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<p>Developing generalist foundation model has recently attracted tremendous attention in the field of AI for Medicine, which requires open-source medical image datasets that incorporate diverse supervision signals across various imaging modalities. In this paper, we introduce RadGenome-Chest CT, a comprehensive, large-scale, region-guided 3D chest CT interpretation dataset based on CT-RATE. Specifically, we leverage the latest powerful universal segmentation model and large language models, to extend the original datasets from the following aspects: organ-level segmentation masks covering 197 categories, which provide intermediate reasoning visual clues for interpretation; 665K multigranularity grounded reports, where each sentence of the report is linked to the corresponding anatomical region of CT volume with a segmentation mask; 1.2M grounded VQA pairs, where questions and answers are all linked with reference segmentation masks, enabling models to associate visual evidence with textual explanations. We believe that RadGenome-Chest CT can significantly advance the development of multimodal medical foundation models, by training to generate texts based on given segmentation regions, which is unattainable with previous relevant datasets.</p>

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Development of a large-scale grounded vision language dataset for chest CT analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月10日

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  • Xiaoman Zhang,
  • Chaoyi Wu,
  • Ziheng Zhao,
  • Jiayu Lei,
  • Weiwei Tian,
  • Ya Zhang,
  • Weidi Xie,
  • Yanfeng Wang

摘要

Developing generalist foundation model has recently attracted tremendous attention in the field of AI for Medicine, which requires open-source medical image datasets that incorporate diverse supervision signals across various imaging modalities. In this paper, we introduce RadGenome-Chest CT, a comprehensive, large-scale, region-guided 3D chest CT interpretation dataset based on CT-RATE. Specifically, we leverage the latest powerful universal segmentation model and large language models, to extend the original datasets from the following aspects: organ-level segmentation masks covering 197 categories, which provide intermediate reasoning visual clues for interpretation; 665K multigranularity grounded reports, where each sentence of the report is linked to the corresponding anatomical region of CT volume with a segmentation mask; 1.2M grounded VQA pairs, where questions and answers are all linked with reference segmentation masks, enabling models to associate visual evidence with textual explanations. We believe that RadGenome-Chest CT can significantly advance the development of multimodal medical foundation models, by training to generate texts based on given segmentation regions, which is unattainable with previous relevant datasets.