# 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 — submitted for review to ACM Multimedia 2025.

## 🌐 Overview

- 6,000+ contrast-enhanced CT studies
- 3D lesion masks aligned with 7 abdominal organs
- Prompt-based annotation and propagation pipeline
- Applications: anomaly detection, lesion retrieval, organ-aware analysis

## 🧠 Pipeline

The lesion segmentation pipeline includes:
1. Organ segmentation via MONAI and lesion-to-organ assignment
3. 2D mask generation using SAM prompts
4. 3D mask propagation using MedSAM2


## 📦 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:
- `Phase II/2D_lesion_mask`:2D lesion masks linked to organs
- `Pahse III/3D_lesion_mask`: 3D lesion masks linked to organs
- ` deeplesion_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.


## 📑 Metadata CSV Format

Each lesion in the 3DLAND dataset is described in a structured CSV file that includes key metadata for localization and segmentation. This CSV file links the image slices with their corresponding organ labels and bounding boxes.

### 📄 Sample Columns

| Column         | Description                                                                 |
|----------------|-----------------------------------------------------------------------------|
| `series`       | Series ID from the DeepLesion dataset                                       |
| `slice_range`  | Range of axial slices in which the lesion may appear (according to deeplesion dataset metadata)           |
| `key_slice`    | Central slice with the most visible view of the lesion                     |
| `lesion_id`    | Unique ID assigned to each lesion                                           |
| `matched_organs` | Organ to which the lesion is anatomically linked                          |
| `File_name`    | PNG file name of the key slice (e.g., `000002_02_01_050.png`)               |
| `Bounding_boxes` | Coordinates of the lesion in the key slice: `[x_min, y_min, x_max, y_max]` |

---



## 📁 Folder Structure for Masks

### 📌 Phase II
- **Location:** `Phase II/2D_lesion_mask`
- **Content:** 2D lesion masks for the **key slice**
- **File Naming:** `{series}_{key_slice}.png`

### 📌 Phase III
- **Location:** `Phase III/3D_lesion_mask`
- **Content:** 3D volumetric lesion masks covering the **entire slice range**
- **File Naming:** `{series}_K{key_slice}/{slice number}.png`

---

## ⚠️ Notes on 3D Masks
- The 3D masks in Phase III cover the **full slice range**, but:
  - **Only a few slices near the key slice** contain non-zero segmentation.
  - Slices **far from the key slice** are fully black (all-zero).
- 💡 This design saves space while retaining anatomically relevant data since **abdominal lesions are usually thin** in the axial plane.

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## 📌 Usage Tips
You can use the metadata CSV to:

- 🔍 Locate and crop lesions from the key slice for **training or inference**
- 🧠 Generate **bounding box** or **center point** prompts for segmentation models
- 🧬 Match each lesion to its organ for **multi-organ anomaly analysis**
- 🖼️ Load and visualize corresponding **2D or 3D masks**

---

## 📂 Metadata File Location
- `3DLAND_Info.csv`


---

## 📥 Downloading CT Scans

The original CT scan volumes used in 3DLAND are sourced from the **DeepLesion** dataset.

You can download the CT scans from the official NIH repository:

👉 [Download CT scans from DeepLesion (NIH)](https://nihcc.app.box.com/v/DeepLesion/folder/51877983116)

> ℹ️ These CT volumes are required to visualize the lesion masks or to apply the 3DLAND segmentation pipeline. Make sure to match each `series` in the metadata with the corresponding CT study.


## 📄 License

The dataset and outputs are licensed under **CC BY 4.0**.  
See the full license in the [LICENSE](LICENSE) file or at [creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0/)