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-aware image filterings (DSAIF) 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 the proposed DSAIF to mutually supervised networks decreases the consensus of their erroneous predictions on unlabeled images. This helps to alleviate the confirmation bias issue of overfitting to noisy pseudo labels of unlabeled images, and thus effectively improves the segmentation performance. 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.

中文翻译：

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用于半监督医学图像分割的双结构感知图像过滤


半监督图像分割近年来引起了极大的关注。关键是如何在训练过程中利用未标记的图像。大多数方法在图像和/或模型级别的变化（例如，添加噪声/扰动或创建替代版本）下保持对未标记图像的一致预测。在大多数图像级变化中，医学图像通常具有先验的结构信息，而这些信息尚未得到很好的探索。在本文中，我们提出了新颖的双结构感知图像过滤 （DSAIF） 作为半监督医学图像分割的图像级变化。在连接滤波的推动下，通过在结构感知的基于树的图像表示中进行过滤来简化图像，我们采用了双对比度不变 Max 树和 Min-tree 表示。具体来说，我们提出了一种新颖的连通过滤，它删除了在 Max/Min 树中没有兄弟姐妹的拓扑等效节点（即连通分量）。这将导致两个过滤图像保留拓扑关键结构。将提出的 DSAIF 应用于相互监督的网络会降低他们对未标记图像错误预测的共识。这有助于缓解对未标注图像的含噪伪标签过拟合的确认偏差问题，从而有效提高分割性能。在三个基准数据集上的广泛实验结果表明，所提出的方法明显/始终优于一些最先进的方法。源代码将公开提供。