  This article presents a general Bayesian learning framework for multi-modal
groupwise registration on medical images. The method builds on probabilistic
modelling of the image generative process, where the underlying common anatomy
and geometric variations of the observed images are explicitly disentangled as
latent variables. Thus, groupwise registration is achieved through the solution
to Bayesian inference. We propose a novel hierarchical variational
auto-encoding architecture to realize the inference procedure of the latent
variables, where the registration parameters can be calculated in a
mathematically interpretable fashion. Remarkably, this new paradigm can learn
groupwise registration in an unsupervised closed-loop self-reconstruction
process, sparing the burden of designing complex intensity-based similarity
measures. The computationally efficient disentangled architecture is also
inherently scalable and flexible, allowing for groupwise registration on
large-scale image groups with variable sizes. Furthermore, the inferred
structural representations from disentanglement learning are capable of
capturing the latent anatomy of the observations with visual semantics.
Extensive experiments were conducted to validate the proposed framework,
including four datasets from cardiac, brain and abdominal medical images. The
results have demonstrated the superiority of our method over conventional
similarity-based approaches in terms of accuracy, efficiency, scalability and
interpretability.
