A Multiple Decoder Cnn For Inverse Consistent 3d Image Registration

The application of deep learning approaches in medical image registration has decreased the registration time and increased registration accuracy. Most of the learning-based registration approaches considers this task as a one directional problem. As a result, only correspondence from the moving image to the target image is considered. However, in some medical procedures bidirectional registration is required. Here, we propose a registration framework with inverse consistency. The proposed method learns in an unsupervised manner a bidirectional transformation that approximates a diffeomorphism. We perform training and testing of the method on the publicly available LPBA40 MRI dataset and demonstrate its strong performance.