Dual Structure-Aware Image Filterings for Semi-supervised Medical Image Segmentation
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) i...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2312.07264 |