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Found 7 papers, 5 papers with code
uniGradICON: A Foundation Model for Medical Image Registration
We therefore propose uniGradICON, a first step toward a foundation model for registration providing 1) great performance \emph{across} multiple datasets which is not feasible for current learning-based registration methods, 2) zero-shot capabilities for new registration tasks suitable for different acquisitions, anatomical regions, and modalities compared to the training dataset, and 3) a strong initialization for finetuning on out-of-distribution registration tasks.
$\texttt{NePhi}$: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration
In contrast to the predominant voxel-based transformation fields used in learning-based registration approaches, NePhi represents deformations functionally, leading to great flexibility within the design space of memory consumption during training and inference, inference time, registration accuracy, as well as transformation regularity.
Inverse Consistency by Construction for Multistep Deep Registration
Inverse consistency is a desirable property for image registration.
GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration.
$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
ICON: Learning Regular Maps Through Inverse Consistency
We explore if it is possible to obtain spatial regularity using an inverse consistency loss only and elucidate what explains map regularity in such a context.
