Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and Inter-Relation Modeling
<|reference_start|>Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and Inter-Relation Modeling: Deep learning has facilitated the automation of radiotherapy by predicting accurate dose distribution maps. However, existing methods fail to derive the desirable radiotherapy parameters that can be directly input into the treatment planning system (TPS), impeding the full automation of radiotherapy. To enable more thorough automatic radiotherapy, in this paper, we propose a novel two-stage framework to directly regress the radiotherapy parameters, including a dose map prediction stage and a radiotherapy parameters regression stage. In stage one, we combine transformer and convolutional neural network (CNN) to predict realistic dose maps with rich global and local information, providing accurate dosimetric knowledge for the subsequent parameters regression. In stage two, two elaborate modules, i.e., an intra-relation modeling (Intra-RM) module and an inter-relation modeling (Inter-RM) module, are designed to exploit the organ-specific and organ-shared features for precise parameters regression. Experimental results on a rectal cancer dataset demonstrate the effectiveness of our method.<|reference_end|>
@article{cui2024dose,
    title={Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and
  Inter-Relation Modeling},
    author={Jiaqi Cui, Yuanyuan Xu, Jianghong Xiao, Yuchen Fei, Jiliu Zhou,
  Xingcheng Peng, Yan Wang},
    journal={arXiv preprint arXiv:2402.18879},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18879},
    primaryClass={cs.CV}
}