Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two different public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to a registration CNN with label supervision.

中文翻译：

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Synth-by-Reg (SbR)：基于合成的配对图像配准的对比学习

由于缺乏作为对齐良好代理的目标函数，非线性模态间配准通常具有挑战性。在这里，我们提出了一种通过注册合成的方法，将这个问题转化为更简单的模态内任务。我们为不需要完全对齐的训练数据的域之间的弱监督图像转换引入了配准损失。这种损失利用具有冻结权重的注册 U-Net，将合成 CNN 推向所需的翻译。我们用基于对比学习的结构保留约束来补充这种损失，这可以防止由于过度拟合而造成的模糊和内容变化。我们将此方法应用于将组织切片配准到 MRI 切片，这是 3D 组织学重建的关键步骤。