Abstract
Aortic dissection (AD) is caused by blood flowing into an intimal tear on the innermost layer of the aorta leading to the formation of true lumen and false lumen. For type B aortic Dissection (TBAD), the tear can appear beyond the left subclavian artery or in the aortic arch according to Stanford classification. Quantitative and qualitative analysis of the geometrical and biomedical parameters of TBAD such as maximum transverse diameter of the thoracic aorta, maximum diameter of the true-false lumen and the length of proximal landing zone is crucial for the treatment planning of thoracic endovascular aortic repair (TEVAR), follow-up as well as long-term outcome prediction of TBAD. Its experience-dependent to measure accurately the parameters of TBAD even with the help of computer-aided software. In this paper, we describe our efforts towards the realization of automatic measurement of TBAD parameters with the hope to help surgeons better manage the disease and lighten their burden. In our efforts to achieve this goal, a large standard TBAD database with manual annotation of the entire aorta, true lumen, false lumen and aortic wall is built. A series of deep learning based methods for automatic segmentation of TBAD are developed and evaluated using the database. Finally, automatic measurement techniques are developed based on the output of our automatic segmentation module. Clinical applications of the automatic measurement methods as well as the perspective of deep learning in dealing with TBAD is also discussed.
J. Li and L. Cao—Contributed equally to this work.
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09 April 2019
The original version of the chapter starting on p. 64 was revised. The author names and their affiliations have been changed.
References
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
Khoynezhad, A., Gupta, P.K., Donayre, C.E., White, R.A.: Current status of endovascular management of complicated acute type B aortic dissection. Future Cardiol. 5(6), 581–588 (2009)
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
Krissian, K., Carreira, J.M., Esclarin, J., Maynar, M.: Semi-automatic segmentation and detection of aorta dissection wall in MDCT angiography. Med. Image Anal. 18(1), 83–102 (2014)
Li, J., Cao, L., Ge, Y., Cheng, W., Bowen, M., Wei, G.: Multi-Task Deep Convolutional Neural Network for the Segmentation of Type B Aortic Dissection. arXiv preprint arXiv:1806.09860 (2018)
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging (2018)
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)
Noothout, J.M., de Vos, B.D., Wolterink, J.M., Išgum, I.: Automatic segmentation of thoracic aorta segments in low-dose chest CT. In: Medical Imaging 2018: Image Processing. International Society for Optics and Photonics (2018)
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
Xie, Y., Padgett, J., Biancardi, A.M., Reeves, A.P.: Automated aorta segmentation in low-dose chest CT images. Int. J. Comput. Assist. Radiol. Surg. 9(2), 211–219 (2014)
Yang, X., et al.: Hybrid loss guided convolutional networks for whole heart parsing. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 215–223. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_23
Zeng, G., Yang, X., Li, J., Yu, L., Heng, P.-A., Zheng, G.: 3D U-net with multi-level deep supervision: fully automatic segmentation of proximal femur in 3D MR images. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 274–282. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_32
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Li, J., Cao, L., Cheng, W., Bowen, M., Guo, W. (2018). Towards Automatic Measurement of Type B Aortic Dissection Parameters: Methods, Applications and Perspective. In: Stoyanov, D., et al. Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS CVII STENT 2018 2018 2018. Lecture Notes in Computer Science(), vol 11043. Springer, Cham. https://doi.org/10.1007/978-3-030-01364-6_8
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