mlsanityData to 14 Oct 2026

ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration

arXiv 2403.16526 ↗First posted 25 Mar 2024cs.CVcited by 4

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Abstract (arXiv)

Deformable image registration plays a crucial role in medical imaging, aiding in disease diagnosis and image-guided interventions. Traditional iterative methods are slow, while deep learning (DL) accelerates solutions but faces usability and precision challenges. This study introduces a pyramid network with the enhanced motion decomposition Transformer (ModeTv2) operator, showcasing superior pairwise optimization (PO) akin to traditional methods. We re-implement ModeT operator with CUDA extensions to enhance its computational efficiency. We further propose RegHead module which refines deformation fields, improves the realism of deformation and reduces parameters. By adopting the PO, the proposed network balances accuracy, efficiency, and generalizability. Extensive experiments on three public brain MRI datasets and one abdominal CT dataset demonstrate the network's suitability for PO, providing a DL model with enhanced usability and interpretability. The code is publicly available at https://github.com/ZAX130/ModeTv2.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 37 of its 81 references that are in the map. First posted on arXiv 25 Mar 2024.

Builds on

37 of 81 references.

References · 37

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25 more
  1. 2019Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfacescited by 186
  2. 2020Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networkscited by 136
  3. 2019Deep Learning in Medical Image Registration: A Surveycited by 124
  4. 2019Recursive Cascaded Networks for Unsupervised Medical Image Registrationcited by 105
  5. 2019Deep Learning in Medical Image Registration: A Reviewcited by 98
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  7. 2019Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Networkcited by 62
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  9. 2022XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attentioncited by 46
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  11. 2023ModeT: Learning Deformable Image Registration via Motion Decomposition Transformercited by 23
  12. 2021Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusioncited by 22
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  14. 2019One Shot Learning for Deformable Medical Image Registration and Periodic Motion Trackingcited by 21
  15. 2021Cross-modal Attention for MRI and Ultrasound Volume Registrationcited by 21
  16. 2023Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registrationcited by 21
  17. 2022Coordinate Translator for Learning Deformable Medical Image Registrationcited by 20
  18. 2022Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learningcited by 20
  19. 2019Recurrent Registration Neural Networks for Deformable Image Registrationcited by 11
  20. 2022Residual Aligner Networkcited by 10
  21. 2022Cross-Modality Image Registration using a Training-Time Privileged Third Modalitycited by 9
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  23. 2022One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTscited by 8
  24. 2022Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registrationcited by 8
  25. 2022Recursive Deformable Image Registration Network with Mutual Attentioncited by 7

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2025Beyond the LUMIR challenge: The pathway to foundational registration modelscited by 12
  2. 2024Encoding Matching Criteria for Cross-domain Deformable Image Registrationcited by 0
  3. 2024FiRework: Field Refinement Framework for Efficient Enhancement of Deformable Registrationcited by 0
  4. 2025Accelerated 3D-3D rigid registration of echocardiographic images obtained from apical window using particle filtercited by 0