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Erschienen in:
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2023 | OriginalPaper | Buchkapitel

A Self-supervised 3D/2D Registration Method for Incomplete DSA Vessels

verfasst von : Yizhou Xu, Cai Meng, Yanggang Li, Ning Li, Longfei Ren, Kun Xia

Erschienen in: Biomedical and Computational Biology

Verlag: Springer International Publishing

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Abstract

For vascular interventional surgery, the preoperative 3D computed tomography (CT) has complete information of vessels but is not convenient for obvervation, while the intraoperative 2D digital subtraction angiography (DSA) is easy for doctors to monitor the vascular conditions real-timely but information is incomplete in each frame. As a result, 2D/3D registration, which is the technology to fuse information from images of different modal, is useful for the guidance of vascular interventional surgery. In this paper, we proposed a self-supervised 2D/3D vascular registration method to improve the performance on DSAs with incomplete vessels. The proposed method contains a rigid and an elastic registration stage, for regressing the 6-dim parameters to obtain a center image and fine-tuning respectively. In addition, a patch-based content loss is introduced to the rigid registration step to give an appropriate similarity measure for images with incomplete vessels, and a masked elastic module is introduced to simulate the incompletion and deformation caused by breath or heart beats on the real vessels in elastic registration. We evaluated our method on both simulated and real images. Experiments prove that our proposed method is effective to register CT and DSA images.

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Vorheriges Kapitel The Charge Transfer Network Model for Arbitrary Proteins Complexes
Nächstes Kapitel The Potential Role of RNA “Writer” TRMT61B in the Immune Regulation of Breast Cancer
Literatur
1.
Markelj, P., Tomaževič, D., Likar, B., Pernuš, F.: A review of 3D/2D registration methods for image-guided interventions. Med. Image Anal. 16(3), 642–661 (2012)CrossRefPubMed
2.
Hipwell, J.H., et al.: Intensity-based 2-D-3-D registration of cerebral angiograms (2003)
3.
Demirci, S., Kutter, O., Manstad-Hulaas, F., Bauernschmitt, R., Navab, N.: Advanced 2D–3D registration for endovascular aortic interventions: addressing dissimilarity in images. In: Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling, vol. 6918, p. 69182S. International Society for Optics and Photonics (2008)
4.
Demirci, S., Baust, M., Kutter, O., Manstad-Hulaas, F., Eckstein, H.H., Navab, N.: Disocclusion-based 2D–3D registration for aortic interventions. Comput. Biol. Med. 43(4), 312–322 (2013)CrossRefPubMed
5.
Miao, S., Liao, R., Zheng, Y.: A hybrid method for 2-D/3-D registration between 3-D volumes and 2-D angiography for trans-catheter aortic valve implantation (TAVI). In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1215–1218. IEEE (2011)
6.
Raheem, A., Carrell, T., Modarai, B., Penney, G.: Non-rigid 2D–3D image registration for use in endovascular repair of abdominal aortic aneurysms. In: Medical Image Understanding and Analysis, pp. 153–157 (2010)
7.
Hunsche, S., et al.: Intensity-based 2D 3D registration for lead localization in robot guided deep brain stimulation. Phys. Med. Biol. 62(6), 2417 (2017)CrossRefPubMed
8.
Meng, C., Wang, Q., Guan, S., Sun, K., Liu, B.: 2D–3D registration with weighted local mutual information in vascular interventions. IEEE Access 7, 162629–162638 (2019)CrossRef
9.
Yang, K., et al.: A novel 2D/3D hierarchical registration framework via principal-directional Fourier transform operator. Phys. Med. Biol. 66(6), 065030 (2021)CrossRefPubMed
10.
Groher, M., Zikic, D., Navab, N.: Deformable 2D–3D registration of vascular structures in a one view scenario. IEEE Trans. Med. Imaging 28(6), 847–860 (2009)CrossRefPubMed
11.
Rivest-Henault, D., Sundar, H., Cheriet, M.: Nonrigid 2D/3D registration of coronary artery models with live fluoroscopy for guidance of cardiac interventions. IEEE Trans. Med. Imaging 31(8), 1557–1572 (2012)CrossRefPubMed
12.
Xu, D., et al.: Single-view 2D/3D registation for X-ray guided bronchoscopy. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 233–236. IEEE (2010)
13.
Ghafurian, S., Hacihaliloglu, I., Metaxas, D.N., Tan, V., Li, K.: 3D/2D image registration using weighted histogram of gradient directions. In: Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 9415, p. 94151Z. International Society for Optics and Photonics (2015)
14.
Zhu, J., et al.: Heuristic tree searching for pose-independent 3D/2D rigid registration of vessel structures. Phys. Med. Biol. 65(5), 055010 (2020)CrossRefPubMed
15.
Yoon, S., Yoon, C.H., Lee, D.: Topological recovery for non-rigid 2D/3D registration of coronary artery models. Comput. Methods Programs Biomed. 200, 105922 (2021)CrossRefPubMed
16.
Chou, C.R., Frederick, B., Mageras, G., Chang, S., Pizer, S.: 2D/3D image registration using regression learning. Comput. Vis. Image Underst. 117(9), 1095–1106 (2013)CrossRefPubMedPubMedCentral
17.
Gouveia, A.R., Metz, C., Freire, L., Almeida, P., Klein, S.: Registration-by-regression of coronary CTA and X-ray angiography. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 5(3), 208–220 (2017)CrossRef
18.
Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D/3D registration. IEEE Trans. Med. Imaging 35(5), 1352–1363 (2016)CrossRef
19.
Miao, S., Wang, Z.J., Zheng, Y., Liao, R.: Real-time 2D/3D registration via CNN regression. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1430–1434. IEEE (2016)
20.
Zheng, J., Miao, S., Wang, Z.J., Liao, R.: Pairwise domain adaptation module for CNN-based 2-D/3-D registration. J. Med. Imaging 5(2), 021204 (2018)CrossRef
21.
Guan, S., Meng, C., Xie, Y., Wang, Q., Sun, K., Wang, T.: Deformable cardiovascular image registration via multi-channel convolutional neural network. IEEE Access 7, 17524–17534 (2019)CrossRef
22.
Miao, S., et al.: Dilated FCN for multi-agent 2D/3D medical image registration. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
23.
Toth, D.D., et al.: 3D/2D model-to-image registration by imitation learning for cardiac procedures. Int. J. Comput. Assist. Radiol. Surg. 13(8), 1141–1149 (2018)CrossRefPubMedPubMedCentral
24.
25.
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9252–9260 (2018)
26.
27.
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. arXiv preprint arXiv:​1506.​02025 (2015)
28.
Hu, Y., et al.: Label-driven weakly-supervised learning for multimodal deformable image registration. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1070–1074. IEEE (2018)
29.
Zeng, Q., et al.: Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy. Phys. Med. Biol. 65(13), 135002 (2020)CrossRefPubMedPubMedCentral
30.
Li, P., Pei, Y., Guo, Y., Ma, G., Xu, T., Zha, H.: Non-rigid 2D–3D registration using convolutional autoencoders. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 700–704. IEEE (2020)
31.
Zhang, Y.: An unsupervised 2D–3D deformable registration network (2D3D-RegNet) for cone-beam CT estimation. Phys. Med. Biol. 66(7), 074001 (2021)CrossRef
32.
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)
33.
Levoy, M.: Display of surfaces from volume data. IEEE Comput. Graphics Appl. 8(3), 29–37 (1988)CrossRef
34.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:​1810.​04805 (2018)
35.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. arXiv preprint arXiv:​2111.​06377 (2021)
36.
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256. JMLR Workshop and Conference Proceedings (2010)
37.
Van de Kraats, E.B., Penney, G.P., Tomazevic, D., Van Walsum, T., Niessen, W.J.: Standardized evaluation methodology for 2-D-3-D registration. IEEE Trans. Med. Imaging 24(9), 1177–1189 (2005)CrossRefPubMed
Metadaten
Titel
A Self-supervised 3D/2D Registration Method for Incomplete DSA Vessels
verfasst von
Yizhou Xu
Cai Meng
Yanggang Li
Ning Li
Longfei Ren
Kun Xia
Copyright-Jahr
2023
Buch
Biomedical and Computational Biology

Print ISBN: 978-3-031-25190-0

Electronic ISBN: 978-3-031-25191-7

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-25191-7_2

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