Deep Groupwise Registration of MRI Using Deforming Autoencoders

Groupwise image registration and the estimation of anatomical shape variation play an important role for dealing with the analysis of large medical image datasets. In this work we adapt the concept of deforming autoencoders that decouples shape and appearance in an unsupervised learning setting, following a deformable template paradigm, and apply its capability for groupwise image alignment. We implement and evaluate this model for the application on medical image data and show its suitability for this domain by training it on middle slice MRI brain scans. Anatomical shape and appearance variation can be modeled by means of splitting a low-dimensional latent code into two parts that serve as inputs for separate appearance and shape decoder networks. We demonstrate the potential of deforming autoencoders to learn meaningful appearance and deformation representations of medical image data.