Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and Inter-Relation Modeling
This work addresses the need for full automation in radiotherapy planning by enabling direct parameter derivation for treatment systems, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of automating radiotherapy by directly regressing radiotherapy parameters from medical images, using a two-stage framework that predicts dose maps and then derives parameters, achieving effectiveness on a rectal cancer dataset.
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.