Skip to main content
SpringerLink
Log in

Validation of an automated system for luminal and medial-adventitial border detection in three-dimensional intravascular ultrasound

  • Published:
The International Journal of Cardiovascular Imaging Aims and scope Submit manuscript

Abstract

The precise tomographic assessment of coronary artery disease by intravascular ultrasound (IVUS) is useful in quantitative studies. Such studies require identification of luminal and medial-adventitial (MA) borders in a sequence of IVUS images. We have developed a three-dimensional (3D) active-surface system for border detection that facilitates the analysis of many images with minimal user interaction. To assess the validity of the technique, luminal and MA borders in 529 end-diastolic images from nine coronary arterial segments (58.8 ± 14.2 images per patient) were traced manually by four experienced observers. The computer-detected borders were compared with borders determined by the four observers using a modified Williams' index (WI), the ratio of inter-observer variability to computer-observer variability. While manual tracing required 49.2 ± 12.1 min for analysis, the analysis system identified luminal (R 2 = 0.92) and MA borders (R 2 = 0.97) in 13.8 ± 4.0 min, a decrease of 35.4 min (p < 0.000001). The computer minus observer differences in lumen area and MA area were −0.88 ± 0.90 and −0.07 ± 0.63 mm2. Therefore, the computer system underestimated both lumen and MA area, but this effect was very small in MA area. The WI values and 95% confidence intervals were 0.98 (0.89,1.06) for luminal border detection and 0.99 (0.95,1.04) for MA border detection. Plaque volume measurements, a common endpoint of clinical trials, also verified the accuracy of the technique (R 2 = 0.98). The proposed 3D active-surface border detection system provides a faster and less-tedious alternative to manual tracing for assessment of coronary artery anatomy in vivo.

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

Similar content being viewed by others

References

  1. Tobis JM, Mahon DJ, Goldberg SL, Nakamura S, Colombo A. Lessons from intravascular ultrasonography: observations during interventional angioplasty procedures. J Clin Ultrasound 1993; 21(9): 589–607.

    Google Scholar 

  2. Hoffmann R, Mintz GS, Popma JJ, et al. Overestimation of acute lumen gain and late lumen loss by quantitative coronary angiography (compared with intravascular ultrasound) in stented lesions. Am J Cardiol 1997; 80(10): 1277–1281.

    Google Scholar 

  3. Weintraub WS. Relation of angiographic and ultrasound assessment of plaque progression to clinical outcomes. Am J Cardiol 1998; 81(8A): 69F–72F.

    Google Scholar 

  4. Bermejo J, Botas J, Garcia E, et al. Mechanisms of residual lumen stenosis after high-pressure stent implantation: a quantitative coronary angiography and intravascular ultrasound study. Circulation 1998; 98(2): 112–118.

    Google Scholar 

  5. Sabate M, Serruys PW, van der Giessen WJ, et al. Geometric vascular remodeling after balloon angioplasty and beta-radiation therapy: a three-dimensional intravascular ultrasound study. Circulation 1999; 100(11): 1182–1188.

    Google Scholar 

  6. Nissen SE, Yock P. Intravascular ultrasound: novel pathophysiological insights and current clinical applications. Circulation 2001; 103(4): 604–616.

    Google Scholar 

  7. Mintz GS, Painter JA, Pichard AD, et al. Atherosclerosis in angiographically'normal' coronaryarteryreference segments: an intravascular ultrasound studywith clinical correlations. J Am Coll Cardiol 1995; 25(7): 1479–1485.

    Google Scholar 

  8. Hausmann D, Johnson JA, Sudhir K, et al. Angiographically silent atherosclerosis detected by intravascular ultrasound in patients with familial hypercholesterolemia and familial combined hyperlipidemia: correlation with high density lipoproteins. J Am Coll Cardiol 1996; 27(7): 1562–1570.

    Google Scholar 

  9. Ziada KM, Tuzcu EM, De Franco AC, et al. Intravascular ultrasound assessment of the prevalence and causes of angiographic 'haziness' following high-pressure coronary stenting. J Am Cardiol 1997; 80(2): 116–121.

    Google Scholar 

  10. Tsutsui H, Ziada KM, Schoenhagen P, et al. Lumen loss in transplant coronaryartery disease is a biphasic process involving earlyintimal thickening and late constrictive remodeling: Results from a 5-year intravascular ultrasound study. Circulation 2001; 104(6): 653–657.

    Google Scholar 

  11. Zhang X, McKay CR, Sonka M. Tissue characterization in intravascular ultrasound images. IEEE Trans Med Imaging 1998; 17(6): 889–899.

    Google Scholar 

  12. Haas C, Ermert H, Holt S, Grewe P, Machraoui A, Barmeyer J. Segmentation of 3D intravascular ultrasonic images based on a random field model. Ultrasound Med Biol 2000; 26(2): 297–306.

    Google Scholar 

  13. von Birgelen C, Di Mario C, Li W, et al. Morphometric analysis in three-dimensional intracoronary ultrasound: an in vitro and in vivo study performed with a novel system for the contour detection of lumen and plaque. Am Heart J 1996; 132(3): 516–527.

    Google Scholar 

  14. Kovalski G, Beyar R, Shofti R, Azhari H. Three-dimensional automatic quantitative analysis of intravascular ultrasound images. Ultrasound Med Biol 2000; 26(4): 527–537.

    Google Scholar 

  15. Siegel RJ, Swan K, Edwalds G, Fishbein MC. Limitations of postmortem assessment of human coronary artery size and luminal narrowing: differential effects of tissue fixation and processing on vessels with different degrees of atherosclerosis. J Am Coll Cardiol 1985; 5(2 Pt 1): 342–346.

    Google Scholar 

  16. Peters RJG, Kok WEM, Rijsterborgh H, et al. Reproducibility of quantitative measurements from intracoronary ultrasound images. Beat-to-beat variabilityand influence of the cardiac cycle. Eur Heart J 1996; 17(10): 1593–1599.

    Google Scholar 

  17. Meier DS, Cothren RM, Vince DG, Cornhill JF. Automated morphometryof coronary arteries with digital image analysis of intravascular ultrasound. Am Heart J 1997; 133(6): 681–690.

    Google Scholar 

  18. Shekhar R, Cothren RM, Vince DG, Chandra S, Thomas JD, Cornhill JF. Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images. Comput Med Imaging Graph 1999; 23(6): 299–309.

    Google Scholar 

  19. Klingensmith JD, Shekhar R, Vince DG. Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images. IEEE Trans Med Imaging 2000; 19(10): 996–1011.

    Google Scholar 

  20. Williams GW. Comparing the joint agreement of several raters with another rater. Biometrics 1976; 32(3): 619–627.

    Google Scholar 

  21. Huttenlocher DP, Klanderman GA, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 1993; 15(9): 850–863.

    Google Scholar 

  22. Chalana V, Kim Y. A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 1997; 16(5): 642–652.

    Google Scholar 

  23. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1(8476): 307–310.

    Google Scholar 

  24. Mojsilovic A, Popovic M, Amodaj N, Babic R, Ostojic M. Automatic segmentation of intravascular ultrasound images: a texture-based approach. Ann Biomed Eng 1997; 25(6): 1059–1071.

    Google Scholar 

  25. Sonka M, Zhang X, Siebes M, Bissing MS, DeJong SC, Collins SM, et al. Segmentation of intravascular ultrasound images: a knowledge-based approach. IEEE Trans Med Imaging 1995; 14(4): 719–732.

    Google Scholar 

  26. Takagi A, Hibi K, Zhang X, Teo TJ, Bonneau HN, Yock PG, et al. Automated contour detection for high-frequency intravascular ultrasound imaging: a technique with blood noise reduction for edge enhancement. Ultrasound Med Biol 2000; 26(6): 1033–1041.

    Google Scholar 

  27. Bouma CJ, Niessen WJ, Zuiderveld KJ, Gussenhoven EJ, Viergever MA. Automated lumen definition from 30 MHz intravascular ultrasound images. Med Image Anal 1997; 1(4): 363–377.

    Google Scholar 

  28. Finet G, Maurincomme E, Reiber JH, Savalle L, Magnin I, Beaune J. Evaluation of an automatic intraluminal edge detection technique for intravascular ultrasound images. Jpn Circ J 1998; 62(2): 115–121.

    Google Scholar 

  29. von Birgelen C, van der Lugt A, Nicosia A, et al. Computerized assessment of coronary lumen and atherosclerotic 103 plaque dimensions in three-dimensional intravascular ultrasound correlated with histomorphometry. Am J Cardiol 1996; 78(11): 1202–1209.

    Google Scholar 

  30. von Birgelen C, de Vrey EA, Mintz GS, et al. ECG-gated three-dimensional intravascular ultrasound: feasibility and reproducibility of the automated analysis of coronary lumen and atherosclerotic plaque dimensions in humans. Circulation 1997; 96(9): 2944–2952.

    Google Scholar 

  31. Schartl M, Bocksch W, Koschyk DH, et al. Use of intravascular ultrasound to compare effects of different strategies of lipid-lowering therapyon plaque volume and composition in patients with coronary artery disease. Circulation 2001; 104(4): 387–392.

    Google Scholar 

  32. von Birgelen C, Mintz GS, Nicosia A, et al. Electrocardiogram-gated intravascular ultrasound image acquisition after coronary stent deployment facilitates on-line three-dimensional reconstruction and automated lumen quantification. J Am Coll Cardiol 1997; 30(2): 436–443.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Lerner Research Institute, OH, 44195, USA

    Jon D. Klingensmith & D. Geoffrey Vince

  2. Department of Cardiology, The Cleveland Clinic Foundation, Cleveland, OH, 44195, USA

    E. Murat Tuzcu & Steven E. Nissen

Authors
  1. Jon D. Klingensmith
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Murat Tuzcu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Steven E. Nissen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. Geoffrey Vince
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Klingensmith, J.D., Tuzcu, E.M., Nissen, S.E. et al. Validation of an automated system for luminal and medial-adventitial border detection in three-dimensional intravascular ultrasound. Int J Cardiovasc Imaging 19, 93–104 (2003). https://doi.org/10.1023/A:1022843104297

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1022843104297

Search

Navigation