Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies

Julia Andresen, Timo Kepp, Jan Ehrhardt, Claus von der Burchard, Johann Roider, Heinz Handels

Abstract: The registration of medical images often suffers from missing correspondences due to inter-patient variations, pathologies and their progression leading to implausible deformations that cause misregistrations and might eliminate valuable information. Detecting non-corresponding regions simultaneously with the registration process helps generating better deformations and has been investigated thoroughly with classical iterative frameworks but rarely with deep learning-based methods.