Current deep learning approaches in medical image registration usually face the challenges of distribution shift and data collection, hindering real-world deployment. In contrast, universal medical image registration aims to perform registration on a wide range of clinically relevant tasks simultaneously, thus having tremendous potential for clinical applications. In this paper, we present the first attempt to achieve the goal of universal 3D medical image registration in sequential learning scenarios by proposing a continual learning method. Specifically, we utilize meta-learning with experience replay to mitigating the problem of catastrophic forgetting. To promote the generalizability of meta-continual learning, we further propose sharpness-aware meta-continual learning (SAMCL). We validate the effectiveness of our method on four datasets in a continual learning setup, including brain MR, abdomen CT, lung CT, and abdomen MR-CT image pairs. Results have shown the potential of SAMCL in realizing universal image registration, which performs better than or on par with vanilla sequential or centralized multi-task training strategies.The source code will be available from https://github.com/xzluo97/Continual-Reg.

本文提出了一种实现通用三维医疗图像配准的连续学习方法，通过基于元学习和经验回放的方式缓解灾难性遗忘，并进一步提出了锐度感知的元-连续学习方法，通过实验证明了该方法在实现通用图像配准方面的潜力。

通过锐度感知元持续学习实现通用医学图像配准