Advertisement

HALE: Healthy Area of Lumen Estimation for Vessel Stenosis Quantification

  • Sethuraman SankaranEmail author
  • Michiel Schaap
  • Stanley C. Hunley
  • James K. Min
  • Charles A. Taylor
  • Leo Grady
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

One of the most widely used non-invasive clinical metric for diagnosing patients with symptoms of coronary artery disease is %stenosis derived from cCTA. Estimation of %stenosis involves two steps - the measurement of local diameter and the measurement of a reference healthy diameter. The estimation of a reference healthy diameter is challenging, especially in diffuse, ostial and bifurcation lesions. We develop a machine learning algorithm using random forest regressors for the estimation of healthy diameter using downstream and upstream properties of coronary tree vasculature as features. We use a population-based estimation, in contrast to single patient estimation that is used in the majority of the literature. We demonstrate that this method is able to predict the diameter of healthy sections with a correlation coefficient of 0.95. We then estimate %stenosis based on the ratio of the local vessel diameter to the estimated healthy diameter. Compared to a reference anisotropic kernel regression method, the proposed method, HALE (Healthy Area of Lumen Estimation), has a superior area under curve (0.90 vs 0.83) and operating point sensitivity/specificity (90 %/85 % vs 82 %/76 %) for the detection of stenoses. We also demonstrate superior performance of HALE against invasive quantitative coronary angiography (QCA), compared to the reference method (mean absolute error: 14 % vs 31 %, p\(\,<\,\)0.001).

Keywords

Healthy lumen diameter Stenosis detection Coronary artery disease 

References

  1. 1.
    Kirisli, H.A., et al.: Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med. Image Anal. 17(8), 859–876 (2012)CrossRefGoogle Scholar
  2. 2.
    Shahzad, R., et al.: Automatic segmentation, detection and quantification of coronary artery stenoses on CTA. Int. J. Cardiovasc. Imaging 29(8), 1847–1859 (2013)CrossRefGoogle Scholar
  3. 3.
    Sankaran, S., Grady, L., Taylor, C.A.: Fast computation of hemodynamic sensitivity to lumen segmentation uncertainty. IEEE TMI 34(12), 2562–2571 (2015)Google Scholar
  4. 4.
    Huo, Y., et al.: CT-based diagnosis of diffuse coronary artery disease on the basis of scaling power laws. Radiology 268(3), 694–701 (2013)CrossRefGoogle Scholar
  5. 5.
    Lesage, D., et al.: A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009)CrossRefGoogle Scholar
  6. 6.
    Schaap, M., et al.: Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med. Image Anal. 13(5), 701–714 (2009)CrossRefGoogle Scholar
  7. 7.
    Sherman, T.F.: On connecting large vessels to small. The meaning of Murray’s law. J. Gen. Physiol. 78(4), 431–453 (1981)CrossRefGoogle Scholar
  8. 8.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  9. 9.
    Min, J.K., et al.: Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 308(12), 1237–1245 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Sethuraman Sankaran
    • 1
    Email author
  • Michiel Schaap
    • 1
  • Stanley C. Hunley
    • 1
  • James K. Min
    • 2
  • Charles A. Taylor
    • 1
  • Leo Grady
    • 1
  1. 1.HeartFlow Inc.Redwood CityUSA
  2. 2.Department of RadiologyWeill-Cornell Medical CollegeNew YorkUSA

Personalised recommendations