Skip to main content
SpringerLink
Log in

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.

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

Similar content being viewed by others

References

  1. S. Allender., European cardiovascular disease statistics. European Heart Network, 2008

  2. Frangi AF, Niessen WJ, Viergever MA: Three dimensional modeling for functional analysis of cardiac images: a review. IEEE Trans Med. Imag 20(1):2–25, 2001

    Article  CAS  Google Scholar 

  3. Petitjean C, Dacher J-N: A review of segmentation methods in short axis cardiac MR images. Med Imag Anal 15(2):169–184, 2011

    Article  Google Scholar 

  4. Shors S, Fung C, Francois C, Finn P, Fieno D: Accurate quantification of right ventricular mass at MR imaging by using cine true fast imaging with steady state precession: study in dogs. Radiology 230(2):383–388, 2004

    Article  PubMed  Google Scholar 

  5. Frangi AF, Niessen WJ, Hoogeveen R, van Walsum T, Viergever MA: Model based quantization of 3-D magnetic resonance angiographic images. IEEE Trans Med Imag 18(10):946–956, 1999

    Article  CAS  Google Scholar 

  6. Selle D, Preim B, Schnek A, Peitgen H: “Analysis of vasculature for liver surgical planning. IEEE Trans Med Imag 21(11):1344–1357, 2002

    Article  Google Scholar 

  7. Keegan J, Horkaew P, Buchanan T, Smart T, Yang G, Firmin D: Intra and interstudy reproducibility of coronary artery diameter measurements in magnetic resonance coronary angiography. J Magn Reson Imag 20(1):160–166, 2004

    Article  Google Scholar 

  8. Kolipaka A, Chatzimavroudis G, White R, O’Donnell T, Setser R: Segmentation of non-viable myocardium in delayed magnetic resonance images. Int J Cadiovasc Imag 21(2):303–311, 2005

    Article  Google Scholar 

  9. Noble N, Hill D, Breeuwer M et al: The automatic identification of hibernating myocardium. In: Proc. Intl. Conf. Med. Image Computing and Computer-Assisted Intervent (MICCAI), 2004, pp 890–898

  10. Schwitter J: Myocardial perfusion. J Magn Reson Imag 24(5):953–963, 2006

    Article  Google Scholar 

  11. Di Bella E, Sitek A et al: Time curve analysis techniques for dynamic contrast MRI studies. In: Intl. Conf Inf. Process. Med. Imag. (IPMI), 2001, pp 211–217

  12. Perperidis D, Mohiaddin R, Rueckert D: Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification. In: MICCAI, 2005, pp 402–410

  13. Besbes A, Komodakis N, Paragios N: Graph-based knowledge-driven discrete segmentation of the left ventricle. In: ISBI, 2009, pp 49–52

  14. Zhu Y, Papademetris X, Sinusas A et al: Segmentation of left ventricle from 3D cardiac mr image sequence using a subject specific dynamic model. In: Proc. CVPR, 2009, pp 1–8

  15. Sun W, Setin M, Chan R et al: Segmenting and tracking of the left ventricle by learning the dynamics in cardiac images. In: Proc. IPMI, , 2005, pp 553–565

  16. Davies RH, Twining CJ, Cootes TF, Waterton JC, Taylor CJ: A minimum description length approach to statistical shape modelling. IEEE Trans Med Imag 21:525–537, 2002

    Article  Google Scholar 

  17. Kaus MR, von Berg J, Weese J, Niessen W, Pekar V: Automated segmentation of the left ventricle in cardiac MRI. Med Image Anal 8(3):245–254, 2004

    Article  PubMed  Google Scholar 

  18. Jolly MP et al: Automatic recovery of the left ventricle blood pool in cardiac cine MR images. In: MICCAI, 2008, pp 110–118

  19. Lorenzo-Valdes M, Sanchez-Ortiz GI, Elkington AG, Mohiaddin RH, Rueckert D: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Med Image Anal 8(3):255, 2004

    Article  PubMed  Google Scholar 

  20. Niessen WJ, Romeny BMTH, Viergever MA: Geodesic deformable models for medical image analysis. IEEE Trans Med Imag 17(4):634–41, 1998

    Article  CAS  Google Scholar 

  21. Paragios N: A variational approach for the segmentation of the left ventricle in cardiac image analysis. Intl J Comp Vis 50(3):345–362, 2002

    Article  Google Scholar 

  22. Jolly MP: Automatic segmentation of the left ventricle in cardiac MR and CT images. Int J Comp Vision 70(2):151–163, 2006

    Article  Google Scholar 

  23. Ltjnen J, Kivist S, Koikkalainen J, Smutek D, Lauerma K: Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images. Med Image Anal 8(3):371–386, 2004

    Article  Google Scholar 

  24. van Assen C, Danilouchkine MG, Frangi AF, Ords S, Westenberg JJ, Reiber JH, Lelieveldt BP: pasm: a 3D-asm for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal 10(2):286–303, 2006

    Article  PubMed  Google Scholar 

  25. Mahapatra D, Sun Y: Joint registration and segmentation of dynamic cardiac perfusion images using MRFs. In: Proc. MICCAI, 2010, pp 493–501

  26. Mahapatra D, Sun Y: Integrating segmentation information for improved MRF based elastic image registration. IEEE Trans Imag Proc 21(1):170–183, 2012

    Article  Google Scholar 

  27. Mitchell SC, Lelieveldt BPF, et al: Multistage hybrid active appearance models: segmentation of cardiac MR and ultrasound images. IEEE Trans Med. Imag 20(5):415–423, 2001

    Article  CAS  Google Scholar 

  28. Goshtasby A, Turner D: Segmentation of cardiac cine MR images for extraction of right and left ventricular chambers. IEEE Trans Med Imag 14(1):56–64, 1995

    Article  CAS  Google Scholar 

  29. Weng J, Singh A, Chiu M: Learning based ventricle detection from cardiac MR and CT images. IEEE Trans Med Imag 16(4):378–391, 1997

    Article  CAS  Google Scholar 

  30. Katouzian A, Konofagau E, Prakash A: A new automated technique for left and right ventricular segmentation in magnetic resonance imaging in IEEE. In: EMBS, 2006, pp 3074–3077

  31. Gering D: Automatic segmentation of cardiac MRI. In: MICCAI, 2003, pp 524–532

  32. Cocosco C, Niessen W, Netsch T, Vonken E-J, Lund G, Stork A, Viergever M: “Automatic image driven segmentation of the ventricles in cardiac cine MRI. J Magn Reson Imag 28(2):366–374, 2008

    Article  Google Scholar 

  33. Battani R, Corsi C, Sarti A et al: Estimation of right ventricular volume without geometrical assumptions utilizing cardiac magnetic resonance data. In: Comput Cardiol, 2003, pp 81–84

  34. Pluempitiwiriyawej C, Moura JMF, Wu YL, Ho C: STACS: new active contour scheme for cardiac MR image segmentation. IEEE Trans Med Imag 24(5):593–603, 2005

    Article  Google Scholar 

  35. Sermeanst M, Moireau P, Camara O, Sainte-Marie J, Adriantsimiavona R, Cimrman R, Hill DL, Chapelle D, Razavi R: Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties. Med Image Anal. 10(4):642–656, 2006

    Article  Google Scholar 

  36. Billet F, Sermeanst M,Delingette H et al: Cardiac motion recovery and boundary conditions estimation by coupling an electromechanical model and cine-MRI data. In: Functional imaging and modeling of the heart (FMIH), 2009, pp 376–385

  37. Ordas S, Boisrobert L, Huguet M et al: Active shape models with invariant optimal features (IOFASM)—application to cardiac MRI segmentation. In: Comput Cardiol, 2003, pp 633–636

  38. Lorenzo-Valdes M, Sanchez-Ortis G, Mohiaddin R et al: Atlas based segmentation and tracking of 3D cardiac MR images using non-rigid registration. In: MICCAI, 2002, pp 642–650

  39. Cremers D, Tischhauser F, Weickert J, Schnorr C: Diffusion snakes: introducing statistical shape knowledge into the Mumford–Shah functional. Intl J Comp Vis 50(3):295–313, 2002

    Article  Google Scholar 

  40. Chang H, Yang Q, Parvin B: Bayesian approach for image segmentation with shape priors. In: CVPR, 2008, pp 1–8

  41. Freedman D, Zhang T: Interactive graph cut based segmentation with shape priors. In CVPR, 2005, pp 755–762

  42. Slabaugh G, Unal G: Graph cuts segmentation using an elliptical shape prior. In: ICIP, 2005, pp 1222–1225

  43. Mahapatra D, Sun Y: Orientation histograms as shape priors for left ventricle segmentation using graph cuts. In: Proc MICCAI, 2011, pp 420–427

  44. Boykov Y, Veksler O: Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23:1222–1239, 2001

    Article  Google Scholar 

  45. Vu N, Manjunath BS: Shape prior segmentation of multiple objects with graph cuts. in CVPR 2008

  46. Chittajallu DR, Shah SK, Kakadiaris IA: A shape driven MRF model for the segmentation of organs in medical images. In: CVPR, 2010, pp 3233–3240

  47. Veksler O: Star shape prior for graph cut segmentation. In: ECCV, 2008, pp 454–467

  48. Zhu-Jacquot J, Zabih R: Segmentation of the left ventricle in cardiac mr images using graph cuts with parametric shape priors. In: ICASSP, 2008, pp 521–524

  49. Ben Ayed I, Punithakumar K, Li S et al: Left ventricle segmentation via graph cut distribution matching. In: MICCAI 2009, pp 901–909

  50. Ali AM, Farag AA, El-Baz AS: Graph cuts framework for kidney segmentation with prior shape constraints. In: MICCAI 2007

  51. Mahapatra D, Sun Y: Registration of dynamic renal mr images using neurobiological model of saliency. In: Proc. ISBI, 2008, pp 1119–1122

  52. Belongie S, Malik J, Puzicha J: Shape matching and object recognition using shape contexts. IEEE PAMI 24(24):509–522, 2002

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  55. Fonseca CG: The cardiac atlas project an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16):2288–2295, 2011

    Article  PubMed  CAS  Google Scholar 

  56. Kadish AH, Bello D, Finn JP, Bonow RO, Schaechte A, Subacius H, Albert C, Daubert JP, Fonseca CG, Goldberger JJ: Rationale and design for the defribrillators to reduce risk by magnetic resonance imaging evaluation (determine) trial. J Cardiovascular Electrophysiology 20(9):982–987, 2009

    Article  Google Scholar 

  57. Elen A, Hermans J, Ganame J, Loeckx D, Bogaert J, Maes F, Suetens P: Automatic 3-D breath-hold related motion correction of dynamic multislice MRI. IEEE Trans Med Imag 29(3):868–878, 2010

    Article  Google Scholar 

  58. Young AA, Cowan BR, Thrupp SF, Hedley WJ, DellItalia LJ: Left ventricular mass and volume: fast calculation with guide-point modeling on MR images. Radiology 202(2):597–602, 2000

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Swiss Federal Institute of Technology (ETH), CAB F 61.1, Universitätstrasse 6, 8092, Zurich, Switzerland

    Dwarikanath Mahapatra

Authors
  1. Dwarikanath Mahapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dwarikanath Mahapatra.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mahapatra, D. Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors. J Digit Imaging 26, 721–730 (2013). https://doi.org/10.1007/s10278-012-9548-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-012-9548-5

Keywords

Search

Navigation