Skip to main content

Advertisement

SpringerLink
Log in

Automatic estimation of the aortic lumen geometry by ellipse tracking

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algorithm to extract the aorta geometry for either normal (with and without contrast) or abnormal computed tomography (CT) cases.

Methods

The algorithm we propose is a fast incremental technique that computes the 3D geometry of the aortic lumen from an initial contour located inside it. Our approach is based on the optimization of the 3D orientation of the cross sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. The optimization involves the size and eccentricity of the ellipse which best fits the aorta contour on each cross-sectional plane. The method works directly on the original CT and does not require a prior segmentation of the aortic lumen. We present experimental results to show the accuracy of the method and its ability to cope with challenging CT cases where the aortic lumen may have low contrast, different kinds of pathologies, artifacts, and even significant angulations due to severe elongations.

Results

The algorithm correctly tracked the aorta geometry in 380 of 385 CT cases. The mean of the dice similarity coefficient was 0.951 for aorta cross sections that were randomly selected from the whole database. The mean distance to a manually delineated segmentation of the aortic lumen was 0.9 mm for sixteen selected cases.

Conclusions

The results achieved after the evaluation demonstrate that the proposed algorithm is robust and accurate for the automatic extraction of the aorta geometry for both normal (with and without contrast) and abnormal CT volumes.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Sackett DL, Rosenberg W, Mc Gray JA, Haynes RB, Richardson WS (1996) Evidence-based medicine: what it is and what it isn’t. BMJ 312:71–2

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Hiratzka LF, Bakris GL, Beckman JA, Bersin RM, Carr VF, Casey DE, Eagle KA, Hermann LK, Isselbacher EM, Kazerooni EA, Kouchoukos NT, Lytle BW, Milewicz DM, Reich DL, Sen S, Shinn JA, Svensson LG, Williams DM (2010) 2010 ACCF/AHA/AATS/ACR/ASA/SCA/SCAI/SIR/ STS/SVM Guidelines for the diagnosis and management of patients with thoracic aortic disease. J Am Coll Cardiol 55(14):1509–1544

    Google Scholar 

  3. Lesage D, Angelini E, Bloch I, Funka-Lea G (2009) A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 13(6):819–845

    PubMed  Google Scholar 

  4. Wang S, Fu L, Yue Y, Kang Y, Liu J (2009) Fast and automatic segmentation of ascending aorta in MSCT volume data. In: 2nd International congress on image and signal processing (CISP), pp 1–5

  5. Martinez-Mera JA, Tahoces PG, Carreira JM, Suárez-Cuenca JJ, Souto M (2013) A hybrid method based on level set and 3D region growing for segmentation of the thoracic aorta. Comput Aided Surg 18(5–6):109–117

    PubMed  Google Scholar 

  6. Kurugol S, Come CE, Diaz AA, Ross JC (2015) Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions. Med Phys 42(9):5467–5478

    PubMed  PubMed Central  Google Scholar 

  7. Dasgupta A, Mukhopadhyay S, Mehre SA, Bhattacharyya P (2016) Morphological geodesic active contour based automatic aorta segmentation in thoracic CT images. In: International conference on computer vision and image processing (CVIP), pp 187–196

    Google Scholar 

  8. Xie Y, Padgett J, Biancardi AM, Reeves AP (2014) Automated aorta segmentation in low-dose chest CT images. Int J Comput Assoc Radiol 9:211–219

    Google Scholar 

  9. Krissian K, Malandain G, Ayache N, Vaillant R, Trousset Y (2000) Model-based detection of tubular structures in 3D images. Comput Vis Image Underst 80(2):130–171

    Google Scholar 

  10. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: International conference on medical image computing and computer-assisted intervention, pp 130–137

    Google Scholar 

  11. Alvarez L, Trujillo A, Cuenca C, González E, Esclarín J, Gomez L, Mazorra L, Alemán-Flores M, Tahoces PG, Carreira JM (2017) Tracking the aortic lumen geometry by optimizing the 3D orientation of its cross-sections. In: International conference on medical image computing and computer-assisted intervention (MICCAI 2017) LNCS, vol 10434, pp 174–181

    Google Scholar 

  12. Alvarez L, González E, Cuenca C, Trujillo A, Tahoces PG, Carreira JM (2018) Ellipse motion estimation using parametric snakes. J Math Imaging Vis 60(7):1095–1110

    Google Scholar 

  13. Elefteriades JA, Farkas EA (2010) Thoracic aortic aneurysm. Clinical pertinent controversies and uncertainties. J Am Coll Cardiol 55(9):841–857

    CAS  PubMed  Google Scholar 

  14. Rajiah P, Shchoenhagen P (2013) The role of computed tomography in pre-procedural planning of cardiovascular surgery and intervention. Insights Imaging 4:671–689

    PubMed  PubMed Central  Google Scholar 

  15. Redheuil A, Yu W-C, Mousseaux E, Harouni AA, Kachenoura N, Wu CO, Bluemke D, Lima JAC (2011) Age-related changes in aortic arch geometry relationship with proximal aortic function and left ventricular mass and remodeling. J Am Coll Cardiol 58(12):1262–1270

    PubMed  PubMed Central  Google Scholar 

  16. Alvarez L, González E, Esclarín J, Gomez L, Alemán-Flores M, Trujillo A, Cuenca C, Mazorra L, Tahoces PG, Carreira JM (2017) Robust detection of circles in the vessel contours and application to local probability density estimation. In: CVII-STENT workshop proceedings, held in conjunction with MICCAI 2017, LNCS, vol 10552, pp 3–11

    Google Scholar 

  17. Cuenca C, González E, Trujillo A, Esclarín J, Mazorra L, Alvarez L, Martínez-Mera JA, Tahoces PG, Carreira JM (2018) Fast and accurate circle tracking using active contour models. J Real-Time Image Process 14:793–802

    Google Scholar 

  18. Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beeke EJ, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP, Roberts RY, Smith AR, Starkey A, Batrah P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38(2):915–931

    PubMed  PubMed Central  Google Scholar 

  19. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057

    PubMed  PubMed Central  Google Scholar 

  20. Isgum I, Staring M, Rutten A, Prokop M, Viergever M, Van Ginneken B (2009) Multi-atlas-based segmentation with local decision fusion application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imag 28(7):1000–1010

    Google Scholar 

  21. Avila-Montes OC, Kurkure U, Nakazato R, Berman DS, Dey D, Kakadiaris IA (2013) Segmentation of the thoracic aorta in noncontrast cardiac CT images. IEEE J Biomed Health Inform 17(5):936–49

    PubMed  Google Scholar 

Download references

Acknowledgements

This research has partially been supported by the MINECO projects references TIN2016-76373-P (AEI/FEDER, UE) and MTM2016-75339-P (AEI/FEDER, UE) (Ministerio de Economía y Competitividad, Spain).

Author information

Authors and Affiliations

  1. Department of Electronics and Computer Science, Universidad de Santiago de Compostela, Santiago de Compostela, Spain

    Pablo G. Tahoces

  2. CTIM, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Luis Alvarez, Esther González, Carmelo Cuenca, Agustín Trujillo, Daniel Santana-Cedrés, Julio Esclarín, Luis Gomez, Luis Mazorra & Miguel Alemán-Flores

  3. Complejo Hospitalario Universitario de Santiago (CHUS), Santiago de Compostela, Spain

    José M. Carreira

Authors
  1. Pablo G. Tahoces
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luis Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Esther González
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carmelo Cuenca
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Agustín Trujillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniel Santana-Cedrés
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Julio Esclarín
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Luis Gomez
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Luis Mazorra
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Miguel Alemán-Flores
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. José M. Carreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pablo G. Tahoces.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tahoces, P.G., Alvarez, L., González, E. et al. Automatic estimation of the aortic lumen geometry by ellipse tracking. Int J CARS 14, 345–355 (2019). https://doi.org/10.1007/s11548-018-1861-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-018-1861-0

Keywords

Search

Navigation