Temporal Prediction of Respiratory Motion Using a Trained Ensemble of Forecasting Methods

  • Xiaoran Chen
  • Christine Tanner
  • Orçun Göksel
  • Gábor Székely
  • Valeria De Luca
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9805)

Abstract

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.

Keywords

Image-guided therapy Temporal prediction Respiratory motion Neural networks Regression 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiaoran Chen
    • 1
  • Christine Tanner
    • 1
  • Orçun Göksel
    • 1
  • Gábor Székely
    • 1
  • Valeria De Luca
    • 1
  1. 1.Computer Vision Lab, ETH ZurichZurichSwitzerland

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