Knowledge-Prompted Trustworthy Disentangled Learning for Thyroid Ultrasound Segmentation With Limited Annotations

The similar textures, diverse shapes and blurred boundaries of thyroid lesions in ultrasound images pose a significant challenge to accurate segmentation. Although several methods have been proposed to alleviate the aforementioned issues, their generalization is hindered by limited annotation data and insufficient ability to distinguish lesion from its surrounding tissues, especially in the presence of noise and outlier. Additionally, most existing methods lack uncertainty estimation which is essential for providing trustworthy results and identifying potential mispredictions. To this end, we propose knowledge-prompted trustworthy disentangled learning (KPTD) for thyroid ultrasound segmentation with limited annotations. The proposed method consists of three key components: 1) knowledge-aware prompt learning (KAPL) encodes TI-RADS reports into text features and introduces learnable prompts to extract contextual embeddings, which assist in generating region activation maps (serving as pseudo-labels for unlabeled images); 2) foreground-background disentangled learning (FBDL) leverages region activation maps to disentangle foreground and background representations, refining their prototype distributions through a contrastive learning strategy to enhance the model's discrimination and robustness; and 3) foreground-background trustworthy fusion (FBTF) integrates the foreground and background representations and estimates their uncertainty based on evidence theory, providing trustworthy segmentation results. Experimental results show that KPTD achieves superior segmentation performance under limited annotations, significantly outperforming state-of-the-art methods.