-ray images using convolutional neural networks. In: Medical Imaging 2017: Computer-Aided Diagnosis, p. 101341 W. International Society for Optics and Photonics (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Long, J., et al.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
Payer, C., Štern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M., 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
Guan, Q., et al.: Diagnose like a radiologist: attention guided convolutional neural network for thorax disease classification. arXiv preprint arXiv:1801.09927 (2018)
Tuysuzoglu, A., Tan, J., Eissa, K., Kiraly, A.P., Diallo, M., Kamen, A.: Deep adversarial context-aware landmark detection for ultrasound imaging. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 151–158. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_18
Lin, T.-Y., et al.: Focal loss for dense object detection. In: ICCV, pp. 2980–2988 (2017)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 61671339, 61432014 and 61772402, and in part by National High-Level Talents Special Support Program of China under Grant CS31117200001.
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Zhong, Z., Li, J., Zhang, Z., Jiao, Z., Gao, X. (2019). An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_60
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DOI: https://doi.org/10.1007/978-3-030-32226-7_60
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