Confidence-Based Training for Clinical Data Uncertainty in Image-Based Prediction of Cardiac Ablation Targets

@inproceedings{Lozoya2014ConfidenceBasedTF,
  title={Confidence-Based Training for Clinical Data Uncertainty in Image-Based Prediction of Cardiac Ablation Targets},
  author={Roc{\'i}o Cabrera Lozoya and J{\'a}n Margeta and Lo{\"i}c Le Folgoc and Yuki Komatsu and Benjamin Berte and Jatin Relan and Hubert Cochet and Michel Ha{\"i}ssaguerre and Pierre Ja{\"i}s and Nicholas Ayache and Maxime Sermesant},
  booktitle={MCV},
  year={2014}
}
Ventricular radio-frequency ablation (RFA) can have a critical impact on preventing sudden cardiac arrest but is challenging due to a highly complex arrhythmogenic substrate. This work aims to identify local image characteristics capable of predicting the presence of local abnormal ventricular activities (LAVA). This can allow, pre-operatively and non-invasively, to improve and accelerate the procedure. To achieve this, intensity and texture-based local image features are computed and random… CONTINUE READING

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
0 Extracted Citations
10 Extracted References
Similar Papers

Referenced Papers

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

3D Extension of Haralick Texture Features for Medical Image Analysis

  • T Ludvik, D Smutek, A Shimizu, H. Kobatake
  • In Proceedings of the Fourth IASTED International…
  • 2007
1 Excerpt

Similar Papers

Loading similar papers…