Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data

@article{Carneiro2013CombiningMD,
  title={Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data},
  author={Gustavo Carneiro and Jacinto C. Nascimento},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2013},
  volume={35},
  pages={2592-2607}
}
We present a new statistical pattern recognition approach for the problem of left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the current time step is estimated based on the appearance, shape, and motion models that take into account all previous and current images and previous segmentation contours produced by the method. The new appearance and shape models decouple the… CONTINUE READING
Recent Discussions
This paper has been referenced on Twitter 3 times over the past 90 days. VIEW TWEETS
31 Citations
72 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 31 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 72 references

A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking

  • M. Arulampalam, S. Maskell, N. Gordon, T. Clapp
  • IEEE Trans. Signal Processing, vol. 50, no. 2, pp…
  • 2002
Highly Influential
3 Excerpts

Computer - assited endocardial border identification from a sequence of two - dimensional echocardiographic images

  • A. Hammoude
  • Image Processing , IEEE Transactions on
  • 2012

Similar Papers

Loading similar papers…