Explore our new
demo on anatomy-aware affine registration
with SynthMorph!
Recent advances in deep learning have dramatically enhanced the accuracy and efficiency of medical image registration. Yet, such algorithms often struggle to generalize beyond the image types they see at training.
The SynthMorph learning strategy for acquisition-agnostic registration directly addresses this challenge by exposing networks to wildly variable synthetic data, which leads to unprecedented robustness across a landscape of real image types and greatly alleviates the need for retraining.
SynthMorph is also a tool leveraging the synthesis strategy for rigid, affine, and deformable registration of any brain image right off the MRI scanner, without preprocessing such as skull-stripping. Choose the regularization strength at test time!
We maintain a standalone container version of SynthMorph on the Docker Hub, with a script for easy setup and use supporting any of the following container tools: Docker, Podman, Apptainer, or Singularity.
In addition,
FreeSurfer
ships the command-line tool mri_synthmorph
. For the latest version,
download a build of the FreeSurfer development branch.
Register a moving to a fixed image using the default joint
affine-deformable transformation model and save the result as
moved.mgz
:
mri_synthmorph -o moved.mgz moving.mgz fixed.mgz
Estimate and save an affine transform trans.lta
in FreeSurfer LTA format:
mri_synthmorph -m affine -t trans.lta moving.mgz fixed.mgz
For detailed instructions and information on transforms, run:
mri_synthmorph -h
For those interested in training registration models, we maintain Google Colab notebooks showcasing the SynthMorph learning strategy:
Users who wish to build custom environments can find SynthMorph scripts in FreeSurfer, access the Python requirements for the latest container, browse source code on GitHub, and download the weight files independently:
Several papers describe SynthMorph registration techniques. For example, affine or rigid registration (* denotes equal contribution):
Anatomy-specific acquisition-agnostic affine registration learned from fictitious images
Hoffmann M, Hoopes A, Fischl B*, Dalca AV*
SPIE Medical Imaging: Image Processing, 12464, p 1246402, 2023
(PDF)
For joint registration or the registration tool, see:
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
Hoffmann M, Hoopes A, Greve DN, Fischl B*, Dalca AV*
arXiv:2301.11329, 2024
(arXiv)
For deformable registration and the general synthesis strategy, please cite:
SynthMorph: learning contrast-invariant registration without acquired images
Hoffmann M, Billot B, Greve DN, Iglesias JE, Fischl B, Dalca AV
IEEE Transactions on Medical Imaging (TMI), 41 (3), pp 543-558, 2022
(arXiv,
PMC,
PDF)
Learning MRI contrast-agnostic registration
Hoffmann M, Billot B, Iglesias JE, Fischl B, Dalca AV
IEEE International Symposium on Biomedical Imaging (ISBI), pp 899-903, 2021
(PMC,
PDF)
If you use SynthMorph, please cite us (BibTex)!
Start a discussion, open an issue on GitHub, or reach out to the FreeSurfer list.
We thank Danielle F. Pace for help with computing surface distances. SynthMorph benefited from computational hardware generously provided by the Massachusetts Life Sciences Center.