Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
This work addresses the need for robust and regular deformation fields in medical image registration, particularly for cardiac MR, but is incremental as it builds on existing probabilistic and learning-based approaches.
The paper tackles the problem of deformable image registration by proposing an unsupervised learning method using a conditional variational autoencoder to model probabilistic deformations, achieving robust intra-subject registration with a mean DICE score of 78.3% and mean Hausdorff distance of 7.9mm on cardiac MR data.
We propose a deformable registration algorithm based on unsupervised learning of a low-dimensional probabilistic parameterization of deformations. We model registration in a probabilistic and generative fashion, by applying a conditional variational autoencoder (CVAE) network. This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space. Most recent learning-based registration algorithms use supervised labels or deformation models, that miss important properties such as diffeomorphism and sufficiently regular deformation fields. In this work, we constrain transformations to be diffeomorphic by using a differentiable exponentiation layer with a symmetric loss function. We evaluated our method on 330 cardiac MR sequences and demonstrate robust intra-subject registration results comparable to two state-of-the-art methods but with more regular deformation fields compared to a recent learning-based algorithm. Our method reached a mean DICE score of 78.3% and a mean Hausdorff distance of 7.9mm. In two preliminary experiments, we illustrate the model's abilities to transport pathological deformations to healthy subjects and to cluster five diseases in the unsupervised deformation encoding space with a classification performance of 70%.