Skip to main content
SpringerLink
Log in

Segmentation of Abdominal Aortic Aneurysm (AAA) Based on Topology Prior Model

  • Conference paper
  • First Online:
Medical Image Understanding and Analysis (MIUA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 723))

Included in the following conference series:

Abstract

In this paper, we propose a statistical based method using a topology prior model, integrating both intensity and shape information, to segment abdominal aortic aneurysm (AAA) from computed tomography angiography (CTA) scans. The method was tested on a total of 48 slices taken from 6 different patients and has shown competitive performance compared with the best reported results in the literature. Our method has achieved a mean Dice coefficient of 0.9303±0.0499, and mean Hausdorff distance of 3.5703±3.1941 mm. This method overcomes the major problem faced by currently existing solutions of similar Hounsfield values of neighboring tissues to that of the AAA thrombus. This is a promising medical tool which can be used to analyze the AAA in order to generate an accurate rupture risk indicator.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Moll, F., et al.: Management of abdominal aortic aneurysms clinical practice guidelines of the european society for vascular surgery. Eur. J. Vasc. Endovasc. Surg. 41(1), 1–58 (2011)

    Article  Google Scholar 

  2. Ernst, C.B.: Abdominal aortic aneurysms. N. Engl. J. Med. 328(16), 1167–1172 (1993)

    Article  Google Scholar 

  3. Vardulaki, K.A., et al.: Growth rates and risk of rupture of abdominal aortic aneurysms. Br. J. Surg. 85(12), 1674–1680 (1998)

    Article  Google Scholar 

  4. Zarins, C.K., et al.: AneuRx stent graft versus open surgical repair of abdominal aortic aneurysms: multicenter prospective clinical trial. J. Vasc. Surg. 29(2), 292–305 (1999)

    Article  Google Scholar 

  5. Darling, R.C., Messina, C.R., Brewster, D.C., Ottinger, L.W.: Autopsy study of unoperated abdominal aortic aneurysms. The case for early resection. Circulation 56(3), 161–164 (1977)

    Google Scholar 

  6. Conway, K.P., Byrne, J., Townsend, M., Lane, I.F.: Prognosis of patients turned down for conventional abdominal aortic aneurysm repair in the endovascular and sonographic era: Szilagyi revisited? J. Vasc. Surg. 33(4), 752–757 (2001)

    Article  Google Scholar 

  7. Georgakarakos, E., et al.: The role of geometric parameters in the prediction of abdominal aortic aneurysm wall stress. Eur. J. Vasc. Endovasc. Surg. 39(1), 42–48 (2010)

    Article  Google Scholar 

  8. Venkatasubramaniam, A.K., et al.: A comparative study of aortic wall stress using finite element analysis for ruptured and non-ruptured abdominal aortic aneurysms. Eur. J. Vasc. Endovasc. Surg. 28(2), 168–176 (2004)

    Google Scholar 

  9. O’Leary, S.A., et al.: Determining the influence of calcification on the failure properties of abdominal aortic aneurysm (AAA) tissue. J. Mech. Behav. Biomed. Mater. 42, 154–167 (2015)

    Article  Google Scholar 

  10. Vorp, D.A., Raghavan, M.L., Webster, M.W.: Mechanical wall stress in abdominal aortic aneurysm: influence of diameter and asymmetry. J. Vasc. Surg. 27(4), 632–639 (1998)

    Article  Google Scholar 

  11. Demirci, S., Lejeune, G., Navab, N.: Hybrid deformable model for aneurysm segmentation. Boston, 28 June–1 July 2009

    Google Scholar 

  12. Das, B., Mallya, Y., Srikanth, S., Malladi, R.: Aortic thrombus segmentation using narrow band active contour model. New York, August–3 September 2006

    Google Scholar 

  13. Zohiosa, C., Kossiorisa, G., Papaharilaou, Y.: Geometrical methods for level set based abdominal aortic aneurysm thrombus and outer wall 2D image segmentation. Comput. Methods Programs Biomed. 107(2), 202–217 (2012)

    Article  Google Scholar 

  14. Subašić, M., Lončarića, S., Sorantin, E.: Model-based quantitative AAA image analysis using a priori knowledge. Comput. Methods Programs Biomed. 80(2), 103–114 (2005)

    Article  Google Scholar 

  15. Loncaric, S., Subasic, M., Sorantin, E.: 3-D deformable model for aortic aneurysm segmentation from CT images, Chicago, IL, 23–28 July 2000

    Google Scholar 

  16. Loncaric, S., Subasic, M., Sorantin, E.: 3-D deformable model for abdominal, 23–28 July 2000

    Google Scholar 

  17. Subasic, M., Loncaric, S., Sorantin, E.: 3-D Image Analysis of Abdominal Aortic Aneurysm. San Diego, CA (2001)

    Google Scholar 

  18. Subasic, M., Loncaric, S., Sorantin, E.: Region-based deformable model for aortic wall segmentation, Rome, 18–20 September 2003

    Google Scholar 

  19. Magee, D., Bulpitt, A., Berry, E.: Level set methods for the 3D segmentation of CT images of abdominal aortic aneurysms, pp. 141–144 (2001)

    Google Scholar 

  20. Bulpitt, A.J., Berry, E.: Spiral CT of abdominal aortic aneurysms: comparison of segmentation with an automatic 3D deformable model and interactive segmentation, San Diego (1998)

    Google Scholar 

  21. Magee, D., Bulpitt, A., Berry, E.: Combining 3D deformable models and level set methods for the segmentation of abdominal aortic aneurysms, Manchester (2001)

    Google Scholar 

  22. Lee, K., et al.: Three-dimensional thrombus segmentation in abdominal aortic aneurysms using graph search based on a triangular mesh. Comput. Biol. Med. 40(3), 271–278 (2010)

    Article  Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. Hraiech, N., Carroll, M., Rochette, M., Coatrieux, J.L.: 3D vascular shape segmentation for fluid-structure modeling, Lyon, 13–15 June 2007

    Google Scholar 

  25. Freiman, M., Esse, S.J., Joskowicz, L., Sosna, J.: An iterative model-constrained graph-cut algorithm for abdominal aortic aneurysm thrombus segmentation. Rotterdam, 14–17 April 2010

    Google Scholar 

  26. Pham, T.D., Golledge, J.: Geostatistically constrained fuzzy segmentation of abdominal aortic aneurysm CT Images, Hong Kong, 1–6 June 2008

    Google Scholar 

  27. Majd, E.M., Sheikh, U.U., Abu-Bakar, S.A.R.: Automatic segmentation of abdominal aortic aneurysm in computed tomography images, Kuala Lumpur, 15–18 December 2010

    Google Scholar 

  28. Dehmeshki, J., et al.: Computer aided detection and measurement of abdominal aortic aneurysm using computed tomography digital images, Cancun, February 2009

    Google Scholar 

  29. Biasi, H.D., Wangenheim, A.V., Silveira, P.G., Comunello, E.: 3D reconstruction of abdominal aortic aneurysms, Maribor (2002)

    Google Scholar 

  30. Macía, I., Legarreta, J.H., Paloc, C., Graña, M., Maiora, J., García, G., Blas, M.: Segmentation of abdominal aortic aneurysms in CT images using a radial model approach. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 664–671. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04394-9_81

    Chapter  Google Scholar 

  31. Bodur, O., et al.: Semi-automatic aortic aneurysm analysis, San Jose (2007)

    Google Scholar 

  32. Hosseini, B., et al.: Automatic segmentation of abdominal aortic aneurysm using logical algorithm. Pisa, 17–19 November 2010

    Google Scholar 

  33. Maiora, J., Ayerdi, B., Graña, M.: Random forest active learning for AAA thrombus segmentation in computed tomography angiography images. Neurocomputing 126, 71–77 (2014)

    Article  Google Scholar 

  34. Maiora, J., Graña, M.: Abdominal CTA image analysis through active learning and decision random forests: application to AAA segmentation, Brisbane, 10–15 June 2012

    Google Scholar 

Download references

Acknowledgements

This research was funded with a generous grant from Al-Jalila Foundation, Grant no. AJF201551. Ethics approval from University of Limerick, Ireland was acquired for the used data set in this research.

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates

    Safa Salahat & Naoufel Werghi

  2. BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, USA

    Ahmed Soliman & Ayman El-Baz

  3. Biomedical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates

    Tim McGloughlin

Authors
  1. Safa Salahat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed Soliman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tim McGloughlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Naoufel Werghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ayman El-Baz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Safa Salahat .

Editor information

Editors and Affiliations

  1. Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom

    María Valdés Hernández

  2. University of León, León, Spain

    Víctor González-Castro

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Salahat, S., Soliman, A., McGloughlin, T., Werghi, N., El-Baz, A. (2017). Segmentation of Abdominal Aortic Aneurysm (AAA) Based on Topology Prior Model. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60964-5_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60963-8

  • Online ISBN: 978-3-319-60964-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation