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Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model

  • Karen López-LinaresEmail author
  • Maialen Stephens
  • Inmaculada García
  • Iván Macía
  • Miguel Ángel González Ballester
  • Raúl San José Estepar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11794)

Abstract

An abdominal aortic aneurysm (AAA) is a ballooning of the abdominal aorta, that if not treated tends to grow and rupture. Computed Tomography Angiography (CTA) is the main imaging modality for the management of AAAs, and segmenting them is essential for AAA rupture risk and disease progression assessment. Previous works have shown that Convolutional Neural Networks (CNNs) can accurately segment AAAs, but have the limitation of requiring large amounts of annotated data to train the networks. Thus, in this work we propose a methodology to train a CNN only with images generated with a synthetic shape model, and test its generalization and ability to segment AAAs from new original CTA scans. The synthetic images are created from realistic deformations generated by applying principal component analysis to the deformation fields obtained from the registration of few datasets. The results show that the performance of a CNN trained with synthetic data to segment AAAs from new scans is comparable to the one of a network trained with real images. This suggests that the proposed methodology may be applied to generate images and train a CNN to segment other types of aneurysms, reducing the burden of obtaining large annotated image databases.

Keywords

Abdominal aortic aneurysm Segmentation Convolutional Neural Network Synthetic images Principal component analysis 

References

  1. 1.
    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) CrossRefGoogle Scholar
  2. 2.
    López-Linares, K., García, I., García-Familiar, A., Macía, I., González Ballester, M.A.: 3D convolutional neural network for abdominal aortic aneurysm segmentation. arXiv preprint arXiv:1903.00879 (2019)
  3. 3.
    López-Linares, K., et al.: 3D pulmonary artery segmentation from CTA scans using deep learning with realistic data augmentation. In: Image Analysis for Moving Organ, Breast, and Thoracic Images, pp. 225–237 (2018)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Freiman, M., Esses, S.J., Joskowicz, L., Sosna, J.: An iterative model-constrained graph-cut algorithm for abdominal aortic aneurysm thrombus segmentation. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 672–675(2010)Google Scholar
  6. 6.
    Demirci, S., Lejeune, G., Navab, N.: Hybrid deformable model for aneurysm segmentation. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 33–36 (2009)Google Scholar
  7. 7.
    Lalys, F., Yan, V., Kaladji, A., Lucas, A., Esneault, S.: Generic thrombus segmentation from pre and postoperative CTA. Int. J. Comput. Assist. Radiol. Surg. 12(9), 1–10 (2017)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Zohios, C., Kossioris, G., Papaharilaou, Y.: Geometrical methods for level set based abdominal aortic aneurysm thrombus and outer wall 2D image segmentation. Comput. Methods. Program. Biomed. 107(2), 202–217 (2012)CrossRefGoogle Scholar
  10. 10.
    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_49CrossRefGoogle Scholar
  11. 11.
    Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24553-9_68CrossRefGoogle Scholar
  12. 12.
    Kazeminia, S., et al.: GANs for medical image analysis. arXiv preprint arXiv:1809.06222 (2018)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Karen López-Linares
    • 1
    • 2
    • 3
    Email author
  • Maialen Stephens
    • 1
  • Inmaculada García
    • 1
    • 2
  • Iván Macía
    • 1
    • 2
  • Miguel Ángel González Ballester
    • 3
    • 4
  • Raúl San José Estepar
    • 5
  1. 1.Vicomtech FoundationSan SebastiánSpain
  2. 2.Biodonostia Health Research InstituteSan SebastiánSpain
  3. 3.BCN Medtech, Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
  4. 4.ICREABarcelonaSpain
  5. 5.Applied Chest Imaging Laboratory, Brigham and Women’s HospitalHarvard medical schoolBostonUSA

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