mlsanityData to 14 Oct 2026

Test-Time Training for Deformable Multi-Scale Image Registration

arXiv 2103.13578 ↗First posted 25 Mar 2021cs.CV cs.LG cs.NEcited by 17

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Abstract (arXiv)

Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentation. Popular registration methods such as ANTs and NiftyReg optimize objective functions for each pair of images from scratch, which are time-consuming for 3D and sequential images with complex deformations. Recently, deep learning-based registration approaches such as VoxelMorph have been emerging and achieve competitive performance. In this work, we construct a test-time training for deep deformable image registration to improve the generalization ability of conventional learning-based registration model. We design multi-scale deep networks to consecutively model the residual deformations, which is effective for high variational deformations. Extensive experiments validate the effectiveness of multi-scale deep registration with test-time training based on Dice coefficient for image segmentation and mean square error (MSE), normalized local cross-correlation (NLCC) for tissue dense tracking tasks. Two videos are in https://www.youtube.com/watch?v=NvLrCaqCiAE and https://www.youtube.com/watch?v=pEA6ZmtTNuQ

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 26 of its 50 references that are in the map. First posted on arXiv 25 Mar 2021.

Builds on

26 of 50 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2015Spatial Transformer Networkscited by 1,429
  4. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  5. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
  6. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 373
  7. 2018A Deep Learning Framework for Unsupervised Affine and Deformable Image Registrationcited by 160
  8. 2018An Unsupervised Learning Model for Deformable Medical Image Registrationcited by 150
  9. 2020Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networkscited by 136
  10. 2019Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimationcited by 131
  11. 2019Deep Learning in Medical Image Registration: A Surveycited by 124
  12. 2018Unsupervised Learning for Fast Probabilistic Diffeomorphic Registrationcited by 116
14 more
  1. 2017Quicksilver: Fast Predictive Image Registration - a Deep Learning Approachcited by 113
  2. 2019Recursive Cascaded Networks for Unsupervised Medical Image Registrationcited by 105
  3. 2017End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Networkcited by 75
  4. 2019Test-Time Training with Self-Supervision for Generalization under Distribution Shiftscited by 59
  5. 2018AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomycited by 54
  6. 2020Multi-Domain Image Completion for Random Missing Input Datacited by 30
  7. 2018Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Datacited by 24
  8. 2016An Artificial Agent for Robust Image Registrationcited by 23
  9. 2017Dilated FCN for Multi-Agent 2D/3D Medical Image Registrationcited by 22
  10. 2020Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfercited by 18
  11. 2018AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networkscited by 12
  12. 2020LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentationcited by 8
  13. 2017Adversarial Deep Structured Nets for Mass Segmentation from Mammogramscited by 6
  14. 2019NeurReg: Neural Registration and Its Application to Image Segmentationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 6; 2024: 2; 2025: 0; 2026: 1.

17 papers.

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  1. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 359
  2. 2021Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescuecited by 72
  3. 2022DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Imagescited by 28
  4. 2022Diffeomorphic Image Registration with Neural Velocity Fieldcited by 20
  5. 2022Medical Image Registration via Neural Fieldscited by 19
  6. 2023Towards Open-World Segmentation of Partscited by 15
  7. 2023Robust deformable image registration using cycle-consistent implicit representationscited by 13
  8. 2022Meta-Learning Initializations for Interactive Medical Image Registrationcited by 4
  9. 2022Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registrationcited by 3
  10. 2022Adversarial Contrastive Self-Supervised Learningcited by 2
  11. 2023Echocardiography Segmentation Using Neural ODE-based Diffeomorphic Registration Fieldcited by 2
  12. 2023Deep learning in medical image registration: introduction and surveycited by 1
5 more
  1. 2024Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statisticscited by 1
  2. 2022Region Specific Optimization (RSO)-based Deep Interactive Registrationcited by 0
  3. 2023Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiographycited by 0
  4. 2024Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domaincited by 0
  5. 2026Recursive Uncertainty-Gated Image Registration for Learning-based Algorithmscited by 0