A time variant seasonal ARIMA model for lung tumor motion prediction

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We propose a prediction method of lung tumor motion for real-time tumor following radiation therapy. An essential core of the method is a model building of time variant nature of the lung tumor motion. The method is based on a seasonal ARIMA model with an estimator of the time variant nature. The estimator provides the time variant period of the lung tumor motion by using a correlation analysis. The time variant SARIMA model can then predict complex lung motion by using the estimated period. The proposed method achieved highly accurate prediction of the average error 0.820±0.669[mm] at 0.5[sec] ahead prediction. This result is superior to other conventional methods at short- or mid-term prediction.

本文言語English
ホスト出版物のタイトルProceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
ページ485-488
ページ数4
出版ステータスPublished - 2010
イベント15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita, Japan
継続期間: 2010 2 42010 2 6

出版物シリーズ

名前Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10

Other

Other15th International Symposium on Artificial Life and Robotics, AROB '10
国/地域Japan
CityBeppu, Oita
Period10/2/410/2/6

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 人間とコンピュータの相互作用

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