Myocardium Segmentation Based on Combining Fully Convolutional Network and Graph cut
Main Article Content
Abstract
Myocardium segmentation from cardiac MRI images is an important task in clinical diagnosis of the left ventricle (LV) function. In this paper, we proposed a new approach for myocardium segmentation based on deep neural network and Graph cut approach. The proposed method is a framework including two steps: in the first step, the fully convolutional network (FCN) was performed to obtain coarse segmentation of LV from input cardiac MR images. In the second step, Graph cut method was employed to further optimize the coarse segmentation results in order to get fine segmentation of LV. The proposed model was validated in 45 subjects of Sunnybrook database using the Dice coefficient metric and compared with other state-of-the-art approaches. Experimental results show the robustness and feasibility of the proposed method.
Keywords
Myocardium segmentation, Graph cut, Fully Convolutional network, Deep learning, Cardiac MRI segmentation
Article Details
References
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[3] M. Lynch, O. Ghita, and P. Whelan, Automatic segmentation of the left ventricle cavity and myocardium in MRI data, Comput. Biol. Medicine, vol. 36, pp. 389-407, 2006.
[4] M. Marsouzi, A. Eftekhar, A. Kocharian, and J. Alirezaie, Endocardial boundary extraction in left ventricular echocardiographic images using fast and adaptive B-spline snake algorithm, Int. J. Comput. Assist. Radiol. Surg., vol. 5, pp. 501-513, 2010.
[5] C. Petitjean and J. Dacher, ÚA review of segmentation methods in short axis cardiac MR images, Med. Image Anal., vol. 15, pp. 169-184, 2011.
[6] Y. Boykov, V. S. Lee, H. Rusinek, and R. Bansal, Segmentation of Dynamic N-D Data Sets via Graph cuts Using Markov Models, Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention, 2001, pp. 1058-1066.
[7] M. Uzunbas, S. Zhang, Pohl KM., D. Metaxas, and L. Axel, Segmentation of myocardium using deformable regions and graph cuts, Proceeding of IEEE Int Symp Biomed Imaging., pp. 254-257, 2012.
[8] I. Ben Ayed, H. Chen, K. Punithakumar, I. Ross, and S. Li, Max-flow segmentation of the left ventricle by recovering subject-specific distributions via a bound of the Bhattacharyya measure, Med Image Anal., vol. 16, pp. 87-100, 2012.
[9] V. T. Pham, T. T. Tran, K. K. Shyu, L. Y. Lin, Y. H. Wang, and M. T. Loik, Multiphase B-spline level set and incremental shape priors with applications to segmentation and tracking of left ventricle in cardiac MR images, Mach. Vis. Appl., vol. 25, pp. 1967-1987, 2014.
[10] V. T. Pham and T. T. Tran, Active contour model and nonlinear shape priors with application to left ventricle segmentation in cardiac MR images, Optik, vol. 127, pp. 991-1002, 2016.
[11] M. R. Avendi, A. Kheradvar, and H. Jafarkhani, A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI, Med. Image Anal., vol. 30, pp. 108-119, 2016.
[12] T. A. Ngo and G. Carneiro, Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks, 20th International Conference on Image Processing, pp. 695-699, 2013.
[13] D. Freedman and T. Zhang, Interactive graph cut based segmentation with shape priors., Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 755-762, 2005.
[14] G. Slabaugh and G. Unal, Graph cuts segmentation using an elliptical shape prior, IEEE International Conference on Image Processing, pp. 1222-1225, 2005.
[15] J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431-3440, 2015.
[16] G. Luo, S. Dong, K. Wang, W. Zuo, S. Cao, and H. Zhang, Multi-views fusion CNN for left ventricular volumes estimation on cardiac MR images, IEEE Transactions on Biomedical Engineering, vol. 65, pp. 1924-1934, 2018.
[17] N. Vu and B. S. Manjunath, Shape prior segmentation of multiple objects with graph cuts, in Proceedings of Computer Vision and Pattern Recognition (CVPR), Anchorage, AK 2008.
[18] Y. Boykov and V. Kolmogorov, Computing geodesics and minimal surfaces via graph cuts, in Proceedings of IEEE International Conference on Computer Vision (ICCV), Nice, France 2003, pp. 26-33.
[19] P. Radau, Lu Y., Connelly K., Paul G., Dick A.J., and W. G.A., Evaluation framework for algorithms segmenting short axis cardiac MRI, The MIDAS Journal-Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070, 2009.
[20] J. Bland and D. Altman, Statistical methods for assessing agreement between two methods of clinical measurements, Lancet, pp. 307-310, 1986.
[21] P. V. Tran, A fully convolutional neural network for cardiac segmentation in short-axis MRI, Available: https://arxiv.org/abs/1604.00494, 2016.
[22] H. Hu, Liu H., Gao Z., and H. L., Hybrid segmentation of left ventricle in cardiac MRI using gaussian-mixture model and region restricted dynamic programming, Magnetic Resonance Imaging, vol. 31, pp. 575-584, 2013.
[23] S. Queirós, D. Barbosa, B. Heyde, P. Morais, J. Vilaça, D. Friboulet, et al., Fast automatic myocardial datasets, Med Image Anal., vol. 18, pp. 1115-segmentation in 4D cine CMR 1131, 2014.