Dual Structure-Aware Image Filterings for Semi-supervised Medical Image Segmentation

TL;DR

The proposed method significantly / consistently outperforms some state-of-the-art methods on three benchmark datasets and helps to alleviate the confirmation bias issue of overfitting to noisy pseudo labels of unlabeled images.

Abstract

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.

Figures (8)

• Figure 1: The pipeline of the proposed DSAIF framework using mutual supervision of CPS chen2021semi as the model-level variations. We propose novel dual structure-aware image filterings (DSAIF) based on Max/Min-tree representation as the image-level variations. We remove every node (marked in red) without siblings in Max/Min-tree which is topologically equivalent to its ancestor node.
• Figure 2: An illustrative example of the proposed DSAIF. For the Max-tree and Min-tree built on the original image (b), we remove every node (marked in red) without siblings which is topologically equivalent to its ancestor node. The two images reconstructed from filtered Max/Min-tree denoted as USAIF (a) and LSAIF (c) have the same topological structure as the original image, but are of quite different appearances. The number after the letter denotes the graylevel of the region.
• Figure 3: An illustrative example of leveraging the contrast-invariance property (a) of Max/Min-tree in DSAIF. Applying monotonically increasing contrast changes before DSAIF increases the appearance diversity while preserving the same topological structure as the original images.
• Figure 4: Some qualitative results of DSAIF on LA dataset xiong2021global (first row), Pancreas-CT clark2013cancer (middle row), and PROMISE12 litjens2014evaluation (bottom row). The changed images in (b) are obtained by applying monotonically increasing contrast change to the original ones.
• Figure 5: Some qualitative segmentation results of DSAIF on LA dataset xiong2021global (first two rows), Pancreas-CT dataset clark2013cancer (middle two rows), and PROMISE12 dataset litjens2014evaluation (bottom two rows).