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 
Keywords (separated by spaces):

Temporal prediction of respiratory motion using a trained ensemble of forecasting methods

X. Chen, C. Tanner, O. Goksel, G. Székely, V. De Luca
Medical Imaging and Augmented Reality (MIAR)


Respiratory motion is a limiting factor during cancer therapy. Although image tracking can facilitate compensation for this motion, system latencies will still reduce the accuracy of tracking-based treatments. We propose a novel approach for temporal prediction of the motion of anatomical targets in the liver, observed from ultrasound sequences. The method is based on an ensemble of six prediction models, including neural networks, which are trained on motion traces and images. Using leave-one-subject-out validation on 24 liver ultrasound 2D sequences from the Challenge on Liver Ultrasound Tracking, the best performance was achieved by the linear regression-based ensemble of all methods with an accuracy of 1.49 (2.39) mm for a latency of 300 (600) ms.

Link to publisher's page
  author = {X. Chen and C. Tanner and O. Goksel and G. Székely and V. De Luca},
  title = {Temporal prediction of respiratory motion using a trained ensemble of forecasting methods},
  booktitle = {Medical Imaging and Augmented Reality (MIAR)},
  year = {2016},
  pages = {383-391},
  keywords = {}