A diffeomorphic unsupervised method for deformable soft tissue image registration
Background and Objectives: The image registration methods for deformable soft tissues utilize nonlinear transformations to align a pair of images precisely. In some situations, when there is huge gray scale difference or large deformation between the images to be registered, the deformation field tends to fold at some local voxels, which will result in the breakdown of the one-to-one mapping between images and the reduction of invertibility of the deformation field. In order to address this issue, a novel registration approach based on unsupervised learning is presented for deformable soft tissue image registration. Methods: A novel unsupervised learning based registration approach, which consists of a registration network, a velocity field integration module and a grid sampling module, is presented for deformable soft tissue image registration. The main contributions are: (1) A novel encoder–decoder network is presented for the evaluation of stationary velocity field. (2) A Jacobian determinant based penalty term (Jacobian loss) is developed to reduce the folding voxels and to improve the invertibility of the deformation field. Results and Conclusions: The experimental results show that a new pair of images can be accurately registered using the trained registration model. In comparison with the conventional state-of-the-art method, SyN, the invertibility of the deformation field, accuracy and speed are all improved. Compared with the deep learning based method, VoxelMorph, the proposed method improves the invertibility of the deformation field.
|Keywords||Deformable soft tissue image registration, Encoder–decoder network, Invertibility, Jacobian loss, Unsupervised learning|
|Journal||Computers in Biology and Medicine|
Zhang, S. (Shuo), Liu, P, Zheng, M. (Minhua), & Shi, W. (Wen). (2020). A diffeomorphic unsupervised method for deformable soft tissue image registration. Computers in Biology and Medicine, 120. doi:10.1016/j.compbiomed.2020.103708