Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment

Abstract

Atlas building and image registration are important tasks for medical image analysis. Once one or multiple atlases from an image population have been constructed, commonly (1) images are warped into an atlas space to study intra-subject or inter-subject variations or (2) a possibly probabilistic atlas is warped into image space to assign anatomical labels. Atlas estimation and nonparametric transformations are computationally expensive as they usually require numerical optimization. Additionally, previous approaches for atlas building often define similarity measures between a fuzzy atlas and each individual image, which may cause alignment difficulties because a fuzzy atlas does not exhibit clear anatomical structures in contrast to the individual images. This work explores using a convolutional neural network (CNN) to jointly predict the atlas and a stationary velocity field (SVF) parameterization for diffeomorphic image registration with respect to the atlas. Our approach does not require affine pre-registrations and utilizes pairwise image alignment losses to increase registration accuracy. We evaluate our model on 3D knee magnetic resonance images (MRI) from the OAI-ZIB dataset. Our results show that the proposed framework achieves better performance than other state-of-the-art image registration algorithms, allows for end-to-end training, and for fast inference at test time. 1

Anatomy-aware and acquisition-agnostic joint registration with SynthMorph

“…Finally, identifying an optimal architecture for affine registration and formulating the problem in a differen- tiable manner will enable embedding and jointly learning affine registration with other tasks, for example for creating conditional template images representing a subject population, from non-aligned input images Ding and Niethammer, 2022;Sinclair et al, 2022).…”