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<p>Accurate segmentation of thyroid nodules from ultrasound images is critical for clinical diagnosis and treatment, as manual analysis is labor-intensive and subject to inter-observer variability. However, existing models often struggle with edge detail capture and discriminative representation of nodule regions, limiting their clinical utility. To address these challenges, we propose a contrast-enhanced VM-UNet for thyroid nodule segmentation, which integrates three key innovations: (1) <b>Wavelet transform</b> to extract frequency-domain features, compensating for VM-UNet’s limitations in capturing edge details using only spatial-domain features, (2) a <b>dual-branch structure</b> (full mask and edge mask) in the upsampling stage to strengthen edge feature learning, (3) a <b>contrastive loss mechanism</b> between background and target regions to enhance the model’s discriminative representation of nodules. Quantitative experiments demonstrate that our method achieves a Dice similarity coefficient (DSC) of 93.61% and a mean Intersection over Union (mIoU) of 88.56% on the TN3k dataset. Crucially, on an external clinical dataset of 360 cases, our model maintained high performance (DSC, 92.87%), significantly outperforming the baseline VM-UNet. Our proposed approach effectively combines wavelet transform, dual-branch edge learning, and contrastive learning, providing a robust technical solution for accurate thyroid nodule segmentation and supporting more reliable clinical decision-making.</p>

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Contrast-Enhanced VM-UNet for Thyroid Nodule Segmentation Based on Wavelet Features and Dual-Branch Edge Learning

  • 期刊论文

  • 发表日期：

    2026年4月2日

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  • Luoning Bao,
  • Lu Liang,
  • Qiongzhu Liu,
  • Huan Cen,
  • Shen Feng,
  • Zhichao Ye,
  • Bijing Li,
  • Pengtao Sun

摘要

Accurate segmentation of thyroid nodules from ultrasound images is critical for clinical diagnosis and treatment, as manual analysis is labor-intensive and subject to inter-observer variability. However, existing models often struggle with edge detail capture and discriminative representation of nodule regions, limiting their clinical utility. To address these challenges, we propose a contrast-enhanced VM-UNet for thyroid nodule segmentation, which integrates three key innovations: (1) Wavelet transform to extract frequency-domain features, compensating for VM-UNet’s limitations in capturing edge details using only spatial-domain features, (2) a dual-branch structure (full mask and edge mask) in the upsampling stage to strengthen edge feature learning, (3) a contrastive loss mechanism between background and target regions to enhance the model’s discriminative representation of nodules. Quantitative experiments demonstrate that our method achieves a Dice similarity coefficient (DSC) of 93.61% and a mean Intersection over Union (mIoU) of 88.56% on the TN3k dataset. Crucially, on an external clinical dataset of 360 cases, our model maintained high performance (DSC, 92.87%), significantly outperforming the baseline VM-UNet. Our proposed approach effectively combines wavelet transform, dual-branch edge learning, and contrastive learning, providing a robust technical solution for accurate thyroid nodule segmentation and supporting more reliable clinical decision-making.