For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective. This correspondence-informing supervision entails cost in annotation that requires significant specialised effort. This paper describes a semi-weakly-supervised registration pipeline that improves the model performance, when only a small corresponding-ROI-labelled dataset is available, by exploiting unlabelled image pairs. We examine two types of augmentation methods by perturbation on network weights and image resampling, such that consistency-based unsupervised losses can be applied on unlabelled data. The novel WarpDDF and RegCut approaches are proposed to allow commutative perturbation between an image pair and the predicted spatial transformation (i.e. respective input and output of registration networks), distinct from existing perturbation methods for classification or segmentation. Experiments using 589 male pelvic MR images, labelled with eight anatomical ROIs, show the improvement in registration performance and the ablated contributions from the individual strategies. Furthermore, this study attempts to construct one of the first computational atlases for pelvic structures, enabled by registering inter-subject MRs, and quantifies the significant differences due to the proposed semi-weak supervision with a discussion on the potential clinical use of example atlas-derived statistics.

翻译：在训练配准网络时，来自分割对应感兴趣区域（ROI）的弱监督已被证明可有效（a）补充无监督方法，以及（b）在无监督损失不可用或无效的配准任务中独立使用。这种对应性监督需要投入大量专业标注成本。本文描述了一种半弱监督配准流程，通过利用未标注图像对，在仅有少量对应ROI标注数据集的情况下提升模型性能。我们研究了两种通过扰动网络权重和图像重采样实现的增强方法，从而对未标注数据应用基于一致性的无监督损失。提出了新颖的WarpDDF和RegCut方法，允许在图像对与预测的空间变换（即配准网络的输入和输出）之间进行可交换扰动，这不同于现有用于分类或分割的扰动方法。实验使用589张男性盆腔磁共振图像（标注了八个解剖学ROI），显示配准性能的提升及各个策略的消融贡献。此外，本研究尝试构建首批盆腔结构计算图谱之一，通过受试者间MR配准实现，并量化了所提出的半弱监督带来的显著差异，同时讨论了图谱衍生统计数据的潜在临床应用。