Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and
  Inter-Relation Modeling
Jiaqi Cui Yuanyuan Xu Jianghong Xiao Yuchen Fei Jiliu Zhou Xingcheng Peng Yan Wang
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.