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Computational Intelligent Image Analysis for Assisting Radiation Oncologists’ Decision Making in Radiation Treatment Planning

  • Hidetaka Arimura
  • Taiki Magome
  • Genyu Kakiuchi
  • Jumpei Kuwazuru
  • Asumi Mizoguchi
Chapter

Abstract

This chapter describes the computational image analysis for assisting radiation oncologists’ decision making in radiation treatment planning for high precision radiation therapy. The radiation therapy consists of five steps, i.e., diagnosis, treatment planning, patient setup, treatment, and follow-up, in which computational intelligent image analysis and pattern recognition methods play important roles in improving the accuracy of radiation therapy and assisting radiation oncologists’ or medical physicists’ decision making. In particular, the treatment planning step is substantially important and indispensable, because the subsequent steps must be performed according to the treatment plan. This chapter introduces a number of studies on computational intelligent image analysis used for the computer-aided decision making in radiation treatment planning. Moreover, the authors also explore computer-aided treatment planning methods including automated beam arrangement based on similar cases, computerized contouring of lung tumor regions using a support vector machine (SVM) classifier, and a computerized method for determination of robust beam directions against patient setup errors in particle therapy.

Keywords

Support Vector Machine Standardize Uptake Value Positron Emission Tomography Image Planning Target Volume Stereotactic Body Radiation Therapy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors are grateful to all members of the Arimura Laboratory (http://www.shs.kyushu-u.ac.jp/~arimura), whose comments made an enormous contribution to this chapter. This research was partially supported by the Ministry of Education, Culture, Sports Science, and Technology (MEXT), Grant-in-Aid for Scientific Research (C), 22611011, 2011to 2012, and Grant-in-Aid for Scientific Research on Innovative Areas, 24103707, 2012.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Hidetaka Arimura
    • 1
  • Taiki Magome
    • 1
  • Genyu Kakiuchi
    • 1
  • Jumpei Kuwazuru
    • 1
  • Asumi Mizoguchi
    • 1
  1. 1.Division of Medical Quantum Sciences, Department of Health Sciences, Faculty of Medical SciencesKyushu UniversityFukuokaJapan

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