Browse > Article
http://dx.doi.org/10.15701/kcgs.2022.28.3.55

Improved Anatomical Landmark Detection Using Attention Modules and Geometric Data Augmentation in X-ray Images  

Lee, Hyo-Jeong (Department of Computational Medicine, Graduate Program in System Health Science and Engineering)
Ma, Se-Rie (Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering Ewha Womans University)
Choi, Jang-Hwan (Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering Ewha Womans University)
Publication Information
Journal of the Korea Computer Graphics Society / v.28, no.3, 2022 , pp. 55-65 More about this Journal
Abstract
Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.
Keywords
Landmark Detection; Medical Image; Deep Learning; Data Augmentation; Attention;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 I. El-Fegh, M. Galhood, M. Sid-Ahmed, and M. Ahmadi, "Automated 2-d cephalometric analysis of x-ray by image registration approach based on least square approximator," in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 3949-3952.
2 R. D. J.H. Hwang, M.G. Kim, "Evaluation of automated cephalometric analysis based on the latest deep learning method," The Angle Orthodontist, pp. 329-335, 2021.
3 Daseong and Han, "A supervised learning framework for physics-based controllers using stochastic model predictive control," Journal of the Korea Computer Graphics Society, pp. 9-17, 2021.
4 Jewon, T. Ahn, T. Gu, and Kwon., "Motion generation of a single rigid body character using deep reinforcement learning," Journal of the Korea Computer Graphics Society, pp. 13-23, 2021.
5 Z. Feng, J. Kittler, M. Awais, P. Huber, and X. Wu, "Wing loss for robust facial landmark localisation with convolutional neural networks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2235-2245, 2018.
6 W. P. Segars, G. Sturgeon, S. Mendonca, J. Grimes, and B. M. Tsui, "4d xcat phantom for multimodality imaging research," Medical physics, pp. 4902-4915, 2010.   DOI
7 A. Kaur and C. Singh, "Automatic cephalometric landmark detection using zernike moments and template matching," Signal, Image and Video Processing, pp. 117-132, 2015.
8 W. Li, Y. Lu, K. Zheng, H. Liao, C. Lin, J. Luo, C.-T. Cheng, J. Xiao, L. Lu, C.-F. Kuo, et al., "Structured landmark detection via topology-adapting deep graph learning," in European Conference on Computer Vision, 2020, pp. 266-283.
9 H. Lee, M. Park, and J. Kim, "Cephalometric landmark detection in dental x-ray images using convolutional neural networks," in Medical imaging 2017: Computer-aided diagnosis, 2017, p. 101341W.
10 B. Bier, M. Unberath, J.-N. Zaech, J. Fotouhi, M. Armand, G. Osgood, N. Navab, and A. Maier, "X-ray-transform invariant anatomical landmark detection for pelvic trauma surgery," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018, pp. 55-63.
11 D. S. W.R. Proffit, H.W. Fields, "Contemporary orthodontics," Elsevier Health Sciences, p. 8, 2006.
12 C.-W. Wang, C.-T. Huang, M.-C. Hsieh, et al., "Evaluation and comparison of anatomical landmark detection methods for cephalometric x-ray images: a grand challenge," IEEE transactions on medical imaging, pp. 1890-1900, 2015.
13 K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
14 J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132-7141.
15 S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, "Cbam: Convolutional block attention module," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3-19.
16 Jie, W. Yao, T. Zeng, S. He, Y. Zhou, J. Zhang, W. Guo, and Tang, "Automatic localization of cephalometric landmarks based on convolutional neural network," American Journal of Orthodontics and Dentofacial Orthopedics, pp. e250-e259,, 2022.
17 Y. Song, X. Qiao, Y. Iwamoto, and Y.-w. Chen, "Automatic cephalometric landmark detection on x-ray images using a deep-learning method," Applied Sciences, p. 2547, 2020.
18 Q. Liu, J. Deng, J. Yang, G. Liu, and D. Tao, "Adaptive cascade regression model for robust face alignment," IEEE Transactions on Image Processing, pp. 797-807, 2016.
19 S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, "Convolutional pose machines," in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2016, pp. 4724-4732.
20 J. Wang, K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, D. Liu, Y. Mu, M. Tan, X. Wang, et al., "Deep high-resolution representation learning for visual recognition," IEEE transactions on pattern analysis and machine intelligence, pp. 3349-3364, 2020.
21 A. Maier, H. G. Hofmann, M. Berger, P. Fischer, C. Schwemmer, H. Wu, K. Muller, J. Hornegger, J.-H. Choi, C. Riess, et al., "Conrad-a software framework for cone-beam imaging in radiology," Medical physics, p. 111914, 2013.
22 P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, and J. Shlens, "Stand-alone self-attention in vision models," Advances in Neural Information Processing Systems, vol. 32, 2019.
23 M. Lee, M. Chung, and Y.-G. Shin, "Cephalometric landmark detection via global and local encoders and patch-wise attentions," Neurocomputing, pp. 182-189, 2022.