Semi-supervised boundary-aware medical image segmentation via symmetric boundary-foreground collaboration

초록

Due to the scarcity of labeled data, semi-supervised segmentation learning has gained significant attention. However, accurate predictions of hard-to-identify boundaries in medical images remains challenging, especially when learning from unlabeled data. To address this issue, we propose a semi-supervised boundary-aware medical image segmentation method Via Symmetric Boundary-Foreground Collaboration (SBFC). Specifically, SBFC framwork constructs a symmetric dual-task segmentation (SDTS) network containing two symmetric segmentation models, each consisting of a dual-task U-shaped net with one encoder and two task-specific decoders for foreground and boundary segmentation. Using the predicted foreground, boundary probability maps and segmentation and derived boundary labels, a novel compound optimization objective function is proposed. This function integrates Cross-Task Consistency Regularization (CTCR) and Cross-Model Consistency Regularization (CMCR) for unlabeled data with supervised optimization for labeled data. Comprehensive experiments conducted on five public medical image datasets show that our method outperforms state-of-the-art comparative methods in terms of multiple consensus segmentation and boundary evaluation metrics.

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