Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Segmentation of Left Ventricle with a Coupled Length Regularization and Sparse Composite Shape Prior: a Variational Approach

1 Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
2 Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA

Special Issues: Data integration and high dimensional statistics in Medical Imaging

Segmentation of left ventricles in Cine MR images plays an important role in analyzing cardiac functions. In this study, we propose a variational method that incorporates both prior knowledge on geometrical coupling and shapes of the endo- and epicardium. Specifically, we dynamically maintain and update a smoothly varying distance between the endo- and epicardial contours, represented by a pair of level set functions, with a novel coupling energy embedded in the length regularization. We encode the shape prior with a sparse composite model based on a set of training templates. A robust fidelity with Gaussian mixture models is employed to provide robust intensity estimates in each subregion under insufficient local gradient information. Quantitative evaluation of the proposed method demonstrates competitive/better DSC and APD accuracy compared to other state-of-the-art approaches.
  Figure/Table
  Supplementary
  Article Metrics

References

1. Lelieveldt B, Geest R, Lamb H, et al. (2001) Automated observer-independent acquisition of cardiac short-axis MR images: A pilot study 1. Radiology 221: 537–542.

2. Lynch M, Ghita O, Whelan PF (2006) Left-ventricle myocardium segmentation using a coupled level-set with a priori knowledge. Comput Mem Imag Grap 30: 255–262.

3. Kohlberger T, Funka-Lea G, Desh V (2007) Soft level set coupling for LV segmentation in gated perfusion SPECT. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 4791: 327–334.

4. Feng C, Li C, Zhao D, et al. (2013) Segmentation of the left ventricle using distance regularized two-layer level set approach. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 8149: 477–484.

5. Tsai A, Yezzi A, Wells W, et al. (2003) A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging 22: 137–154.

6. Cremer D, Osher S, Soatto S (2006) Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. Int J Comput Vision 69: 335–351.    

7. Liu W, Ruan D (2014) Segmentation with a shape dictionary. In Biomedical Imaging (ISBI), IEEE International Symposium on: 357–360.

8. Wang L, Li C, Sun Q, et al. (2008) Brain MR image segmentation using local and global intensity fitting active contours/surfaces. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 5241: 384–392.

9. Verma N, Muralidhar GS, Bovik AC, et al. (2011) Model-driven, probabilistic level set based segmentation of magnetic resonance images of the brain. In Engineering in Medicine and Biology Society (EMBC), IEEE Annual International Conference on: 2821–2824.

10. Li C, Xu C, Gui C, et al. (2005) Level set evolution without re-initialization: a new variational formulation. In Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society Conference on 1: 430–436.

11. Osher S, Fedkiw R (2003) Level set methods and dynamic implicit surfaces. Springer.

12. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B (Methodological) 39: 1–38.

13. Boyd S, Parikh N, Chu E, et al. (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning 3: 1– 122.

14. Jolly MP, Xue H, Grady L, et al. (2009) Combining registration and minimum surfaces for the segmentation of the left ventricle in cardiac Cine MR images. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) 5762: 910–918.

15. Constantinides C, Chenoune Y, Kachenoura N, et al. (2009) Semi-automated cardiac segmentation on Cine magnetic resonance images using GVF-snake deformable models. The MIDAS Journal-Cardiac MR Left Ventricle Segmentation Challenge.

16. Folkesson J, Samset E, Kwong R, et al. (2008) Unifying statistical classification and geodesic active regions for segmentation of cardiac MRI. IEEE Trans Inf Technol Biomed 12: 328–334.    

Copyright Info: © 2015, Wenyang Liu, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved