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Automatic detection of carotid arteries in computed tomography angiography: a proof of concept protocol

  • Original Paper
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Abstract

Atherosclerosis is one of the leading causes of mortality in the western world. Computed tomography angiography (CTA) is the conventional imaging method used for pre-surgery assessment of the blood flow within the carotid vessel. In this paper, we present a proof of concept of a novel, fast and operator independent protocol for the automatic detection (seeding) of the carotid arteries in CTA in the thorax and upper neck region. The dataset is composed of 14 patients’ CTA images of the neck region. The performance of this method is compared with manual seeding by four trained operators. Inter-operator variation is also assessed based on the dataset. The minimum, average and maximum coefficient of variation among the operators was (0, 2, 5 %), respectively. The performance of our method is comparable with the state of the art alternative, presenting a detection rate of 75 and 71 % for the lowest and uppermost image levels, respectively. The mean processing time is 167 s per patient versus 386 s for manual seeding. There are no significant differences between the manual and automatic seed positions in the volumes (p = 0.29). A fast, operator independent protocol was developed for the automatic detection of carotid arteries in CTA. The results are encouraging and provide the basis for the creation of automatic detection and analysis tools for carotid arteries.

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Notes

  1. Matlab function: bwareaopen (image, minimum area value).

  2. Matlab function: regionprops (image, properties).

  3. Matlab function: pdist2(X 1 , X 2 ) (expressed as dist in this manuscript).

  4. Matlab function: ranksum.

Abbreviations

A:

Area

BDvar :

Border distance (variance)

CB:

Carotid bifurcation

CCA:

Common carotid artery

CTA:

Computed tomography angiography

CV:

Coefficient of variation

DCV:

Distance to center of volume

DT:

Distance to the center of the trachea

ECC:

Eccentricity

HU:

Hounsfield units

ICA:

Internal carotid artery

MRI:

Magnetic resonance imaging

Op:

Operator

ROI:

Region of interest

SF:

Shape factor

TP:

Trachea/pharynx volume

Var:

Variance

w p :

Weight of descriptor P

\({\tau }_{P}\) :

Threshold of descriptor P

References

  1. Wittenauer R, Smith L (2012) Priority medicines for Europe and the world: a public health approach to innovation update on 2004 background paper

  2. Go AS, Mozaffarian D, Roger VL et al (2013) Heart disease and stroke statistics-2013 update: a report from the American Heart Association. Circulation. doi:10.1161/CIR.0b013e31828124ad

    Google Scholar 

  3. Enterline DS, Kapoor G (2006) A practical approach to CT angiography of the neck and brain. Tech Vasc Interv Radiol 9:192–204. doi:10.1053/j.tvir.2007.03.003

    Article  PubMed  Google Scholar 

  4. Vukadinovic D, van Walsum T, Manniesing R et al (2010) Segmentation of the outer vessel wall of the common carotid artery in CTA. IEEE Trans Med Imaging 29:65–76. doi:10.1109/TMI.2009.2025702

    Article  PubMed  Google Scholar 

  5. Augst AD, Ariff B, Thom SAG McG, Xu XY, Hughes AD (2007) Analysis of complex flow and the relationship between blood pressure, wall shear stress, and intima-media thickness in the human carotid artery. Am J Physiol Heart Circ Physiol 293(2):H1031–H1037. doi:10.1152/ajpheart.00989.2006

    Article  CAS  PubMed  Google Scholar 

  6. Santos FLC, Joutsen A, Terada M et al (2014) A semi-automatic segmentation method for the structural analysis of carotid atherosclerotic plaques by computed tomography angiography. J Atheroscler Thromb 21:930–940. doi:10.5551/jat.21279

    Article  PubMed  Google Scholar 

  7. Indes LG, Gates L, Indes J (2014) Evaluation and treatment of carotid artery stenosis, carotid artery disease. In: Rezzani R (ed) Carotid artery disease—from bench to bedside beyond. InTech, Rijeka, pp 1–18

  8. Markiewicz T, Dziekiewicz M, Maruszyński M et al (2014) Recognition of atherosclerotic plaques and their extended dimensioning with computerized tomography angiography imaging. Int J Appl Math Comput Sci 24:33–47. doi:10.2478/amcs-2014-0003

    Article  Google Scholar 

  9. Bartlett E (2006) Quantification of carotid stenosis on CT angiography. AJR Am J Roentgenol 36:13–19. doi:10.1016/j.ejvs.2008.04.016

    Google Scholar 

  10. Renard F, Yang YYY (2008) Image analysis for detection of coronary artery soft plaques in MDCT images. In: 2008 5th IEEE int symp biomed imaging from nano to macro, pp 25–28. doi:10.1109/ISBI.2008.4540923

  11. Weert TT, Monyé C, Meijering E et al (2008) Assessment of atherosclerotic carotid plaque volume with multidetector computed tomography angiography. Int J Cardiovasc Imaging 24:751–759. doi:10.1007/s10554-008-9309-1

    Article  PubMed  PubMed Central  Google Scholar 

  12. Atherton TJ, Kerbyson DJ (1999) Size invariant circle detection. Image Vis Comput 17:795–803. doi:10.1016/S0262-8856(98)00160-7

    Article  Google Scholar 

  13. Yuen H, Princen J, Illingworth J, Kittler J (1990) Comparative study of Hough Transform methods for circle finding. Image Vis Comput 8:71–77. doi:10.1016/0262-8856(90)90059-E

    Article  Google Scholar 

  14. Adame IM, Van Der Geest RJ, Wasserman B et al (2004) Automatic segmentation and plaque characterization in atherosclerotic carotid artery MR images. Magn Reson Mater Phys Biol Med 16:227–234. doi:10.1007/s10334-003-0030-8

    CAS  Google Scholar 

  15. Bogunović H, Pozo JM, Cárdenes R, Frangi AF (2010) Automatic identification of internal carotid artery from 3DRA images. Conf Proc EEE Eng Med Biol Soc 2010:5343–5346. doi:10.1109/IEMBS.2010.5626473

    Google Scholar 

  16. Sanderse M, Marquering H, Hendriks E et al (2005) Automatic initialization algorithm for carotid artery segmentation in CTA images. In: Medical image computing and computer-assisted intervention – MICCAI 2005, vol 3750. Springer, Heidelberg, pp 846–853. doi:10.1007/11566489_104

  17. Kim J, Srinivasan MA (2005) Characterization of viscoelastic soft tissue properties from in vivo animal experiments and inverse FE parameter estimation. Med Image Comput Comput Assist Interv 8:599–606. doi:10.1007/11566489

    PubMed  Google Scholar 

  18. Hassan M, Chaudhry A, Khan A, Iftikhar MA (2014) Robust information gain based fuzzy c-means clustering and classification of carotid artery ultrasound images. Comput Methods Program Biomed 113:593–609. doi:10.1016/j.cmpb.2013.10.012

    Article  Google Scholar 

  19. Kawai F, Hayata K, Ohmiya J et al (2013) Fully automatic detection of the carotid artery from volumetric ultrasound images using anatomical position-dependent LBP features. In: Wu G, Zhang D, Shen D et al (eds) Lecture notes in computer science (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). Springer, Cham, pp 41–48

    Google Scholar 

  20. Stoitsis J, Golemati S, Kendros S, Nikita KS (2008) Automated detection of the carotid artery wall in B-mode ultrasound images using active contours initialized by the Hough transform. Conf Proc IEEE Eng Med Biol Soc 2008:3146–3149. doi:10.1109/IEMBS.2008.4649871

    CAS  PubMed  Google Scholar 

  21. Acharya UR, Sree SV, Mookiah MR et al (2013) Computed tomography carotid wall plaque characterization using a combination of discrete wavelet transform and texture features: a pilot study. Proc Inst Mech Eng Part H - J Eng Med 227:643–654. doi:10.1177/0954411913480622

    Article  CAS  Google Scholar 

  22. Santos FLC, Joutsen A, Salenius J, Eskola H (2014) Fusion of edge enhancing algorithms for atherosclerotic carotid wall contour detection in computed tomography angiography. In: Comput. Cardiol. Conf. (CinC), 2014. Cambridge, MA, pp 925–928

  23. Ferguson GG, Eliasziw M, Barr HWK et al (1999) The North American symptomatic carotid endarterectomy trial: surgical results in 1415 patients. Stroke 30:1751–1758. doi:10.1161/01.STR.30.9.1751

    Article  CAS  PubMed  Google Scholar 

  24. Fox AJ (1993) How to measure carotid stenosis. Radiology 186:316–318. doi:10.1148/radiology.186.2.8421726

    Article  CAS  PubMed  Google Scholar 

  25. Griscom NT, Wohl MEB (1986) Dimensions of the growing trachea related to age and gender. Am J Roentgenol 146:233–237. doi:10.2214/ajr.146.2.233

    Article  CAS  Google Scholar 

  26. Hoffstein V, Fredberg JJ (1991) The acoustic reflection technique for non-invasive assessment of upper airway area. Eur Respir J 4:602–611

    CAS  PubMed  Google Scholar 

  27. Eller A, Wuest W, Kramer M et al (2014) Carotid CTA: radiation exposure and image quality with the use of attenuation-based, automated kilovolt selection. AJNR Am J Neuroradiol 35:237–241. doi:10.3174/ajnr.A3659

    Article  CAS  PubMed  Google Scholar 

  28. Zhang Z, Berg M, Ikonen A et al (2005) Carotid stenosis degree in CT angiography: assessment based on luminal area versus luminal diameter measurements. Eur Radiol 15:2359–2365. doi:10.1007/s00330-005-2801-2

    Article  PubMed  Google Scholar 

  29. Podczeck F, Newton JM (1994) A shape factor to characterize the quality of spheroids. J Pharm Pharmacol 46:82–85. doi:10.1111/j.2042-7158.1994.tb03745.x

    Article  CAS  PubMed  Google Scholar 

  30. Koon D-J Region Growing. http://www.mathworks.com/matlabcentral/fileexchange/19084-region-growing

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Acknowledgments

FS was supported by the CIMO Foundation (Centre for International Mobility; KM-12-8107), Tampere University Hospital and the iBioMEP doctoral scholarship. AJ was supported by the Tampere City Science Fund and by Tampere University Hospital. MP was supported by the Finnish Cultural Foundation (Central Fund). This project was also partly supported by the Competitive State Research Financing of the Expert Responsibility Area of Tampere University Hospital (Grant number R07210/9 K115). The authors would like to thank RN Raija Paalavuo, MD Anna-Kaisa Parkkila, and Lic.Sc., Med. Phys. Ullamari Hakulinen for their help with patient recruitment and management.

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Authors and Affiliations

  1. Department of Electronics and Communications Engineering, Tampere University of Technology, Finn-Medi 1 4-208, Biokatu 6, 33520, Tampere, Finland

    Florentino Luciano Caetano dos Santos, Atte Joutsen, Michelangelo Paci & Hannu Eskola

  2. BioMediTech, Tampere, Finland

    Michelangelo Paci

  3. Division of Vascular Surgery, Department of Surgery, Tampere University Hospital and Medical School, Tampere, Finland

    Juha Salenius

  4. Department of Radiology, Regional Imaging Centre, Tampere University Hospital, Tampere, Finland

    Hannu Eskola

Authors
  1. Florentino Luciano Caetano dos Santos
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  2. Atte Joutsen
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  3. Michelangelo Paci
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  5. Hannu Eskola
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Corresponding author

Correspondence to Florentino Luciano Caetano dos Santos.

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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. This research was approved by the Ethics Committee of the Pirkanmaa Hospital District (decision number R07210).

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Informed consent was obtained from all individual participants included in the study.

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dos Santos, F.L.C., Joutsen, A., Paci, M. et al. Automatic detection of carotid arteries in computed tomography angiography: a proof of concept protocol. Int J Cardiovasc Imaging 32, 1299–1310 (2016). https://doi.org/10.1007/s10554-016-0880-6

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