Deep Learning-Based Head and Neck Radiotherapy Planning Dose Prediction via Beam-Wise Dose Decomposition
2022
会议录名称LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
ISSN0302-9743
卷号13437 LNCS
页码575-584
发表状态已发表
DOI10.1007/978-3-031-16449-1_55
摘要

Accurate dose map prediction is key to external radiotherapy. Previous methods have achieved promising results; however, most of these methods learn the dose map as a black box without considering the beam-shaped radiation for treatment delivery in clinical practice. The accuracy is usually limited, especially on beam paths. To address this problem, this paper describes a novel "disassembling-then-assembling" strategy to consider the dose prediction task from the nature of radiotherapy. Specifically, a global-to-beam network is designed to first predict dose values of the whole image space and then utilize the proposed innovative beam masks to decompose the dose map into multiple beam-based sub-fractions in a beam-wise manner. This can disassemble the difficult task to a few easy-to-learn tasks. Furthermore, to better capture the dose distribution in region-of-interest (ROI), we introduce two novel value-based and criteria-based dose volume histogram (DVH) losses to supervise the framework. Experimental results on the public OpenKBP challenge dataset show that our method outperforms the state-of-the-art methods, especially on beam paths, creating a trustable and interpretable AI solution for radiotherapy treatment planning. Our code is available at https://github.com/ukaukaaaa/BeamDosePrediction. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词Deep learning Graphic methods Image segmentation Medical imaging Radiotherapy Beam mask Beam path Black boxes Dose prediction Dose-volume histograms External radiotherapy Head and neck Head-and-neck cancer Learn+ Radiotherapy planning
会议名称25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
会议地点Singapore, Singapore
会议日期September 18, 2022 - September 22, 2022
收录类别EI ; CPCI ; CPCI-S
语种英语
出版者Springer Science and Business Media Deutschland GmbH
EI入藏号20224012829506
EI主题词Forecasting
EISSN1611-3349
EI分类号461.1 Biomedical Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 461.6 Medicine and Pharmacology ; 622.3 Radioactive Material Applications ; 746 Imaging Techniques
原始文献类型Conference article (CA)
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文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/240504
专题生物医学工程学院_PI研究组_崔智铭组
上海科技大学
信息科学与技术学院_硕士生
信息科学与技术学院_本科生
生物医学工程学院_PI研究组_沈定刚组
通讯作者Feng, Qianjin; Shen, Dinggang
作者单位
1.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China;
2.School of Information Science and Technology, ShanghaiTech University, Shanghai, China;
3.School of Biomedical Engineering, Southern Medical University, Guangzhou, China;
4.Department of Computer Science, The University of Hong Kong, Hong Kong;
5.Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, United States;
6.Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
第一作者单位生物医学工程学院;  信息科学与技术学院
通讯作者单位生物医学工程学院
第一作者的第一单位生物医学工程学院
推荐引用方式
GB/T 7714
Wang, Bin,Teng, Lin,Mei, Lanzhuju,et al. Deep Learning-Based Head and Neck Radiotherapy Planning Dose Prediction via Beam-Wise Dose Decomposition[C]:Springer Science and Business Media Deutschland GmbH,2022:575-584.
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