Title: 3DLAND: 3D Lesion Abdominal anomaly Localization Dataset
Primary area: datasets and benchmarks
Keywords: 3D Lesion Segmentation, Anomaly Detection, Medical Imaging Dataset, Abdominal CT scans, Lesion Localization, Multi-Organ Abdominal CT Dataset, Health Care

Abstract: 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.

TL;DR: We introduce the largest publicly available abdominal organ dataset with 3D lesion segmentations, supporting fine-grained organ-level lesion benchmarking across seven abdominal structures.