Deep Learning for Dose Prediction in Radiation Therapy : A comparison study of state-of-the-art U-net based architectures

Abstract: Machine learning has shown great potential as a step in automating radiotherapy treatment planning. It can be used for dose prediction and a popular deep learning architecture for this purpose is the U-net. Since it was proposed in 2015, several modifications and extensions have been proposed in the literature. In this study, three promising modifications are reviewed and implemented for dose prediction on a prostate cancer data set and compared with a 3D U-net as a baseline. The tested modifications are residual blocks, densely connected layers and attention gates. The different models are compared in terms of voxel error, conformity, homogeneity, dose spillage and clinical goals. The results show that the performance was similar in many aspects for the models. The residual blocks model performed similar or better than the baseline in almost all evaluations. The attention gates model performed very similar to the baseline and the densely connected layers were uneven in the results, often with low dose values in comparison to the baseline. The study also shows the importance of consistent ground truth data and how inconsistencies affect metrics such as isodose Dice score and Hausdorff distance.