Fully Automated Non-rigid Segmentation with Distance Regularized Level Set Evolution Initialized and Constrained by Deep-Structured Inference

@article{Ngo2014FullyAN,
  title={Fully Automated Non-rigid Segmentation with Distance Regularized Level Set Evolution Initialized and Constrained by Deep-Structured Inference},
  author={Tuan Anh Ngo and Gustavo Carneiro},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2014},
  pages={3118-3125}
}
We propose a new fully automated non-rigid segmentation approach based on the distance regularized level set method that is initialized and constrained by the results of a structured inference using deep belief networks. This recently proposed level-set formulation achieves reasonably accurate results in several segmentation problems, and has the advantage of eliminating periodic re-initializations during the optimization process, and as a result it avoids numerical errors. Nevertheless, when… CONTINUE READING
Highly Cited
This paper has 34 citations. REVIEW CITATIONS
17 Citations
29 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 17 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 29 references

Evaluation framework for algorithms segmenting short axis cardiac mri

  • P. Radau, Y. Lu, K. Connelly, G. Paul, A. Dick, G. Wright
  • MIDAS J.Cardiac MR Left Ventricle Segmentation…
  • 2009
Highly Influential
7 Excerpts

Fully automatic left ventricle segmentation in cardiac cine mr images using registration and minimum surfaces

  • M. Jolly
  • The MIDAS Journal, vol. 49, 2009. 2, 6, 8
  • 2009
Highly Influential
6 Excerpts

Similar Papers

Loading similar papers…