State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised learning, which heavily relies on hand-crafted similarity metrics designed by domain experts, or learning from synthetic data, which introduces a domain shift. To overcome the limitations of these strategies, we propose a novel self-supervised learning paradigm for unsupervised registration, relying on self-training. Our idea is based on two key insights. Feature-based differentiable optimizers 1) perform reasonable registration even from random features and 2) stabilize the training of the preceding feature extraction network on noisy labels. Consequently, we propose cyclical self-training, where pseudo labels are initialized as the displacement fields inferred from random features and cyclically updated based on more and more expressive features from the learning feature extractor, yielding a self-reinforcement effect. We evaluate the method for abdomen and lung registration, consistently surpassing metric-based supervision and outperforming diverse state-of-the-art competitors. Source code is available at https://github.com/multimodallearning/reg-cyclical-self-train.

翻译：基于深度学习的先进配准方法主要采用三种学习策略：监督学习（需要昂贵的人工标注）、无监督学习（高度依赖领域专家设计的相似性度量）以及合成数据学习（存在域偏移问题）。为突破这些策略的局限，我们提出一种新颖的自监督学习范式，通过循环自训练实现无监督配准。该方法基于两个关键发现：基于特征的可微优化器①即使在随机特征条件下也能实现合理的配准；②能稳定含噪声标签下特征提取网络的训练过程。据此我们提出循环自训练框架——将随机特征推断的位移场作为初始伪标签，并基于学习型特征提取器不断优化的特征进行循环更新，最终形成自增强效应。在腹部和肺部配准任务上的评估表明，该方法始终优于基于度量的监督方法，并超越多种主流竞争者。源代码已开源至https://github.com/multimodallearning/reg-cyclical-self-train。