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<p>Lung cancer outcomes depend on early detection and accurate lesion delineation, yet conventional segmentation methods remain clinically detached by yielding scanner sensitive pixel masks that do not align with radiologist language or reporting standards. To address this limitation, we propose BiomedLoop, a text guided framework that integrates semantic descriptions with spatial quantification to mirror routine diagnostic practice. Our pipeline couples localization via fine-tuned Grounding DINO and refinement using SEEM, which is enhanced by a novel Uncertainty Aware Feature Modulator to ensure boundary sensitive representation. A core innovation involves converting mask derived geometric descriptors into structured pseudo text prompts to fine tune the localization pathway, enabling supervision even on datasets without native radiology reports. Additionally, the system outputs structured reports compliant with the TID 1500 specification. Extensive experiments across five public benchmarks demonstrate that BiomedLoop yields elevated Dice similarity coefficients and consistently lower Hausdorff distances relative to both conventional CNN architectures and Segment Anything Model variants. Collectively, these results show that systematic semantic spatial joint modeling successfully bridges the critical disconnect between traditional segmentation and clinical utility in resource limited settings.</p>

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Closed loop text guided framework for lung cancer lesion segmentation and quantification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月12日

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  • Shiyang Wang,
  • Ziyi Wang,
  • Wanfu Men,
  • Zhenyu Song,
  • Dayu Hu,
  • Tianyu Liu,
  • Boyang Wang,
  • Dexing Kong,
  • Xuehao Li,
  • Kaiming Ren,
  • Mingrui Shao

摘要

Lung cancer outcomes depend on early detection and accurate lesion delineation, yet conventional segmentation methods remain clinically detached by yielding scanner sensitive pixel masks that do not align with radiologist language or reporting standards. To address this limitation, we propose BiomedLoop, a text guided framework that integrates semantic descriptions with spatial quantification to mirror routine diagnostic practice. Our pipeline couples localization via fine-tuned Grounding DINO and refinement using SEEM, which is enhanced by a novel Uncertainty Aware Feature Modulator to ensure boundary sensitive representation. A core innovation involves converting mask derived geometric descriptors into structured pseudo text prompts to fine tune the localization pathway, enabling supervision even on datasets without native radiology reports. Additionally, the system outputs structured reports compliant with the TID 1500 specification. Extensive experiments across five public benchmarks demonstrate that BiomedLoop yields elevated Dice similarity coefficients and consistently lower Hausdorff distances relative to both conventional CNN architectures and Segment Anything Model variants. Collectively, these results show that systematic semantic spatial joint modeling successfully bridges the critical disconnect between traditional segmentation and clinical utility in resource limited settings.