Dose-Conditioned Synthesis of Radiotherapy Dose With Auxiliary Classifier Generative Adversarial Network

Abstract

In recent years, there are more and more researches on automatic radiotherapy planning based on artificial intelligence technology. Most of the work focuses on the dose prediction of radiotherapy planning, that is, the generation of radiation dose distribution image. Because of the small sample nature of radiotherapy planning data, it is difficult to obtain large-scale training data sets. In this paper, we propose a model of Dose-Conditioned Synthesis of Radiotherapy dose by using Auxiliary Classifier Generative Adversarial Network(ACGAN), and a method of customize and synthesis dose distribution images of specific tumor types and beam types is considered. This method can customize and generate dose distribution images of tumor types and beam types. The dose distribution images generated by our model are evaluated by MS-SSIM and PSNR, the results show that the image quality of dose distribution generated by ACGAN model was excellent, which was very close to the real data and shows high diversity, it can be used for data enhancement work of training data sets of dose prediction methods.

Keywords

• Radiotherapy IMRT dose distribution autoplanning ACGAN data enhancement