Abstract
This paper proposes a convexity-preserving level set (CPLS) and a novel modeling of the heart named Convex Shape Decomposition (CSD) for segmentation of Left Ventricle (LV) and Right Ventricle (RV) from cardiac magnetic resonance images. The main contributions are two-fold. First, we introduce a convexity preserving mechanism in the level set framework, which is helpful for overcoming the difficulties arised from the overlap between intensities of papillary muscles and trabeculae and intensities of myocardium. Furthermore, such a generally contrained convexity-preserving level set method can be useful in many other potential applications. Second, by decomposing the heart into two convex structures, and essentially converting RV segmentation into LV segmentation, we can solve both LV and RV segmentation in a unified framework without training any specific shape models for RV. The proposed method has been quantitatively validated on open datasets, and the experimental results and comparisons with other methods demonstrate the superior performance of our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Avendi, M.R., Kheradvar, A., Jafarkhani, H.: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 30, 108–119 (2016)
Bai, W., Shi, W., Ledig, C., Rueckert, D.: Multi-atlas segmentation with augmented features for cardiac MR images. Med. Image Anal. 19(1), 98–109 (2015)
Bai, W., et al.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE Trans. Med. Imaging 32(7), 1302–1315 (2013)
ElBaz, M.S., Fahmy, A.S.: Active shape model with inter-profile modeling paradigm for cardiac right ventricle segmentation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 691–698. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_85
Gorelick, L., Veksler, O., Boykov, Y., Nieuwenhuis, C.: Convexity shape prior for segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 675–690. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_44
Grosgeorge, D., Petitjean, C., Dacher, J.N., Ruan, S.: Graph cut segmentation with a statistical shape model in cardiac MRI. Comput. Vis. Image Und. 117(9), 1027–1035 (2013)
Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)
Liu, Y., et al.: Distance regularized two level sets for segmentation of left and right ventricles from CINE-MRI. Magn. Reson. Imaging 34(5), 699–706 (2016)
Maier, O.M.O., Jimnez, D., Santos, A., Ledesma-Carbayo, M.J.: Segmentation of RV in 4D cardiac MR volumes using region-merging graph cuts. In: Proceedings of the IEEE CinC, pp. 697–700 (2012)
Moolan-Feroze, O., Mirmehdi, M., Hamilton, Markand Bucciarelli-Ducci, C.: Segmentation of the right ventricle using diffusion maps and markov random fields. In: Proceedings of the MICCAI, pp. 682–689 (2014)
Nambakhsh, C.M.S., et al.: Left ventricle segmentation in MRI via convex relaxed distribution matching. Med. Image Anal. 17(8), 1010–1024 (2013)
Ngo, T.A., Carneiro, G.: Fully automated non-rigid segmentation with distance regularized level set evolution initialized and constrained by deep-structured inference. In: Proceedings of the CVPR, pp. 3118–3125 (2014)
Park, E.A., Lee, W., Kim, H.K., Chung, J.W.: Effect of papillary muscles and trabeculae on left ventricular measurement using cardiovascular magnetic resonance imaging in patients with hypertrophic cardiomyopathy. Korean J. Radiol. 16(1), 4–12 (2015)
Petitjean, C., et al.: Right ventricle segmentation from cardiac MRI: a collation study. Med. Image Anal. 19(1), 187–202 (2015)
Qi, J., et al.: Drosophila eye nuclei segmentation based on graph cut and convex shape prior. In: Proceedings of the ICIP, pp. 670–674 (2013)
Qin, X., Cong, Z., Fei, B.: Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set. Phys. Med. Biol. 58(21), 7609 (2013)
Queirs, S.: Fast automatic myocardial segmentation in 4D cine CMR datasets. Med. Image Anal. 18(7), 1115–1131 (2014)
Ringenberg, J., Deo, M., Devabhaktuni, V., Berenfeld, O., Boyers, P., Gold, J.: Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI. Comput. Med. Imaging Graph. 38(3), 190–201 (2014)
Yang, C., Shi, X., Yao, D., Li, C.: A level set method for convexity preserving segmentation of cardiac left ventricle. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2159–2163. IEEE (2017)
Zhu, L., et al.: Automatic delineation of the myocardial wall from CT images via shape segmentation and variational region growing. IEEE Trans. Bio-med. Eng. 60(10), 2887–2895 (2013)
Zuluaga, M.A., Cardoso, M. Jorgeand Modat, M.O.S.: Multi-atlas propagation whole heart segmentation from MRI and CTA using a local normalised correlation coefficient criterion. In: Proceedings of the FIMH, pp. 174–181 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Shi, X., Tang, L., Zhang, S., Li, C. (2018). Heart Modeling by Convexity Preserving Segmentation and Convex Shape Decomposition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-03801-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03800-7
Online ISBN: 978-3-030-03801-4
eBook Packages: Computer ScienceComputer Science (R0)