3DLAND提供超6000例腹部CT的3D病灶标注，助力医疗AI精准定位。
3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset
- 三阶段流程自动生成高精度3D病灶标注
- 覆盖7个器官，超2万例病灶，表面Dice超0.75
- 适合研究病灶定位与跨器官迁移学习的团队使用
现有腹部CT医学影像数据集常缺乏三维标注、多器官覆盖或精确的病灶-器官关联，限制了鲁棒表征学习与临床应用。为弥补这一空白，我们提出3DLAND，一个大规模基准数据集，包含超过6,000例增强型CT体积，拥有超过20,000个高保真3D病灶标注，关联七个腹部器官：肝脏、双肾、胰腺、脾脏、胃和胆囊。我们的三阶段流水线整合了自动化空间推理、提示优化的2D分割与记忆引导的3D传播，经专家放射科医师验证，表面Dice分数超过0.75。通过涵盖多样化的病灶类型与患者人口统计特征，3DLAND支持异常检测、定位及跨器官迁移学习的可扩展评估。该数据集为评估器官感知的3D分割模型设立了新基准，推动面向医疗的AI发展。为促进可复现性与后续研究，3DLAND数据集与实现代码已公开，访问地址为 https://mehrn79.github.io/3DLAND。
原文摘要 · Abstract (English)
Existing medical imaging datasets for abdominal CT often lack three-dimensional annotations, multi-organ coverage, or precise lesion-to-organ associations, hindering robust representation learning and clinical applications. To address this gap, we introduce 3DLAND, a large-scale benchmark dataset comprising over 6,000 contrast-enhanced CT volumes with over 20,000 high-fidelity 3D lesion annotations linked to seven abdominal organs: liver, kidneys, pancreas, spleen, stomach, and gallbladder. Our streamlined three-phase pipeline integrates automated spatial reasoning, prompt-optimized 2D segmentation, and memory-guided 3D propagation, validated by expert radiologists with surface dice scores exceeding 0.75. By providing diverse lesion types and patient demographics, 3DLAND enables scalable evaluation of anomaly detection, localization, and cross-organ transfer learning for medical AI. Our dataset establishes a new benchmark for evaluating organ-aware 3D segmentation models, paving the way for advancements in healthcare-oriented AI. To facilitate reproducibility and further research, the 3DLAND dataset and implementation code are publicly available at https://mehrn79.github.io/3DLAND.
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