Registration networks have shown great application potentials in medical image analysis. However, supervised training methods have a great demand for large and high-quality labeled datasets, which is time-consuming and sometimes impractical due to data sharing issues. Unsupervised image registration algorithms commonly employ intensity-based similarity measures as loss functions without any manual annotations. These methods estimate the parameterized transformations between pairs of moving and fixed images through the optimization of the network parameters during training. However, these methods become less effective when the image quality varies, e.g., some images are corrupted by substantial noise or artifacts. In this work, we propose a novel approach based on a low-rank representation, i.e., Regnet-LRR, to tackle the problem. We project noisy images into a noise-free low-rank space, and then compute the similarity between the images. Based on the low-rank similarity measure, we train the registration network to predict the dense deformation fields of noisy image pairs. We highlight that the low-rank projection is reformulated in a way that the registration network can successfully update gradients. With two tasks, i.e., cardiac and abdominal intra-modality registration, we demonstrate that the low-rank representation can boost the generalization ability and robustness of models as well as bring significant improvements in noisy data registration scenarios.

翻译：在医学图像分析中,注册网络显示出巨大的应用潜力。然而,受监督的培训方法对大型和高品质的标签数据集的需求很大,由于数据共享问题,这种数据集耗费时间,有时不切实际。未经监督的图像登记算法通常使用基于强度的类似措施作为损失功能,而没有任何手动说明。这些方法通过优化网络参数来估计移动图像和固定图像之间的参数转换。然而,当图像质量不同时,这些方法就变得不那么有效,例如,有些图像被大量噪音或文物腐蚀。在这项工作中,我们提出了一个以低级别代表制为基础的新颖方法,即Regnet-LRRR,以解决问题。我们将噪音图像投放到一个无噪音的低级别空间,然后将图像的相似性进行比较。根据低级别相似度测量,我们培训登记网络,以预测噪音图像配对的密集变形区域。我们强调低级别投影是为了使登记网络能够成功更新梯度,即Regnet-LRRRR, 来解决这个问题。我们将噪音图像投影射成一个显著的升级模型,从而显示我们内部升级的升级为稳定的升级。