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mlVIRNET: Multilevel Variational Image Registration Network

arXiv 1909.10084 ↗First posted 22 Sep 2019cs.CVcited by 24

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Abstract (arXiv)

We present a novel multilevel approach for deep learning based image registration. Recently published deep learning based registration methods have shown promising results for a wide range of tasks. However, these algorithms are still limited to relatively small deformations. Our method addresses this shortcoming by introducing a multilevel framework, which computes deformation fields on different scales, similar to conventional methods. Thereby, a coarse-level alignment is obtained first, which is subsequently improved on finer levels. We demonstrate our method on the complex task of inhale-to-exhale lung registration. We show that the use of a deep learning multilevel approach leads to significantly better registration results.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2019.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 5; 2024: 4; 2025: 5.

24 papers.

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  1. 2021Conditional Deformable Image Registration with Convolutional Neural Networkcited by 59
  2. 2023A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyondcited by 55
  3. 2024uniGradICON: A Foundation Model for Medical Image Registrationcited by 55
  4. 2022textttGradICON: Approximate Diffeomorphisms via Gradient Inverse Consistencycited by 40
  5. 2021Learning a Model-Driven Variational Network for Deformable Image Registrationcited by 25
  6. 2023Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registrationcited by 21
  7. 2022Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learningcited by 20
  8. 2021Embedding Gradient-based Optimization in Image Registration Networkscited by 10
  9. 2022Deep Learning for Medical Image Registration: A Comprehensive Reviewcited by 8
  10. 2022Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registrationcited by 8
  11. 2022Medical image registration using unsupervised deep neural network: A scoping literature reviewcited by 5
  12. 2021MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration networkcited by 4
12 more
  1. 2023Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registrationcited by 4
  2. 2024ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registrationcited by 4
  3. 2023ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Imagescited by 3
  4. 2024MrRegNet: Multi-resolution Mask Guided Convolutional Neural Network for Medical Image Registration with Large Deformationscited by 3
  5. 2023Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methodscited by 2
  6. 2024Recurrent Inference Machine for Medical Image Registrationcited by 2
  7. 2025GuidedMorph: Two-Stage Deformable Registration for Breast MRIcited by 2
  8. 2025Bridging Classical and Learning-based Iterative Registration through Deep Equilibrium Modelscited by 2
  9. 2025Uncertainty Estimation for Pretrained Medical Image Registration Models via Transformation Equivariancecited by 1
  10. 2021DDR-Net: Dividing and Downsampling Mixed Network for Diffeomorphic Image Registrationcited by 0
  11. 2025Biomechanical Constraints Assimilation in Deep-Learning Image Registration: Application to sliding and locally rigid deformationscited by 0
  12. 2025Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlationcited by 0