This is the implementation of our paper "Deep learning-based Head and Neck Radiotherapy Planning Dose Prediction via Beam-wise Dose Decomposition" published in MICCAI 2022.
Our extended journal paper, "Beam-wise dose composition learning for head and neck cancer dose prediction in radiotherapy", has already been published on Medical Image Analysis, 2024. More detailed Code has been released with this paper.
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.
- Visualization of our results and some comparisons with other methods.
- Our algorithm has been evaluated on the public Dataset OpenKBP and we achieve the state-of-the-art quantitative results as follows:
| Dose score | DVH score |
|---|---|
| 2.276 | 1.257 |
@inproceedings{wang2022deep,
title={Deep Learning-Based Head and Neck Radiotherapy Planning Dose Prediction via Beam-Wise Dose Decomposition},
author={Wang, Bin and Teng, Lin and Mei, Lanzhuju and Cui, Zhiming and Xu, Xuanang and Feng, Qianjin and Shen, Dinggang},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={575--584},
year={2022},
organization={Springer}
}
@article{teng2024beam,
title={Beam-wise dose composition learning for head and neck cancer dose prediction in radiotherapy},
author={Teng, Lin and Wang, Bin and Xu, Xuanang and Zhang, Jiadong and Mei, Lanzhuju and Feng, Qianjin and Shen, Dinggang},
journal={Medical Image Analysis},
volume={92},
pages={103045},
year={2024},
publisher={Elsevier}
}
You are welcome to contact us:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent