A Multiple Decoder Cnn For Inverse Consistent 3d Image Registration
Abdullah Nazib, Clinton Fookes, Olivier Salvado, Dimitri Perrin · 2021
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.