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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3256-3263
Number of pages8
ISBN (Electronic)9781728188089
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: Jan 10 2021Jan 15 2021

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
Country/TerritoryItaly
CityVirtual, Milan
Period1/10/211/15/21

Keywords

  • ART
  • CNN
  • CT images
  • NPC

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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