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<p>Medical image segmentation plays a crucial role in clinical medicine, serving as a key tool for auxiliary diagnosis, treatment planning, and disease monitoring. However, traditional segmentation methods such as U-Net are often limited by weak semantic expression of target regions, which stems from insufficient generalization and a lack of interactivity. Incorporating text prompts offers a promising avenue to more accurately pinpoint lesion locations, yet existing text-guided methods are still hindered by insufficient cross-modal interaction and inadequate cross-modal feature representation. To address these challenges, we propose the text-guided multi-stage cross-perception network (TMC). TMC incorporates a multi-stage cross-attention module (MCM) to enhance the model’s understanding of fine-grained semantic details and a multi-stage alignment loss (MA Loss) to improve the consistency of cross-modal semantics across different feature levels. Experimental results on three public datasets (QaTa-COV19, MosMedData, and Duke-Breast-Cancer-MRI) demonstrate the superior performance of TMC, achieving Dice scores of 84.65%, 78.39%, and 88.09%, respectively, and consistently outperforming both U-Net-based networks and existing text-guided methods.</p>

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Text-Guided Multi-stage Cross-perception Network for Medical Image Segmentation

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Gaoyu Chen,
  • Haixia Pan,
  • Yuhan Tian,
  • Zhaohui Tian,
  • Xirui Wu

摘要

Medical image segmentation plays a crucial role in clinical medicine, serving as a key tool for auxiliary diagnosis, treatment planning, and disease monitoring. However, traditional segmentation methods such as U-Net are often limited by weak semantic expression of target regions, which stems from insufficient generalization and a lack of interactivity. Incorporating text prompts offers a promising avenue to more accurately pinpoint lesion locations, yet existing text-guided methods are still hindered by insufficient cross-modal interaction and inadequate cross-modal feature representation. To address these challenges, we propose the text-guided multi-stage cross-perception network (TMC). TMC incorporates a multi-stage cross-attention module (MCM) to enhance the model’s understanding of fine-grained semantic details and a multi-stage alignment loss (MA Loss) to improve the consistency of cross-modal semantics across different feature levels. Experimental results on three public datasets (QaTa-COV19, MosMedData, and Duke-Breast-Cancer-MRI) demonstrate the superior performance of TMC, achieving Dice scores of 84.65%, 78.39%, and 88.09%, respectively, and consistently outperforming both U-Net-based networks and existing text-guided methods.