Abstract
Aortic dissection (AD) is a dangerous disease usually diagnosed by computed tomography angiography. Segmentation of true and false lumens of aortic trunk and major branches is very important for the diagnosis and treatment of this disease. In this paper, we proposed a fully automatic vessel analysis algorithm for dissected aorta, which can output centerlines, true lumen, and false lumen of trunk and major branches, and perfusion source of branches. In our experiment, the mean dice similarity coefficient (DSC) of true lumen segmentation was 0.939 for trunk and 0.912 for branch while the mean DSC of whole lumen segmentation was 0.974 for trunk and 0.937 for branch, and the classification accuracy of branch perfusion source was 0.863.
This work was supported in part by the National Natural Science Foundation of China under Grants 61976121 and 82071921.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pepe, A., et al.: Detection, segmentation, simulation and visualization of aortic dissections: a review. Med. Image Anal. 65, 101773 (2020)
Chiesa, R., Melissano, G., Zangrillo, A., Coselli, J.S.: Thoraco-Abdominal Aorta: Surgical and Anesthetic Management, vol. 783. Springer, Milano (2011). https://doi.org/10.1007/978-88-470-1857-0
Kovács, T., Cattin, P., Alkadhi, H., Wildermuth, S., Székely, G.: Automatic segmentation of the aortic dissection membrane from 3D CTA images. In: Yang, G.-Z., Jiang, T.Z., Shen, D., Gu, L., Yang, J. (eds.) MIAR 2006. LNCS, vol. 4091, pp. 317–324. Springer, Heidelberg (2006). https://doi.org/10.1007/11812715_40
Lee, N., Tek, H., Laine, A.F.: True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching. In: Medical Imaging 2008: Computer-Aided Diagnosis, vol. 6915, p. 69152V. International Society for Optics and Photonics (2008)
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
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Li, Z., et al.: Lumen segmentation of aortic dissection with cascaded convolutional network. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 122–130. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_14
Hahn, L.D., et al.: CT-based true-and false-lumen segmentation in type B aortic dissection using machine learning. Radiol. Cardiothorac. Imaging 2(3), e190179 (2020)
Cao, L., et al.: Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning. Eur. J. Radiol. 121, 108713 (2019)
Fantazzini, A., et al.: 3D automatic segmentation of aortic computed tomography angiography combining multi-view 2D convolutional neural networks. Cardiovasc. Eng. Technol. 11(5), 576–586 (2020)
Xu, X., He, Z., Niu, K., Zhang, Y., Tang, H., Tan, L.: An automatic detection scheme of acute stanford type A aortic dissection based on DCNNs in CTA images. In: Proceedings of the 2019 4th International Conference on Multimedia Systems and Signal Processing, pp. 16–20 (2019)
Tan, Z., Duan, Y., Wu, Z., Feng, J., Zhou, J.: A cascade regression model for anatomical landmark detection. In: STACOM 2019. LNCS, vol. 12009, pp. 43–51. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39074-7_5
Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Fang, H. et al. (2022). Vessel Extraction and Analysis of Aortic Dissection. In: Puyol Antón, E., et al. Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science(), vol 13131. Springer, Cham. https://doi.org/10.1007/978-3-030-93722-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-93722-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93721-8
Online ISBN: 978-3-030-93722-5
eBook Packages: Computer ScienceComputer Science (R0)
