# A Meta-Learning Approach for Medical Image Registration

arXiv: https://arxiv.org/abs/2104.10447 · HTML: https://mlsanity.com/paper/2104.10447/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://mlsanity.com/paper/2007.02035/) (2020)
- [Meta-Transfer Learning for Zero-Shot Super-Resolution](https://mlsanity.com/paper/2002.12213/) (2020)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network](https://mlsanity.com/paper/1704.06065/) (2017)
- [Non-rigid image registration using fully convolutional networks with deep self-supervision](https://mlsanity.com/paper/1709.00799/) (2017)
- [Meta-Learning Initializations for Image Segmentation](https://mlsanity.com/paper/1912.06290/) (2019)
- [Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2011.09608/) (2020)
- [Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2003.01300/) (2020)
- [3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds](https://mlsanity.com/paper/2010.11504/) (2020)

## Built on by (most cited first, 5 of 5)

- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records](https://mlsanity.com/paper/2112.05321/) (2021)
- [Meta-Learning Initializations for Interactive Medical Image Registration](https://mlsanity.com/paper/2210.15371/) (2022)
- [Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration](https://mlsanity.com/paper/2207.10996/) (2022)
- [Deep learning in medical image registration: introduction and survey](https://mlsanity.com/paper/2309.00727/) (2023)

## Abstract (arXiv)

Non-rigid registration is a necessary but challenging task in medical imaging studies. Recently, unsupervised registration models have shown good performance, but they often require a large-scale training dataset and long training times. Therefore, in real world application where only dozens to hundreds of image pairs are available, existing models cannot be practically used. To address these limitations, we propose a novel unsupervised registration model which is integrated with a gradient-based meta learning framework. In particular, we train a meta learner which finds an initialization point of parameters by utilizing a variety of existing registration datasets. To quickly adapt to various tasks, the meta learner was updated to get close to the center of parameters which are fine-tuned for each registration task. Thereby, our model can adapt to unseen domain tasks via a short fine-tuning process and perform accurate registration. To verify the superiority of our model, we train the model for various 2D medical image registration tasks such as retinal choroid Optical Coherence Tomography Angiography (OCTA), CT organs, and brain MRI scans and test on registration of retinal OCTA Superficial Capillary Plexus (SCP). In our experiments, the proposed model obtained significantly improved performance in terms of accuracy and training time compared to other registration models.