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. Code and data are publicly available at \url{https://github.com/acasamitjana/SynthByReg}

翻译：非线性跨模态配准常因缺乏与对齐性高度相关的客观函数而面临挑战。本文提出一种“合成引导配准”方法，将该问题转化为更易处理的同模态任务。我们引入一种面向弱监督图像跨域翻译的配准损失函数，该函数无需完美对齐的训练数据，而是利用参数冻结的配准U-Net驱动合成CNN实现目标翻译。结合基于对比学习的结构保持约束，可防止过拟合导致的模糊与内容偏移。将该方法应用于组织学切片与MRI切片的配准——这是三维组织学重建的关键步骤。在两个公开数据集上的实验表明，本方法优于基于互信息的配准（地标误差降低13%）及CycleGAN等合成算法（误差降低11%），并与基于标签监督的配准CNN性能相当。代码与数据已开源在：\url{https://github.com/acasamitjana/SynthByReg}