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2020 | OriginalPaper | Chapter

Abdominal Aortic Aneurysm Segmentation from Contrast-Enhanced Computed Tomography Angiography Using Deep Convolutional Networks

Authors : Tomasz Dziubich, Paweł Białas, Łukasz Znaniecki, Joanna Halman, Jakub Brzeziński

Published in: ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

Publisher: Springer International Publishing

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Abstract

One of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying U-Net, ResNet, and VBNet frameworks. Our results show that we are able to outperform state-of-the-art methods by 3% on the Dice metric without any additional post-processing steps.

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Bai, W., et al.: Recurrent neural networks for aortic image sequence segmentation with sparse annotations. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 586–594. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_67CrossRef

Claridge, R., Arnold, S., Morrison, N., van Rij, A.M.: Measuring abdominalaortic diameters in routine abdominal computed tomography scans and implications for abdominal aortic aneurysm screening. J. Vasc. Surg. 65(6), 1637–1642 (2017). https://doi.org/10.1016/j.jvs.2016.11.044CrossRef

Duquette, A.A., Jodoin, P.M., Bouchot, O., Lalande, A.: 3D segmentation of abdominal aorta from CT-scan and MR images. Comput. Med. Imaging Graph. 36(4), 294–303 (2012). https://doi.org/10.1016/j.compmedimag.2011.12.001. http://www.sciencedirect.com/science/article/pii/S0895611111001480CrossRef

Hahn, S., Perry, M., Morris, C.S., Wshah, S., Bertges, D.J.: Machine deep learning accurately detects endoleak after endovascular abdominal aortic aneurysm repair. Vasc. Sci. JVS 1, 5–12 (2020)CrossRef

Jaeger, P.F., et al.: Retina U-NET: embarrassingly simple exploitation of segmentation supervision for medical object detection. arXiv preprint arXiv:1811.08661 (2018)

Joldes, G.R., Miller, K., Wittek, A., Forsythe, R.O., Newby, D.E., Doyle, B.J.: BioPARR: a software system for estimating the rupture potential index for abdominal aortic aneurysms. Sci. Rep. 7(1), 1–15 (2017)CrossRef

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Lareyre, F., Adam, C., Carrier, M., Dommerc, C., Mialhe, C., Raffort, J.: A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation. Sci. Rep. 9(1), 13750 (2019). https://doi.org/10.1038/s41598-019-50251-8CrossRef

Lu, J.-T., et al.: DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 723–731. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_80CrossRef

10.

López-Linares, K., et al.: Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks. Med. Image Anal. 46, 202–214 (2018). https://doi.org/10.1016/j.media.2018.03.010. http://www.sciencedirect.com/science/article/pii/S1361841518301117CrossRef

11.

Siriapisith, T., Kusakunniran, W., Haddawy, P.: Outer wall segmentation of abdominal aortic aneurysm by variable neighborhood search through intensity and gradient spaces. J. Digital Imaging 31(4), 490–504 (2018). https://doi.org/10.1007/s10278-018-0049-zCrossRef

12.

Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 29 (2015). https://doi.org/10.1186/s12880-015-0068-xCrossRef

13.

Xie, Q., Hovy, E., Luong, M.T., Le, Q.V.: Self-training with noisy student improves ImageNet classification. arXiv preprint arXiv:1911.04252 (2019)

14.

Zhuge, F., Rubin, G.D., Sun, S., Napel, S.: An abdominal aortic aneurysm segmentation method level: set with region and statistical information. Med. Phys. 33(5), 1440–1453 (2006). https://doi.org/10.1118/1.2193247. https://aapm.onlinelibrary.wiley.com/doi/abs/10.1118/1.2193247CrossRef

Title: Abdominal Aortic Aneurysm Segmentation from Contrast-Enhanced Computed Tomography Angiography Using Deep Convolutional Networks
Authors: Tomasz Dziubich
Paweł Białas
Łukasz Znaniecki
Joanna Halman
Jakub Brzeziński
Publisher: Springer International Publishing
Book: ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium
Print ISBN: 978-3-030-55813-0

Electronic ISBN: 978-3-030-55814-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-55814-7_13

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