  Developing generalist foundation model has recently attracted tremendous
attention among researchers in the field of AI for Medicine (AI4Medicine). A
pivotal insight in developing these models is their reliance on dataset
scaling, which emphasizes the requirements on developing 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 and large language models, to extend the original datasets (over
25,692 non-contrast 3D chest CT volume and reports from 20,000 patients) from
the following aspects: (i) organ-level segmentation masks covering 197
categories, which provide intermediate reasoning visual clues for
interpretation; (ii) 665 K multi-granularity grounded reports, where each
sentence of the report is linked to the corresponding anatomical region of CT
volume in the form of a segmentation mask; (iii) 1.3 M grounded VQA pairs,
where questions and answers are all linked with reference segmentation masks,
enabling models to associate visual evidence with textual explanations. All
grounded reports and VQA pairs in the validation set have gone through manual
verification to ensure dataset quality. 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. We will release all segmentation
masks, grounded reports, and VQA pairs to facilitate further research and
development in this field.
