mlsanityData to 7 Oct 2026

multiGradICON: A Foundation Model for Multimodal Medical Image Registration

arXiv 2408.00221 ↗First posted 1 Aug 2024eess.IV cs.CVcited by 16

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Abstract (arXiv)

Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventional non-deep-learning-based approaches, anatomy-specific. Recently, a universal deep registration approach, uniGradICON, has been proposed. However, uniGradICON focuses on monomodal image registration. In this work, we therefore develop multiGradICON as a first step towards universal *multimodal* medical image registration. Specifically, we show that 1) we can train a DL registration model that is suitable for monomodal *and* multimodal registration; 2) loss function randomization can increase multimodal registration accuracy; and 3) training a model with multimodal data helps multimodal generalization. Our code and the multiGradICON model are available at https://github.com/uncbiag/uniGradICON.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 24 of its 58 references that are in the map. First posted on arXiv 1 Aug 2024.

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24 of 58 references.

References · 24

  1. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  2. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 657
  3. 2020SynthMorph: learning contrast-invariant registration without acquired imagescited by 137
  4. 2020Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networkscited by 136
  5. 2017Quicksilver: Fast Predictive Image Registration - a Deep Learning Approachcited by 113
  6. 2020Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networkscited by 107
  7. 2018Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registrationcited by 90
  8. 2021HyperMorph: Amortized Hyperparameter Learning for Image Registrationcited by 84
  9. 2019DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentationcited by 66
  10. 2023A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyondcited by 55
  11. 2024uniGradICON: A Foundation Model for Medical Image Registrationcited by 55
  12. 2019Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representationscited by 52
12 more
  1. 2019Networks for Joint Affine and Non-parametric Image Registrationcited by 44
  2. 2022textttGradICON: Approximate Diffeomorphisms via Gradient Inverse Consistencycited by 40
  3. 2021The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patientscited by 32
  4. 2021ICON: Learning Regular Maps Through Inverse Consistencycited by 26
  5. 2020Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registrationcited by 25
  6. 2016A Deep Metric for Multimodal Registrationcited by 19
  7. 2024Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registrationcited by 17
  8. 2018Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similaritycited by 16
  9. 2018Adversarial Image Registration with Application for MR and TRUS Image Fusioncited by 14
  10. 2023SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramidcited by 14
  11. 2023SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformationcited by 9
  12. 2017Fast Predictive Multimodal Image Registrationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 6.

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  1. 2025Improving Generalization of Medical Image Registration Foundation Modelcited by 6
  2. 2025Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Studycited by 3
  3. 2024CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularizationcited by 2
  4. 2026On The Robustness of Foundational 3D Medical Image Segmentation Models Against Imprecise Visual Promptscited by 2
  5. 2025Implicit Deformable Medical Image Registration with Learnable Kernelscited by 1
  6. 2025Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registrationcited by 1
  7. 2025MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detectioncited by 1
  8. 2025A Steerable Deep Network for Model-Free Diffusion MRI Registrationcited by 0
  9. 2025Downstream Analysis of Foundational Medical Vision Models for Disease Progressioncited by 0
  10. 2025OncoReg: Medical Image Registration for Oncological Challengescited by 0
  11. 2025Test Time Optimized Generalized AI-based Medical Image Registration Methodcited by 0
  12. 2026Data-driven registration and modeling of brain deformation for image-guided neurosurgerycited by 0
4 more
  1. 2026Foundation Models for Medical Imaging: Status, Challenges, and Directionscited by 0
  2. 2026Adapting Frozen Mono-modal Backbones for Multi-modal Registration via Contrast-Agnostic Instance Optimizationcited by 0
  3. 2026XRF-to-Optical Field-of-View Localization with Vision Language Modelscited by 0
  4. 2026Benchmarking Intra-Patient 3D Deformable Multimodal Image Registrationcited by 0