# 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.
3. **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.
4. **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
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