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<p>Routine radiology practice documents abnormalities primarily in narrative reports rather than pixel-level annotations. However, many clinically important analyses require spatially localized evidence. This mismatch limits the reuse of large-scale image-report archives for spatially resolved quantitative analysis, as heterogeneous clinical findings cannot be directly translated into consistent spatial representations. We therefore present Clinical-Findings-Guided Segmentation (CF2Seg), a chest X-ray segmentation framework that learns spatial representations directly from full radiology findings. We further curate a multi-source benchmark comprising 53,386 examinations, each paired with expert spatial annotations and corresponding reports across diverse thoracic pathologies and institutions. CF2Seg uses a dual-stage text–image fusion strategy in which report findings guide feature formation and are revisited during decoding to improve boundary delineation across scales. On a unified multi-source benchmark, CF2Seg achieves strong overall performance and remains stable under clinically realistic variation, including distribution shift, annotation scarcity, and clinically realistic image and report perturbations. Beyond segmentation accuracy, CF2Seg-derived masks preserve proportional lesion-burden trends with strong agreement to expert annotations, supporting retrospective spatial annotation and quantitative reuse of large-scale clinical image–report archives.</p>

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Grounding Radiology Report Findings into Medical Image Segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

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  • Suyang Xi,
  • Songtao Hu,
  • Shansong Wang,
  • Hong Ding,
  • Yuheng Li,
  • Wenfeng He,
  • Kuo Zhang,
  • Luke Del Balzo,
  • Chong Zhong,
  • Mingzhe Hu,
  • Catherine C. Liu,
  • Yunhao Liu,
  • Xiaofeng Yang

摘要

Routine radiology practice documents abnormalities primarily in narrative reports rather than pixel-level annotations. However, many clinically important analyses require spatially localized evidence. This mismatch limits the reuse of large-scale image-report archives for spatially resolved quantitative analysis, as heterogeneous clinical findings cannot be directly translated into consistent spatial representations. We therefore present Clinical-Findings-Guided Segmentation (CF2Seg), a chest X-ray segmentation framework that learns spatial representations directly from full radiology findings. We further curate a multi-source benchmark comprising 53,386 examinations, each paired with expert spatial annotations and corresponding reports across diverse thoracic pathologies and institutions. CF2Seg uses a dual-stage text–image fusion strategy in which report findings guide feature formation and are revisited during decoding to improve boundary delineation across scales. On a unified multi-source benchmark, CF2Seg achieves strong overall performance and remains stable under clinically realistic variation, including distribution shift, annotation scarcity, and clinically realistic image and report perturbations. Beyond segmentation accuracy, CF2Seg-derived masks preserve proportional lesion-burden trends with strong agreement to expert annotations, supporting retrospective spatial annotation and quantitative reuse of large-scale clinical image–report archives.