Unsupervised Three-Dimensional Image Registration Using a Cycle Convolutional Neural Network
Ziwei Lu, Jean-Louis Coatrieux, Guanyu Yang, Tiancong Hua, Liyu Hu, Youyong Kong, Lijun Tang, Xiaomei Zhu, Jean‐Louis Dillenseger, Huazhong Shu · 2019
In this paper, an unsupervised cycle image registration convolutional neural network named CIRNet is developed for 3D medical image registration. Different from most deep learning based registration methods that require known spatial transforms, our proposed method is trained in an unsupervised way and predicts the dense displacement vector field. The CIRNet is composed by two image registration modules which have the same architecture and share the parameters. A cycle identical loss is designed in the CIRNet to provide additional constraints to ensure the accuracy of the predicted dense displacement vector field. The method is evaluated by the registration in 4D (3D+t) cardiac CT and MRI images respectively. Quantitative evaluation results demonstrate that our method performs better than the other two existing image registration algorithms. Especially, compared to the traditional image registration methods, our proposed network can finish 3D image registration in less than one second.