Publications

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication


Year(s) from:  to 
Author:
Keywords (separated by spaces):

A Learning-based Approach for Fast and Robust Vessel Tracking in Long Ultrasound Sequences

Valeria De Luca, Michael Tschannen, Gabor Székely, and Christine Tanner
International Conference on Medical Image Computing and Computer-Assisted Intervention
2013

Abstract

We propose a learning-based method for robust tracking in long ultrasound sequences for image guidance applications. The framework is based on a scale-adaptive block-matching and temporal realignment driven by the image appearance learned from an initial training phase. The latter is introduced to avoid error accumulation over long sequences. The vessel tracking performance is assessed on long 2D ultrasound sequences of the liver of 9 volunteers under free breathing. We achieve a mean tracking accuracy of 0.96 mm. Without learning, the error increases signi cantly (2.19 mm, p<0.001).


Download in pdf format
@InProceedings{eth_biwi_01031,
  author = {Valeria De Luca and Michael Tschannen and Gabor Székely and and Christine Tanner},
  title = {A Learning-based Approach for Fast and Robust Vessel Tracking in Long Ultrasound Sequences},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year = {2013},
  volume = {518-525},
  series = {LNCS},
  publisher = {Springer},
  keywords = {tracking, block-matching, learning, real-time, ultrasound}
}