mlsanityData to 14 Oct 2026

MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network

arXiv 2111.12123 ↗First posted 23 Nov 2021cs.CV cs.AI cs.LGcited by 4

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Abstract (arXiv)

Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical application was made difficult by the high calculation time needed to solve the optimisation problem. Deep learning overtook this limitation by taking advantage of GPU calculation and the learning process. However, many deep learning methods do not take into account desirable properties respected by classical algorithms. In this paper, we present MICS, a novel deep learning algorithm for medical imaging registration. As registration is an ill-posed problem, we focused our algorithm on the respect of different properties: inverse consistency, symmetry and orientation conservation. We also combined our algorithm with a multi-step strategy to refine and improve the deformation grid. While many approaches applied registration to brain MRI, we explored a more challenging body localisation: abdominal CT. Finally, we evaluated our method on a dataset used during the Learn2Reg challenge, allowing a fair comparison with published methods.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 35 of its 64 references that are in the map. First posted on arXiv 23 Nov 2021.

Builds on

35 of 64 references.

References · 35

  1. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  2. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  3. 2015Spatial Transformer Networkscited by 1,429
  4. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
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  8. 2018A Deep Learning Framework for Unsupervised Affine and Deformable Image Registrationcited by 160
  9. 2015DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentationcited by 159
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  11. 2018An Unsupervised Learning Model for Deformable Medical Image Registrationcited by 150
  12. 2020Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networkscited by 136
23 more
  1. 2019Deep Learning in Medical Image Registration: A Surveycited by 124
  2. 2018Unsupervised Learning for Fast Probabilistic Diffeomorphic Registrationcited by 116
  3. 2019Recursive Cascaded Networks for Unsupervised Medical Image Registrationcited by 105
  4. 2019Deep Learning in Medical Image Registration: A Reviewcited by 98
  5. 2018Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registrationcited by 90
  6. 2018Learning a Probabilistic Model for Diffeomorphic Registrationcited by 62
  7. 2019Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Networkcited by 62
  8. 2018Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearancecited by 58
  9. 2019Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representationscited by 52
  10. 2019Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networkscited by 40
  11. 2018Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registrationcited by 32
  12. 2020LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentationcited by 28
  13. 2019mlVIRNET: Multilevel Variational Image Registration Networkcited by 24
  14. 2018Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registrationcited by 23
  15. 2019Unsupervised Deformable Image Registration Using Cycle-Consistent CNNcited by 22
  16. 2020Medical Image Registration Using Deep Neural Networks: A Comprehensive Reviewcited by 22
  17. 2019One Shot Learning for Deformable Medical Image Registration and Periodic Motion Trackingcited by 21
  18. 2018FAIM -- A ConvNet Method for Unsupervised 3D Medical Image Registrationcited by 19
  19. 2020Learning Deformable Registration of Medical Images with Anatomical Constraintscited by 19
  20. 2018Linear and Deformable Image Registration with 3D Convolutional Neural Networkscited by 8
  21. 2018Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Trackingcited by 7
  22. 2019FIRE: Unsupervised bi-directional inter-modality registration using deep networkscited by 7
  23. 2020Deep learning based registration using spatial gradients and noisy segmentation labelscited by 7

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2.

4 papers.

  1. 2022Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisationcited by 19
  2. 2025Beyond the LUMIR challenge: The pathway to foundational registration modelscited by 12
  3. 2023SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registrationcited by 7
  4. 2025Biomechanical Constraints Assimilation in Deep-Learning Image Registration: Application to sliding and locally rigid deformationscited by 0