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Automatic cardiac ventricle segmentation in MR images: a validation study

International Journal of Computer Assisted Radiology and Surgery volume 6, pages 573–581 (2011)Cite this article

Abstract

Purpose

Segmenting the cardiac ventricles in magnetic resonance (MR) images is required for cardiac function assessment. Numerous segmentation methods have been developed and applied to MR ventriculography. Quantitative validation of these segmentation methods with ground truth is needed prior to clinical use, but requires manual delineation of hundreds of images. We applied a well-established method to this problem and rigorously validated the results.

Methods

An automatic method based on active contours without edges was used for left and the right ventricle cavity segmentation. A large database of 1,920 MR images obtained from 59 patients who gave informed consent was evaluated. Two standard metrics were used for quantitative error measurement.

Results

Segmentation results are comparable to previously reported values in the literature. Since different points in the cardiac cycle and different slice levels were used in this study, a detailed error analysis is possible. Better performance was obtained at end diastole than at end systole, and on mid-ventricular slices than apical slices. Localization of segmentation errors were highlighted through a study of their spatial distribution.

Conclusions

Ventricular segmentation based on region-driven active contours provided satisfactory results in MRI, without the use of a priori knowledge. The study of error distribution allows identification of potential improvements in algorithm performance.

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References

  1. Caudron J, Fares J, Bauer F, Dacher J-N (2011) Left ventricular diastolic function assessment by cardiac MRI. RadioGraphics (in press)

  2. van der Geest R, Jansen E, Buller V, Reiber J (1994) Automated detection of left ventricular epi- and endocardial contours in short-axis MR images. In: Computers in cardiology. Bethesda, MD, USA, pp 33–36

  3. O’Donnell T, Funka-Lea G, Tek H, Jolly M-P, Rasch M (2006) Comprehensive cardiovascular image analysis using MR and CT at Siemens Corporate Research. Int J Comput Vis 70(2): 165–178

    Article  Google Scholar 

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

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

    PubMed  Article  CAS  Google Scholar 

  6. Pednekar A, Kurkure U, Muthupillai R, Flamm S (2006) Automated left ventricular segmentation in cardiac MRI. IEEE Trans Biomed Eng 53(7): 1425–1428

    PubMed  Article  Google Scholar 

  7. Lynch M, Ghita O, Whelan P (2006) Automatic segmentation of the left ventricle cavity and myocardium in MRI data. Comput Biol Med 36(4): 389–407

    PubMed  Article  CAS  Google Scholar 

  8. Kurkure U, Pednekar A, Muthupillai R, Flamm S, Kakadiaris IA (2009) Localization and segmentation of left ventricle in cardiac cine-MR images. IEEE Trans Biomed Eng 56(5): 1360–1370

    PubMed  Article  Google Scholar 

  9. El Berbari R, Bloch I, Redheuil A, Angelini E, Mousseaux E, Frouin F, Herment A (2007) An automated myocardial segmentation in cardiac MRI. In: Conf Proc IEEE Eng Med Biol Soc, pp 4508–4511

  10. Xu C, Pham DL, Prince JL (2000) Medical image segmentation using deformable models. In: Handbook of medical imaging, vol 2: Medical Image Processing and Analysis. SPIE Press, pp 129–174

  11. Paragios N (2002) A variational approach for the segmentation of the left ventricle in cardiac image analysis. Int J Comput Vis 50(3): 345–362

    Article  Google Scholar 

  12. Chakraborty A, Staib L, Duncan JS (1996) Deformable boundary finding in medical images by integrating gradient and region information. IEEE Trans Med Imaging 15: 859–870

    PubMed  Article  CAS  Google Scholar 

  13. Zhu Y, Papademetris X, Sinusas AJ, Duncan JS (2010) Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model. IEEE Trans Med Imaging 29(3): 669–687

    PubMed  Article  Google Scholar 

  14. Montagnat J, Delingette H (2005) 4D deformable models with temporal constraints: application to 4D cardiac image segmentation. Med Image Anal 9(1): 87–100

    PubMed  Article  Google Scholar 

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

    PubMed  Article  Google Scholar 

  16. Mitchell S, Lelieveldt B, van der Geest R, Bosch J, Reiber J, Sonka M (2001) Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Trans Med Imaging 20(5): 415–423

    PubMed  Article  CAS  Google Scholar 

  17. Mitchell S, Bosch J, Lelieveldt B, van der Geest R, Reiber J, Sonka M (2002) 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE Trans Med Imaging 21(9): 1167–1178

    PubMed  Article  Google Scholar 

  18. van Assen HC, Danilouchkine M, Frangi A, Ordas S, Westenberg J, Reiber JHC, Lelieveldt BPF (2006) SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal 10(2): 286–303

    PubMed  Article  Google Scholar 

  19. Abi-Nahed J, Jolly M-P, Yang G-Z (2006) Robust active shape models: A robust, generic and simple automatic segmentation tool. In: Proceedings of medical image computing and computer-assisted intervention (MICCAI), no 2, pp 1–8

  20. Andreopoulos A, Tsotsos JK (2008) Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Med Image Anal 12(3): 335–357

    PubMed  Article  Google Scholar 

  21. Lorenzo-Valdes M, Sanchez-Ortiz G, Elkington A, Mohiaddin R, Rueckert D (2004) Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Med Image Anal 8(3): 255–265

    PubMed  Article  Google Scholar 

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

    PubMed  Article  Google Scholar 

  23. Zhang H, Wahle A, Johnson RK, Scholz TD, Sonka M (2010) 4-D cardiac MR image analysis: left and right ventricular morphology and function. IEEE Trans Med Imaging 29(2): 350–364

    PubMed  Article  Google Scholar 

  24. Higgins CB, de Roos A (2006) MRI and CT of the cardiovascular system. Lippincott Williams & Wilkins, Philadelphia, USA

    Google Scholar 

  25. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Med Imaging 10(2): 266–277

    CAS  Google Scholar 

  26. Pluempitiwiriyawej C, Moura J, Wu Y, Ho C (2005) STACS: new active contour scheme for cardiac MR image segmentation. IEEE Trans Med Imaging 24(5): 593–603

    PubMed  Article  Google Scholar 

  27. Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulation. J Comput Phys 79: 12–49

    Article  Google Scholar 

  28. Kedenburg G, Cocosco C, Köthe U, Niessen W, Vonken E, Viergever M (2006) Automatic cardiac MRI myocardium segmentation using graphcut In: Proceedings of SPIE, number 6144 in Medical Imaging

  29. Qian X, Tagare HD, Tao Z (2006) Segmentation of Rat Cardiac Ultrasound Images With Large dropout Regions. In: Proceedings of IEEE computer society workshop on mathematical methods in biomedical image analysis (MMBIA)

  30. Lynch M, Ghita O, Whelan P (2008) Segmentation of the left ventricle of the heart in 3D+t MRI data using an optimised non-rigid temporal model. IEEE Trans Med Imaging 27(2): 195–203

    PubMed  Article  Google Scholar 

  31. Storvik G (1994) A Bayesian approach to dynamic contours through stochastic sampling and simulated annealing. IEEE Trans PAMI 16(10): 976–986

    Article  Google Scholar 

  32. Li H, Yezzi A (2007) Local or global minima: flexible dual-front active contours. IEEE Trans PAMI 29(1): 1–14

    Article  Google Scholar 

  33. Sundaramoorthi G, Yezzi A, Mennucci AC (2008) Coarse-to-fine segmentation and tracking using Sobolev active contours. IEEE Trans PAMI 30(5): 851–864

    Article  Google Scholar 

  34. Keriven R (1997) Partial differential equations, curves and surface evolutions and scale-spaces in computer vision. PhD thesis Ecole des Ponts ParisTech, Paris, France

  35. Xia Q, Wang MY, Wang S, Chen S (2006) Semi-Lagrange method for level-set-based structural topology and shape optimization. Struct Multidiscip Optim 31(6)

  36. Osher S, Fedkiw RP (2003) Level set methods and dynamic implicit surfaces. Springer, Berlin

    Google Scholar 

  37. Sussman M, Fatemi E, Smereka P, Osher S (1997) An improved level set method for incompressible two-phase flows. Comput Fluids 27(5–6): 663–680

    Google Scholar 

  38. Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. IEEE Int Conf Comput Vis Pattern Recognit (CVPR) San Diego 1: 430–436

    Article  Google Scholar 

  39. Adalsteinsson D, Sethian JA (1995) A fast level set method for propagating interfaces. J Comput Phys 118(2): 269–277

    Article  Google Scholar 

  40. Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, Pennell DJ, Rumberger JA, Ryan T, Verani MS (2002) Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation 105(4): 539–542

    PubMed  Article  Google Scholar 

  41. Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D (2008) Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging 27(11): 1668–1681

    PubMed  Article  Google Scholar 

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Affiliations

  1. Université de Rouen, LITIS EA 4108, BP 12, 76801, Saint-Etienne-du-Rouvray, France

    Damien Grosgeorge & Caroline Petitjean

  2. Department of Radiology, University Hospital of Rouen, 76031, Rouen, France

    Jérôme Caudron, Jeannette Fares & Jean-Nicolas Dacher

Authors
  1. Damien Grosgeorge
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  2. Caroline Petitjean
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  3. Jérôme Caudron
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  4. Jeannette Fares
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  5. Jean-Nicolas Dacher
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Corresponding author

Correspondence to Caroline Petitjean.

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Grosgeorge, D., Petitjean, C., Caudron, J. et al. Automatic cardiac ventricle segmentation in MR images: a validation study. Int J CARS 6, 573–581 (2011). https://doi.org/10.1007/s11548-010-0532-6

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  • DOI: https://doi.org/10.1007/s11548-010-0532-6

Keywords

  • Cardiac magnetic resonance imaging (CMRI)
  • Image segmentation
  • Ventricle segmentation
  • Validation
  • Active contours