Application of deformable registration method of medical images based on unsupervised learning in radiotherapy

Abstract

Image registration technology has important application value in Image Guide Radiation Therapy (IGRT), Deformable image registration with 2D/3D in IGRT has been developed for a long time, and although the current methods perform well, they are not accurate enough to meet the growing demand for patient care. In this paper, a fast unsupervised learning registration model based on U-net is proposed. The proposed model directly estimates the spatial transformation from float images and fixed images, and applies a regularization term to constrain the model to learn local matching. The training process does not require supervised information such as the ground truth deformation field, and the entire deformation field can be predicted at once. Through the data set, the method is compared with the existing popular registration methods in terms of accuracy, The preliminary results showed that for approximately 96% cases, the learning-based method could obtain improved or comparable registration in comparison with the baseline method.