MAGIC-CT: Multiorgan Annotation and Grounded Image Captioning in CT for Cancer

We present MAGIC-CT, a contrast-enhanced CT dataset of 562 patients with abdominal tumors (liver cysts/cancer, lung metastases, lung cancer, kidney cysts, renal cancer, pancreatic cancer). All 562 patients have CT scans and 3D lesion/organ masks; a subset of 492 patients also have organ-wise, radiologist-authored reports (RU/KZ/EN) totaling 4,937 organ descriptions. The dataset spans 8 pathologies across 4 organs, with about 1,250 annotated lesions and about 500 lesion-linked textual findings, enabling training of multimodal systems that connect volumetric localization to clinical language. MAGIC-CT uniquely integrates volumetric lesion localization, quantitative metrics (e.g., tumor volume, angular involvement of vasculature), and rich semantic context (e.g., "cuff-like encasement of the celiac trunk"), addressing the lack of resources bridging radiological imaging, segmentation, and clinical language. This dataset is expected to enable advancements in AI-driven tumor characterization, automated report generation, and metastasis tracking, with implications for precision oncology.

 

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