2021年
A Deep Learning-Based Method for Predicting Volumes of Nasopharyngeal Carcinoma for Adaptive Radiation Therapy Treatment
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
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- 開始ページ
- 3256
- 終了ページ
- 3263
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICPR48806.2021.9412924
- 出版者・発行元
- IEEE COMPUTER SOC
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.
- リンク情報
- ID情報
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- DOI : 10.1109/ICPR48806.2021.9412924
- ISSN : 1051-4651
- Web of Science ID : WOS:000678409203049