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:

1. 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.
2. 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.
3. 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 organs
• 3D_data: 3D lesion masks linked to organs
• 3DLAND_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.

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license: cc-by-4.0