# Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021 arXiv: https://arxiv.org/abs/2112.03053 ยท HTML: https://mlsanity.com/paper/2112.03053/ ## Key facts - Cited by 40 papers in the map (data through 14 Oct 2026). - Builds on 3 of its 9 references that are in the map. - First posted on arXiv 6 Dec 2021. ## Builds on (most cited first, 3 of 3) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017) - [Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks](https://mlsanity.com/paper/1907.10931/) (2019) ## Built on by (most cited first, 30 of 40) - [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021) - [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023) - [Large-Scale 3D Medical Image Pre-training with Geometric Context Priors](https://mlsanity.com/paper/2410.09890/) (2024) - [Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis](https://mlsanity.com/paper/2411.02372/) (2024) - [Deep Learning in Medical Image Registration: Magic or Mirage?](https://mlsanity.com/paper/2408.05839/) (2024) - [Coordinate Translator for Learning Deformable Medical Image Registration](https://mlsanity.com/paper/2203.03626/) (2022) - [Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation](https://mlsanity.com/paper/2203.00046/) (2022) - [Medical Image Registration via Neural Fields](https://mlsanity.com/paper/2206.03111/) (2022) - [Spatially Covariant Image Registration with Text Prompts](https://mlsanity.com/paper/2311.15607/) (2023) - [Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration](https://mlsanity.com/paper/2402.18933/) (2024) - [SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid](https://mlsanity.com/paper/2307.09727/) (2023) - [General Purpose Image Encoder DINOv2 for Medical Image Registration](https://mlsanity.com/paper/2402.15687/) (2024) - [Vector Field Attention for Deformable Image Registration](https://mlsanity.com/paper/2407.10209/) (2024) - [Deformable Cross-Attention Transformer for Medical Image Registration](https://mlsanity.com/paper/2303.06179/) (2023) - [SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation](https://mlsanity.com/paper/2311.14986/) (2023) - [textttNePhi: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration](https://mlsanity.com/paper/2309.07322/) (2023) - [Does DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration](https://mlsanity.com/paper/2509.06467/) (2025) - [DINOv3 with Test-Time Training for Medical Image Registration](https://mlsanity.com/paper/2508.14809/) (2025) - [Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts](https://mlsanity.com/paper/2207.00371/) (2022) - [Deep Implicit Optimization enables Robust Learnable Features for Deformable Image Registration](https://mlsanity.com/paper/2406.07361/) (2024) - [Unsupervised Multimodal 3D Medical Image Registration with Multilevel Correlation Balanced Optimization](https://mlsanity.com/paper/2409.05040/) (2024) - [UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2410.20348/) (2024) - [Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration](https://mlsanity.com/paper/2303.06168/) (2023) - [Towards Saner Deep Image Registration](https://mlsanity.com/paper/2307.09696/) (2023) - [EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration](https://mlsanity.com/paper/2403.11026/) (2024) - [Encoder-Only Image Registration](https://mlsanity.com/paper/2509.00451/) (2025) - [Unsupervised MR-US Multimodal Image Registration with Multilevel Correlation Pyramidal Optimization](https://mlsanity.com/paper/2602.06288/) (2026) - [Gaussian Primitives for Deformable Image Registration](https://mlsanity.com/paper/2406.03394/) (2024) - [UniReg: Conditional Unified Model for Medical Image Registration](https://mlsanity.com/paper/2503.12868/) (2025) - [SAMIR, an efficient registration framework via robust feature learning from SAM](https://mlsanity.com/paper/2509.13629/) (2025) ## Abstract (arXiv) Current approaches for deformable medical image registration often struggle to fulfill all of the following criteria: versatile applicability, small computation or training times, and the being able to estimate large deformations. Furthermore, end-to-end networks for supervised training of registration often become overly complex and difficult to train. For the Learn2Reg2021 challenge, we aim to address these issues by decoupling feature learning and geometric alignment. First, we introduce a new very fast and accurate optimisation method. By using discretised displacements and a coupled convex optimisation procedure, we are able to robustly cope with large deformations. With the help of an Adam-based instance optimisation, we achieve very accurate registration performances and by using regularisation, we obtain smooth and plausible deformation fields. Second, to be versatile for different registration tasks, we extract hand-crafted features that are modality and contrast invariant and complement them with semantic features from a task-specific segmentation U-Net. With our results we were able to achieve the overall Learn2Reg2021 challenge's second place, winning Task 1 and being second and third in the other two tasks.