Real-time Tumor Tracking with Respiratory Motion Based on Short-term Prediction

DOI

10.2991/caai-17.2017.112How to use a DOI?

Keywords

learning-based; tumor tracking; surrogate signals; respiratory motion.

Abstract

The purpose of this study is to design a 3D navigation strategy with 2D ultrasonic images, with the assumption that the internal target trajectory could be evaluated with extern surrogate signals. This paper first proposes a simple 3D navigation strategy and then designs a fast tumor tracking system that exploits learning based methods, which learns the mapping relation between the external surrogate signals and the internal tumor trajectory. This paper uses our own retrospective clinical data to test the developed system. The experimental results show that this system has the potential to implementing high accuracy tumor tracking and navigation.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tian Qiao
AU  - Yixu Song
AU  - Chao Ren
PY  - 2017/06
DA  - 2017/06
TI  - Real-time Tumor Tracking with Respiratory Motion Based on Short-term Prediction
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 499
EP  - 503
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.112
DO  - 10.2991/caai-17.2017.112
ID  - Qiao2017/06
ER  -