Oral Cone Beam Computed Tomography Images Segmentation Based On Multi-view Fusion
Pages 59 - 64
Abstract
In computer-aided orthodontic treatment, it is necessary to establish a 3D model of the teeth and alveolar bone. Segmenting the teeth and alveolar bone from cone-beam computed tomography (CBCT) images is the primary step of reconstructing the model. However, previous studies mainly focused on the segmentation and reconstruction of teeth, and the research on alveolar bone segmentation is very rare. Different viewpoints of oral CBCT images provide complementary information. This paper proposes a multi-view information fusion method to realize the segmentation of teeth and alveolar bone in oral CBCT images. Firstly, for each of the three axes of the 3D data, an individual 2D deep network is trained to achieve coarse segmentation results. Secondly, in order to make full use of the information obtained from different viewpoints of oral CBCT images, a convolution network is used for multi-view fusion. To better fuse the information of different viewpoints, the channel-spatial attention mechanism is applied. Final segmentation is obtained by stacking 2D slices predictions, and the contextual information is added in a post-processing manner through fusing three-viewpoints results. According to the experimental results, our proposed method achieves significant improvements over the baseline.
References
[1]
H. Akhoondali, R. Zoroofi, and G. Shirani. 2009. Rapid automatic segmentation and visualization of teeth in ct-scan data. Journal of Applied Sciences, 9(11): 2031–2044
[2]
S. Kakehbaraei, H. Seyedarabi, and A. T. Zenouz. 2018. Dental segmentation in cone-beam computed tomography images using watershed and morphology operators. Journal of medical signals and sensors, 8(2): 119
[3]
Y. Fan, R. Beare, H. Matthews, P. Schneider, N. Kilpatrick, J. Clement, P. Claes, A. Penington, and C. Adamson. 2019. Marker-based watershed transform method for fully automatic mandibular segmentation from cbct images. Dentomaxillofacial Radiology, 48(2): 20180261
[4]
H. Gao and O. Chae. 2010. Individual tooth segmentation from ct images using level set method with shape and intensity prior. Pattern Recognition, 43(7): 2406–2417
[5]
Y. Gan, Z. Xia, J. Xiong, G. Li, and Q. Zhao. 2017. Tooth and alveolar bone segmentation from dental computed tomography images. IEEE journal of biomedical and health informatics, 22(1): 196–204
[6]
Y. Wang, S. Liu, G. Wang, and Y. Liu. 2018. Accurate tooth segmentation with improved hybrid active contour model. Physics in Medicine & Biology, 64(1): 015012
[7]
Y. Pei, X. Ai, H. Zha, T. Xu, and G. Ma. 2016. 3d exemplar-based random walks for tooth segmentation from cone-beam computed tomography images. Medical physics, 43(9): 5040–5050
[8]
S. Lee, S. Woo, J. Y u, J. Seo, J. Lee, and C. Lee. 2020. Automated cnn-based tooth segmentation in cone-beam ct for dental implant planning. IEEE Access, 8: 50 507–50 518
[9]
Y. Rao, Y. Wang, F. Meng, J. Pu, J. Sun, and Q. Wang. 2020. A symmetric fully convolutional residual network with dcrf for accurate tooth segmentation. IEEE Access, 8: 92 028–92 038
[10]
Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651
[11]
Y. Chen, H. Du, Z. Y un, S. Yang, Z. Dai, L. Zhong, Q. Feng, and W. Yang. 2020. Automatic segmentation of individual tooth in dental cbct images from tooth surface map by a multi-task fcn. IEEE Access
[12]
Z. Cui, C. Li, and W. Wang. 2019. Toothnet: automatic tooth instance segmentation and identification from cone beam ct images// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE: 6368–6377
[13]
Noori M, Bahri A and Mohammadi K. 2019. Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation//Proceedings of 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE). Mashhad, Iran: IEEE: 269-275
[14]
Nima Tajbakhsh, Jae Y Shin, Suryakanth R Gurudu, R Todd Hurst, Christopher B Kendall, Michael B Gotway, and Jianming Liang. Convolutional neural networks for medical image analysis: Full training or fine tuning? TMI, 35(5):1299–1312, 2016. 3
[15]
Luo Kaikai, Wang Ting, Ye Fangfang. 2021. Brain tumor MR image segmentation based on attention mechanism and multi-view fusion U-Net. Journal of Image and Graphics
[16]
Q Yu, Y Xia, L Xie, EK Fishman, AL Yuille. 2019. Thickened 2D Networks for Efficient 3D Medical Image Segmentation [EB/OL], [2019-11-23]. https://arxiv.org/abs/1904.01150v2
[17]
Hu J, Shen L and Sun G. 2018. Squeeze-and excitation networks//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. SLC, Utah, USA: IEEE: 7132-7141
[18]
Fang L, Wan Z, Zeng W, 2013. Research on internationalization of 3d slicer[C/OL]//2013 International Conference on Computer Sciences and Applications: 469-473
[19]
Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of International Conference on Medical image computing and computer-assisted intervention. Switzerland: Springer: 234-241
[20]
Badrinarayanan V, Kendall A and Cipolla R. 2017. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-1
[21]
Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2017. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4): 834-848
[22]
Çiçek Ö, Abdulkadir A, Lienkamp S S, Brox T and Ronneberger O. 2016.3D U-Net: learning dense volumetric segmentation from sparse annotation//Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention. Athens: Springer: 424-432
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Published In
February 2022
137 pages
ISBN:9781450396172
DOI:10.1145/3524889
Copyright © 2022 ACM.
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Published: 02 May 2022
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ICIIT 2022
ICIIT 2022: 2022 7th International Conference on Intelligent Information Technology
February 25 - 27, 2022
Foshan, China
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