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.

提出了一种新颖的双结构保持图像滤波（DSPIF）作为半监督医学图像分割的图像级变化方法，通过在最大树和最小树表示中删除没有兄弟节点的拓扑等价节点，保留拓扑关键结构，在相互监督中应用这种滤波对半监督医学图像分割有益，并在三个基准数据集上的实验证明，该方法明显优于一些最先进的方法。