An Unsupervised Convolution Neural Network for Deformable Registration of Mono/Multi-Modality Medical Images

Image registration is a fundamental and crucial step in medical image analysis. However, due to the differences between mono-mode and multi-mode registration tasks and the complexity of the corresponding relationship between multi-mode image intensity, the existing unsupervised methods based on deep learning can hardly achieve the two registration tasks simultaneously. In this paper, we proposed a novel approach to register both mono- and multi-mode images in a same framework. By approximately calculating the mutual information in a differentiable form and combining it with CNN, the deformation field can be predicted quickly and accurately without any prior information about the image intensity relationship. The registration process is implemented in an unsupervised manner, avoiding the need for the ground truth of the deformation field. We utilize two public datasets to evaluate the performance of the algorithm for mono-mode and multi-mode image registration, which confirms the effectiveness and feasibility of our method. In addition, the experiments on patient data also demonstrate the practicability and robustness of the proposed method.