Papers › Learning a Probabilistic Model for Diffeomorphic Registration
Learning a Probabilistic Model for Diffeomorphic Registration
Julian Krebs, Hervé Delingette, Boris Mailhé, Nicholas Ayache, Tommaso Mansi
We propose to learn a low-dimensional probabilistic deformation model from data which can be used for registration and the analysis of deformations. The latent variable model maps similar deformations close to each other in an encoding space. It enables to compare deformations, generate normal or pathological deformations for any new image or to transport deformations from one image pair to any other image. Our unsupervised method is based on variational inference. In particular, we use a conditional variational autoencoder (CVAE) network and constrain transformations to be symmetric and diffeomorphic by applying a differentiable exponentiation layer with a symmetric loss function. We also present a formulation that includes spatial regularization such as diffusion-based filters. Additionally, our framework provides multi-scale velocity field estimations. We evaluated our method on 3-D intra-subject registration using 334 cardiac cine-MRIs. On this dataset, our method showed state-of-the-art performance with a mean DICE score of 81.2% and a mean Hausdorff distance of 7.3mm using 32 latent dimensions compared to three state-of-the-art methods while also demonstrating more regular deformation fields. The average time per registration was 0.32s. Besides, we visualized the learned latent space and show that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Diffeomorphic Medical Image Registration | Automatic Cardiac Diagnosis Challenge (ACDC) | cVAE Diffeomorphic (S3) | Dice | 0.812 | #1 of 3 | Archive leaderboard | report |
| Diffeomorphic Medical Image Registration | Automatic Cardiac Diagnosis Challenge (ACDC) | cVAE Diffeomorphic (S3) | Grad Det-Jac | 1.4 | #1 of 3 | Archive leaderboard | report |
| Diffeomorphic Medical Image Registration | Automatic Cardiac Diagnosis Challenge (ACDC) | cVAE Diffeomorphic (S3) | Hausdorff Distance (mm) | 7.3 | #1 of 3 | Archive leaderboard | report |
| Diffeomorphic Medical Image Registration | Automatic Cardiac Diagnosis Challenge (ACDC) | cVAE Diffeomorphic (S3) | RMSE | 0.30 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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