Abstract
In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a “context penalty” that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method’s robustness to noise and inaccurate segmentations.
Similar content being viewed by others
References
Allender S: European Cardiovascular Disease Statistics. European Heart Network. 2008
A.F. Frangi, W.J. Niessen, and M.A. Viergever, “Three dimensional modeling for functional analysis of cardiac images: a review,” IEEE Trans Med. Imag, vol. 20, no. 1, pp. 2–25, 2001
C. Petitjean and J-N. Dacher, “A review of segmentation methods in short axis cardiac mr images,” Med. Imag. Anal., vol. 15, no. 2, pp. 169–184, 2011
S. Shors, C. Fung, C. Francois, P. Finn, and D. Fieno, “Accurate quantification of right ventricular mass at MR imaging by using cine true fast imaging with steady state precession: study in dogs.,” Radiology, vol. 230, no. 2, pp. 383–388, 2004
Jolly MP: Automatic recovery of the left ventricle blood pool in cardiac cine MR images. In: MICCAI, 2008, pp 110–118
N. Paragios, “A variational approach for the segmentation of the left ventricle in cardiac image analysis,” Intl. J. Comp. Vis., vol. 50, no. 3, pp. 345–362, 2002
M. Lynch, O. Ghita, and P. Whelan, “Left ventricle myocardium segmentation using a coupled level set with a-priori knowledge,” Comput. Med. Imag. Graph., vol. 30, no. 4, pp. 255–262, 2006
Lin X, Cowan B, and Young A: Model based graph cut method for segmentation of the left ventricle. In In Proc: EMBC, 2005, pp 3059–3062
Mahapatra D and Sun Y: Orientation histograms as shape priors for left ventricle segmentation using graph cuts. In Proc: MICCAI, 2011, pp 420–427
Mahapatra D: Cardiac image segmentation from cine cardiac MRI using graph cuts and shape priors. Journal of Digital Imaging. doi:10.1007/s10278-012-9548-5, 2013
J. Cousty, L. Najman, M. Couprie, S. Clment-Guinaudeau, T. Goissen, and J. Garot, “Segmentation of 4-D cardiac MRI: automated method based on spatio temporal watershed cuts.,” Image and Vis. Comput., vol. 28, no. 8, pp. 1229–1243, 2010
C. Cocosco, W. Niessen, T. Netsch, E-J. Vonken, G. Lund, A. Stork, and M. Viergever.,“Automatic image driven segmentation of the ventricles in cardiac cine MRI.,” J. magn. Reson. Imag., vol. 28, no. 2, pp. 366–374, 2008
Mahapatra D, and Sun Y: Joint registration and segmentation of dynamic cardiac perfusion images using mrfs. In In Proc: MICCAI, 2010, pp 493–501
D. Mahapatra and Y. Sun, “Integrating segmentation information for improved elastic registration of perfusion images using an mrf framework,” IEEE Trans. Imag. Proc., vol. 21, no. 1, pp. 170–183, 2012
Mahapatra D: Joint segmentation and groupwise registration of cardiac perfusion images using temporal information. Journal of Digital Imaging
D. Mahapatra: Groupwise registration of dynamic cardiac perfusion images using temporal dynamics and segmentation information", SPIE Medical Imaging 2012, SPIE Vol 8314, pp 1–7
M.R. Kaus, J. von Berg, J. Weese, W. Niessen, and V. Pekar, “Automated segmentation of the left ventricle in cardiac MRI,” Med Image Anal., vol. 8, no. 3, pp. 245–254, 2004
Zhu Y, Papademetris X, Sinusas A, and Duncan J.S: Segmentation of left ventricle from 3d cardiac mr image sequence using a subject specific dynamic model. In Proc.IEEE CVPR, 2008, pp 1–8
Sun W, Setin M, Chan R, Reddy V, Holmvang G, Ch V, and Willsky A: segmenting and tracking of the left ventricle by learning the dynamics in cardiac images. In Proc. IPMI, 2005, pp 553–565
R. H. Davies, C. J. Twining, T. F. Cootes, J. C. Waterton, and C. J. Taylor, “A minimum description length approach to statistical shape modelling,” IEEE Trans. Med. Imag., vol. 21, pp. 525–537, 2002
Perperidis D, Mohiaddin R, and Rueckert D: Construction of a 4d statistical atlas of the cardiac anatomy and its use in classification. In MICCAI, 2005, pp 402–410
Besbes A, Komodakis N, and Paragios N: Graph-based knowledge-driven discrete segmentation of the left ventricle. In IEEE ISBI, 2009, pp 49–52
S.C. Mitchell, B.P.F. Lelieveldt, R.J. van der Geest, H.G. Bosch, J.H.C Reiver, and M. Sonka, “Multistage hybrid active appearance models: segmentation of cardiac MR and ultrasound images,” IEEE Trans Med. Imag, vol. 20, no. 5, pp. 415–423, 2001
H. Zhang, A. Wahle, R. Johnson, T. Scholz, and M. Sonka, “4-D cardiac MR image analysis: left and right ventricular morphology and function.,” IEEE Trans Med. Imag, vol. 29, no. 2, pp. 350–364, 2010
Zambal S, Hladuvka J, and Buhler K: Improving segmentation of the left ventricle using a two component statistical model. In MICCAI, 2006, pp 151–158
Lelieveldt B, Mitchell S, Bosch J, van der Geest R, Sonka M, and Reiber J: Time continuous segmentation of cardiac image sequences using active appearance motion models. In IPMI, 2001, pp 446–452
C. Pluempitiwiriyawej, J.M.F. Moura, Y.L.Wu, and C. Ho, “STACS: new active contour scheme for cardiac MR image segmentation,” IEEE Trans. Med. Imag., vol. 24, no. 5,pp. 593–603, 2005
Billet F, Sermeanst M, Delingette H, and Ayache N: 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
J. Ltjnen, S. Kivist, J. Koikkalainen, D. Smutek, and K. Lauerma, “Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images,” Med Image Anal., vol. 8, no. 3, pp. 371–386, 2004
S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images.,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 6, no. 6, pp. 721–741, 1984
Kumar S and Hebert M: Discriminative random fields: a discriminative framework for contextual interaction in classification. In Proc. ICCV, 2003, pp 1150–1157
S. Belongie, J. Malik, and J. Puzicha, “Shape matching and object recognition using shape contexts,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 24, no. 24, pp. 509–522, 2002
Hoiem D, Efros AA, and Hebert M: Putting objects in perspective. In Proc. CVPR, 2006, pp 2137–2144
He X, Zemel RS, and Carreira-Perpinan MA: Multiscale conditional random fields for image labeling. In Proc. CVPR, 2004, pp 695–702
Murphy K, Torralba A and Freeman WT: Graphical model for recognizing scenes and objects. In Proc. NIPS
Z. Tu and X. Bai, “Auto-context and its application to high-level vision tasks and 3d brain image segmentation,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 32, no. 10, pp. 1744 – 1757, 2010
Li W, Liao S, Feng Q, Chen W, and Shen D: Learning image context for segmentation of prostate in ct-guided radiotherapy. In MICCAI, 2011, pp 570–578
Delong A and Boykov Y: Globally optimal segmentation of multi-region objects. In ICCV, 2009, pp 285–292
Ben Ayed I, Punithakumar K, Garvin G, Romano W, and Li S: Graph cuts withinvariant object-interaction priors: Application to intervertebral disc segmentation. In IPMI, 2011, pp 221–232
Song Q, Chen M, Bai J, Sonka M, and Wu X: Surface-region context in optimal multi-object graph based segmentation: robust delineation of pulmonary tumors. In IPMI, 2011, pp 61–72
Y. Boykov and O. Veksler, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, pp. 1222–1239, 2001
V. Chalana and Y. Kim, “A methodology for evaluation of boundary detection algorithms on medical images,” IEEE Trans. Med. Imag., vol. 16, no. 5, pp. 642–652, 1997
D.P. Huttenlocher, G.A. Klanderman, and W.J. Rucklidge, “Comparing images using the hausdorff distance,” IEEE Trans. Pattern Anal. Machine Intell., vol. 15, no. 9, pp. 850–863, 1993
C.G. Fonseca, M. Backhaus, D.A. Bluemke, R.D. Britten, J.D. Chung, B.R. Cowan, I.D. Dinov, J.P. Finn, P.J. Hunter, A.H. Kadish, D.C. Lee, J.A.C. Lima, P. Medrano-Gracia, K. Shivkumar, A. Suinesiaputra, W. Tao, and A.A. Young., “The cardiac atlas project an imaging database for computational modeling and statistical atlases of the heart.,” Bioinformatics, vol. 27, no. 16, pp. 2288–2295, 2011
A.H. Kadish, D. Bello, J.P. Finn, R.O. Bonow, A. Schaechter, H. Subacius, C. Albert, J.P. Daubert, C.G. Fonseca, and J.J. Goldberger., “Rationale and design for the defribrillators to reduce risk by magnetic resonance imaging evaluation (determine) trial.,” J. Cardiovascular Electrophysiology, vol. 20, no. 9, pp. 982–987, 2009
A. Elen, J. Hermans, J. Ganame, D. Loeckx, J. Bogaert, F. Maes, and P. Suetens., “Automatic 3-d breath-hold related motion correction of dynamic multislice mri.,” IEEE Trans. Med. Imag., vol. 29, no. 3, pp. 868–878, 2010
A.A. Young, B.R. Cowan, S.F. Thrupp, W.J. Hedley, and L.J. DellItalia., “Left ventricular mass and volume: fast calculation with guide-point modeling on mr images.,” Radiology, vol. 202, no. 2, pp. 597–602, 2000
Suinesiaputra A and et al: Left ventricular segmentation challenge from cardiac mri: a collation study. In STACOM 2011, 2011, pp 88–97
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mahapatra, D. Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle. J Digit Imaging 26, 898–908 (2013). https://doi.org/10.1007/s10278-013-9573-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10278-013-9573-z