Organ-aware 3D lesion segmentation dataset and pipeline for abdominal CT analysis (ACM Multimedia 2025 candidate)

3DLAND

Organ-aware 3D lesion segmentation dataset and pipeline for abdominal CT analysis (ACM Multimedia 2025)

3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset

This repository contains the code and dataset instructions for 3DLAND, the first large-scale, organ-aware 3D lesion segmentation benchmark for abdominal CT scans, introduced at 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:

Organ segmentation via MONAI
Lesion-to-organ assignment
2D mask generation using SAM prompts
3D mask propagation using MedSAM2

📦 Dataset

The dataset (metadata + mask annotations) is hosted at:
👉 Download via Zenodo (or your link) This project uses the DeepLesion dataset, publicly available from the NIH Clinical Center. Please note that this dataset is not released under Apache 2.0. Users must ensure compliance with NIH terms of use: https://nihcc.app.box.com/v/DeepLesion

🚀 Getting Started

```bash git clone https://github.com/yourusername/3DLAND.git cd 3DLAND pip install -r requirements.txt

📜 License

Code in this repository is licensed under Apache License 2.0.

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