3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset
This repository accompanies the 3DLAND project — a large-scale, organ-aware 3D lesion segmentation benchmark for abdominal CT scans.
📦 Dataset
We curated over 6,000 contrast-enhanced abdominal CT scans from the publicly available DeepLesion dataset, selecting only those studies that include visible lesions or anomalies in abdominal organs.
To transform these raw scans into a structured, organ-aware 3D segmentation benchmark, we developed a multi-stage pipeline with both automated and expert-in-the-loop components:
- Organ Segmentation: We used MONAI models trained on TotalSegmentator to segment seven abdominal organs — liver, kidneys, pancreas, spleen, stomach, and gallbladder. 1.2. Lesion-to-Organ Assignment: Lesions were matched to the most probable organ based on IoU overlap and 3D proximity, with ambiguous cases reviewed by clinicians.
- 2D Lesion Mask Generation: Using MedSAM1, we generated lesion masks from DeepLesion's bounding boxes. We found that shrinking the box to 70% of its original size, along with a center point prompt, significantly improved segmentation precision.
- 3D Mask Propagation: The resulting 2D masks were propagated across slices using MedSAM2, producing dense 3D segmentations with anatomical continuity.
Each lesion in the dataset is:
- Annotated in 2D on the slice where the lesion is most clearly visible within the CT series
- Localized in 3D across all slices where the lesion is present and discernible
- Assigned to a specific abdominal organ
- Each 3D segmentation mask is saved as a stack of 2D PNG slices, preserving spatial consistency across the volume
The dataset includes:
2D_data:2D lesion masks linked to organs3D_data: 3D lesion masks linked to organs3DLAND_Info.csv: CSV file of Our dataset metadata according to DeepLesion metadata
All annotations underwent clinical review on 10–20% of lesions per organ to ensure high-quality ground truth.