Semi-supervised image segmentation has attracted great attention recently. The key is how to leverage unlabeled images in the training process. Most methods maintain consistent predictions of the unlabeled images under variations (e.g., adding noise/perturbations, or creating alternative versions) in the image and/or model level. In most image-level variation, medical images often have prior structure information, which has not been well explored. In this paper, we propose novel dual structure-preserving image filterings (DSPIF) as the image-level variations for semi-supervised medical image segmentation. Motivated by connected filtering that simplifies image via filtering in structure-aware tree-based image representation, we resort to the dual contrast invariant Max-tree and Min-tree representation. Specifically, we propose a novel connected filtering that removes topologically equivalent nodes (i.e. connected components) having no siblings in the Max/Min-tree. This results in two filtered images preserving topologically critical structure. Applying such dual structure-preserving image filterings in mutual supervision is beneficial for semi-supervised medical image segmentation. Extensive experimental results on three benchmark datasets demonstrate that the proposed method significantly/consistently outperforms some state-of-the-art methods. The source codes will be publicly available.

翻译：半监督图像分割近年来受到广泛关注，其关键问题在于如何在训练过程中有效利用未标记图像。现有方法大多通过保持未标记图像在图像级和/或模型级变化（如添加噪声/扰动或生成替代版本）下预测结果的一致性来实现。然而在大多数图像级变化中，医学图像常具备先验结构信息，这一特性尚未被充分挖掘。本文提出一种新颖的双重结构保持图像滤波（DSPIF）作为图像级变化，用于半监督医学图像分割。受连接滤波（通过结构感知的树状图像表示进行滤波来简化图像）的启发，我们采用对偶对比度不变的Max-tree与Min-tree表示。具体而言，我们提出一种新颖的连接滤波方法，移除Max/Min-tree中无兄弟节点的拓扑等价节点（即连通分量），从而获得两幅保留关键拓扑结构的滤波图像。将这种双重结构保持图像滤波应用于相互监督机制，有利于半监督医学图像分割。在三个基准数据集上的大量实验结果表明，所提方法显著/持续优于部分现有先进方法。相关源代码将公开发布。