  Multimodal groupwise registration aligns internal structures in a group of
medical images. Current approaches to this problem involve developing
similarity measures over the joint intensity profile of all images, which may
be computationally prohibitive for large image groups and unstable under
various conditions. To tackle these issues, we propose BInGo, a general
unsupervised hierarchical Bayesian framework based on deep learning, to learn
intrinsic structural representations to measure the similarity of multimodal
images. Particularly, a variational auto-encoder with a novel posterior is
proposed, which facilitates the disentanglement learning of structural
representations and spatial transformations, and characterizes the imaging
process from the common structure with shape transition and appearance
variation. Notably, BInGo is scalable to learn from small groups, whereas being
tested for large-scale groupwise registration, thus significantly reducing
computational costs. We compared BInGo with five iterative or deep learning
methods on three public intrasubject and intersubject datasets, i.e. BraTS,
MS-CMR of the heart, and Learn2Reg abdomen MR-CT, and demonstrated its superior
accuracy and computational efficiency, even for very large group sizes (e.g.,
over 1300 2D images from MS-CMR in each group).
