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.

翻译：现有腹部CT医学影像数据集通常缺乏三维标注、多器官覆盖或精确的病灶-器官关联，这阻碍了鲁棒表征学习与临床应用。为填补这一空白，我们提出了3DLAND——一个大规模基准数据集，包含超过6,000例增强CT三维影像，涵盖超过20,000个与七种腹部器官（肝脏、肾脏、胰腺、脾脏、胃和胆囊）相关联的高保真三维病灶标注。我们构建的流线型三阶段流程整合了自动化空间推理、提示优化的二维分割和记忆引导的三维传播，经放射学专家验证其表面骰子系数超过0.75。通过提供多样化的病灶类型与患者群体特征，3DLAND能够支持异常检测、定位及跨器官迁移学习在医疗AI领域的可扩展评估。本数据集为评估器官感知的三维分割模型建立了新基准，为推动医疗健康导向的人工智能发展铺平道路。为促进可复现性与后续研究，3DLAND数据集及实现代码已公开于https://mehrn79.github.io/3DLAND。