ShanghaiTech University Knowledge Management System
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)
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ISSN | 0302-9743 |
卷号 | 13437 LNCS |
页码 | 575-584 |
发表状态 | 已发表 |
DOI | 10.1007/978-3-031-16449-1_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 |
EISSN | 1611-3349 |
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) |
引用统计 | 正在获取...
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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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