Anatomy-Guided Representation Learning Using a Transformer-Based Network for Thyroid Nodule Segmentation in Ultrasound Images

• URL: http://arxiv.org/abs/2512.12662v1
• Date: Sun, 14 Dec 2025 12:20:20 GMT
• Title: Anatomy-Guided Representation Learning Using a Transformer-Based Network for Thyroid Nodule Segmentation in Ultrasound Images
• Authors: Muhammad Umar Farooq, Abd Ur Rehman, Azka Rehman, Muhammad Usman, Dong-Kyu Chae, Junaid Qadir,
• Abstract summary: We propose SSMT-Net, a Semi-Supervised Multi-Task Transformer-based Network that enhances Transformer-centric encoder feature extraction capability in an initial unsupervised phase.<n>In the supervised phase, the model jointly optimize nodule segmentation, gland segmentation, and size estimation, integrating both local and global contextual features.<n>In evaluations on the TN3K and DDTI datasets, SSMT-Net outperforms state-of-the-art methods, with higher accuracy and robustness, indicating its potential for real-world clinical applications.
• Score: 16.78356926470714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate thyroid nodule segmentation in ultrasound images is critical for diagnosis and treatment planning. However, ambiguous boundaries between nodules and surrounding tissues, size variations, and the scarcity of annotated ultrasound data pose significant challenges for automated segmentation. Existing deep learning models struggle to incorporate contextual information from the thyroid gland and generalize effectively across diverse cases. To address these challenges, we propose SSMT-Net, a Semi-Supervised Multi-Task Transformer-based Network that leverages unlabeled data to enhance Transformer-centric encoder feature extraction capability in an initial unsupervised phase. In the supervised phase, the model jointly optimizes nodule segmentation, gland segmentation, and nodule size estimation, integrating both local and global contextual features. Extensive evaluations on the TN3K and DDTI datasets demonstrate that SSMT-Net outperforms state-of-the-art methods, with higher accuracy and robustness, indicating its potential for real-world clinical applications.

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