143
Views
11
CrossRef citations to date
0
Altmetric
Original Articles

Integrated 3D anatomical model for automatic myocardial segmentation in cardiac CT imagery

, , &
Pages 690-706 | Received 21 Dec 2017, Accepted 13 Feb 2019, Published online: 07 Mar 2019

References

  • Caselles V, Kimmel R, Sapiro G. 1997. Geodesic active contours. Int J Comput Vis. 22(61):61–79.
  • CDC. 2015. Heart disease: scope and impact. . [accessed 2017 Dec 11] http://www.theheartfoundation.org/heart-disease-facts/heart-disease-statistics/.
  • Chakraborty A, Staib L, Duncan J. 1994. An integrated approach to boundary finding in medical images. Proc. IEEE Workshop Biomedical Image Analysis. Seattle, WA: IEEE. p. 13–22.
  • Chan T, Vese L. 1999. An active contour model without edges. Int. Conf. Scale-Space Theories in Computer Vision. Corfu, Greece: Springer-Verlag  Berlin Heidelberg. p. 141–151.
  • Chan T, Vese L. 2001. A level set algorithm for minimizing the mumford-shah functional in image processing. IEEE Workshop on Variational and Level Set Methods in Computer Vision. Vancouver, BC: IEEE,  p. 161–168.
  • Chen Y, Thiruvenkadam S, Huang F, Wilson D, Geiser EA, Tagare H. 2001. On the incorporation of shape priors into geometric active contours. IEEE Workshop on Variational and Level Set Methods in Computer Vision. Vancouver, BC: IEEE, p. 145–152.
  • Cheng H, Gupta K. 1989. A historical note on finite rotations. J Appl Mech. 56(1):139–145.
  • Cootes T, Edwards G, Taylor C. 2001. Active appearance models. IEEE Trans Pattern Anal Mach Intell. 23(6):681–685.
  • Cootes T, Taylor C. 1992. Smart snakes. Proceedings of British Machine Vision Conference. Leeds, UK: Springer, p. 266–275.
  • Cootes T, Taylor C. 1998. Active appearance models. Proceedings of European Conference on Computer Vision. Vol. 2; Freiburg, Germany: Springer-Verlag  Berlin Heidelberg. p. 484–498.
  • Cootes T, Taylor C, Cooper C, Graham J. 1995. Active shape models - their training and application. Comput Vision Image Understanding. 61(9):38–59.
  • Cootes TF, Taylor CJ 2001. Statistical models of appearance for computer vision. University of Manchester. Report No.: MSU-CSE-06-2.
  • Dryden I, Mardia K. 1998. Statistical shape analysis. Chichester, UK: John Wiley & Sons.
  • Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker M. 2008. Automatic model-based segmentation of the heart in ct images. IEEE Trans Med Imag. 27(9):1189–1201.
  • Gallego G, Yezzi A. 2015. A compact formula for the derivative of a 3-d rotation in exponential coordinates. J Math Imaging Vis. 51(3):378–384.
  • Gao Y, Kikinis R, Bouix S, Shenton M, Tannenbaum A. 2012. A 3d interactive multi-object segmentation tool using local robust statistics driven active contours. Med Image Anal. 16(6):1216–1217.
  • Goodall C. 1991. Procrustes methods in the statistical analysis of shape. J R Stat Soc Series B. 53(2):285–339.
  • Kichenessamy S, Kumar A, Olver P, Tannenbaum A, Yezzi A. 1995. Gradient flows and geometric active contour models. Proc. of Intl. Conf. Computer Vision. Cambridge, MA: IEEE, p. 810–815.
  • Kirişli HA, Schaap M, Klein S, Papadopoulou SL, Bonardi M, Chen CH, Weustink AC, Mollet NR, Vonken EJ, van der Geest RJ, et al. 2010. Evaluation of a multi-atlas based method for segmentation of cardiac cta data: a large-scale, multicenter, and multivendor study. Med Phys. 37(12):6279–6291.
  • Leventon M, Grimson E, Faugeras O. 2000. Statistical shape influence in geodesic active contours. Proc. IEEE Conf. on Computer Vision and Pattern Recognition. Hilton Head Island, SC: IEEE, Vol. 1. p. 316–323.
  • Leveton M 2000. Statistical models in medical image analysis [ dissertation]. Boston (MA): Massachussets Institute of Technology.
  • Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W. 2003. Pet-ct image registration in the chest using free-form deformations. IEEE Trans Med Imaging. 22:120–128.
  • Mitchell SC, Lelieveldt B, van der Geest RJ, Bosch JG, Reiber JHC, Sonka M. 2001. Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac mr images. IEEE Trans Med Imag. 20(5):415–423.
  • Mumford D, Shah J. 1989. Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math. 42(6):577–685.
  • Murray R, Li Z, Sastry S. 1994. A mathematical introduction to robotic manipulation. Boca Raton, FL: CRC Press.
  • Osher S, Sethian J. 1988. Fronts propagating with curvature dependent speed: algorithms based on hamilton-jacobi formulations. J Comput Phys. 79(1):12–49.
  • Piccinelli M, Faber T, Arepalli C, Appia V, Vinten-Johnsen J. 2014. Automatic detection of left and right ventricles from cta enables efficient alignment of anatomy with myocardial perfusion data. J Nucl Cardiol. 21:96–108.
  • Qian N. 1999. On the momentum term in gradient descent learning algorithms. Neural Networks. 12(1):145–151.
  • Rohlfing T, Brandt R, Menzel R, Maurer CR. 2003. Segmentation of three-dimensional images using non-rigid registration: methods and validation with application to confocal microscopy images of bee brains. Proc SPIE. 5032:363–374.
  • Sebastian R. 2017. An overview of gradient descent optimization algorithms. arXiv. 1609.04747v2 [cs.LG]:1–14.
  • Sethian J. 1996. A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci U S A. 93(4):1591–1595.
  • Shahzad R, Bos D, Budde RPJ, Pellikaan K, Niessen WJ, van der Lugt A, Walsum T. 2017. Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac ct scans. Phys Med Biol. 62(9):3798. [accessed 2018 Mar 25] http://stacks.iop.org/0031-9155/62/i=9/a=3798.
  • Staib L, Duncan J. 1992. Boundary finding with parametrically deformable contour models. IEEE Trans Pattern Anal Mach Intell. 14:1061–1075.
  • Statistical binary classification method. 2017. https://en.wikipedia.org/wiki/F1_score.
  • Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A. 2003. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging. 22:137–154.
  • van Assen HC, Danilouchkine MG, Dirksen MS, Reiber JH, Lelieveldt BP. 2008. A 3-d active shape model driven by fuzzy inference: application to cardiac ct and mr. IEEE Trans Inf Technol Biomed. 12(5):595–605.
  • van Assen HC, Danilouchkine MG, Frangi FF, Ordas S, Westenberg JJ, Reiber JH. 2006. Spasm: A 3d-asm for segmentation of sparse and arbitrarily oriented cardiac mri data. Med Image Anal. 10(2):286–303.
  • Vikram A, Ganapathy B, Abufadel A, Yezzi A, Faber T. 2010. A regions of confidence based approach to enhance segmentation with shape priors. Proc. of SPIE-IS&T Electronic Imaging; San Jose, CA: SPIE; p. 753302.
  • Vikram A, Ganapathy B, Yezzi A, Faber T. 2011. Localized principal component analysis based curve evolution: A divide and conquer approach. Proc. of IEEE International Conference in Computer Vision. Barcelona, Spain: IEEE, p. 1981–1986.
  • Weese J, Kaus MR, Lorenz C, Lobregt S, Truyen R, Pekar V. 2001. Shape constrained deformable models for 3-d medical image segmentation. Information Processing in Medical Imaging; Berlin Heidelberg: Springer. p. 380–387.
  • Yezzi A, Kichenassamy S, Kumar A, Olver P, Tannenbaum A. 1997. A geometric snake model for segmentation of medical imagery. IEEE Trans Med Imaging. 16:199–209.
  • Yezzi A, Zollei L, Kapur T. 2003. A variational framework for integrating segmentation and registration through active contours. J Med Image Anal. 7:171–185.
  • Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D. 2008. Four-chamber heart modeling and automatic segmentation for 3d cardiac ct volumes using marginal space learning and steerable features. IEEE Trans Med Imag. 27(11):1668–1681.
  • Zhu L, Gao Y, Appia V, Yezzi A, Arepalli C, Faber T, Stillman A, Tannenbaum A. 2013a. Automatic delineation of the myocardial wall from ct images via shape segmentation and variational region growing. IEEE Trans Biomed Imaging. 60(10):2887–2895.
  • Zhu L, Gao Y, Appia V, Yezzi A, Arepalli C, Faber T, Stillman A, Tannenbaum A. 2013b. Automatic delineation of the myocardial wall from ct images via shape segmentation and variational region growing. IEEE Trans. on Biomedical Engineering. p. 2887–2895. https://ieeexplore.ieee.org/document/6523066
  • Zhu L, Gao Y, Appia V, Yezzi A, Arepalli C, Faber T, Stillman A, Tannenbaum A. 2014. A complete system for automatic extraction of left ventricular myocardium from ct images using shape segmentation and contour evolution. IEEE Trans Image Process. 23(3):1340–1351.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.