A deep learning-based method for predicting volumes of nasopharyngeal carcinoma for adaptive radiation therapy treatment

Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

This paper presents a new system for predicting the spatial change of Nasopharyngeal carcinoma(NPC) and organ-at-risks (OARs) volumes over the course of the radiation therapy (RT) treatment for facilitating the workflow of adaptive radiotherapy. The proposed system, called” Tumor Evolution Prediction (TEP-Net)”, predicts the spatial distributions of NPC and 5 OARs, separately, in response to RT in the coming week, week n. Here, TEP-Net has (n-1)-inputs that are week 1 to week n-1 of CT axial, coronal or sagittal images acquired once the patient complete the planned RT treatment of the corresponding week. As a result, three predicted results of each target region are obtained from the three-view CT images. To determine the final prediction of NPC and 5 OARs, two integration methods, weighted fully connected layers and weighted voting methods, are introduced. From the experiments using weekly CT images of 140 NPC patients, our proposed system achieves the best performance for predicting NPC and OARs compared with conventional methods.

本文言語英語
ホスト出版物のタイトルProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3256-3263
ページ数8
ISBN(電子版)9781728188089
DOI
出版ステータス出版済み - 2020
イベント25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, イタリア
継続期間: 1 10 20211 15 2021

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
ISSN(印刷版)1051-4651

会議

会議25th International Conference on Pattern Recognition, ICPR 2020
国/地域イタリア
CityVirtual, Milan
Period1/10/211/15/21

All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識

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