A 3D U-Net based two stage deep learning framework for predicting dose distributions in radiation treatment planning

Automation of the various steps of radiotherapy is gaining importance nowadays. Predicting the amount of radiation dose received by the tumor and nearby organs is one among them. Many deep learning architectures that predict the 3D dose distribution images from corresponding CT and contour images have been proposed in the literature. However, a detailed investigation has yet to be done on the significance of input images (CT and contour) in predicting dose distributions. This study introduces a novel two-stage deep learning framework using a transfer learning technique for the same domain. The main objective of this approach is to accurately extract valuable information from the CT and contour images to determine the amount of radiation dose received by the organs. Training and testing are performed on the public dataset-OpenKBP, consisting of 340 oropharyngeal cancer patient data. The model performance is evaluated using the metrics dose score and DVH score. The proposed model outperforms the single-stage deep learning models by 0.34% for the DVH score and 0.14% for the dose score. While comparing the mean dose difference between the predicted and actual dose values for each organ, the proposed model shows better performance in almost all cases. The results imply that medical professionals can utilize the predicted dose distributions to aid the optimization process in the treatment planning systems.