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README.md

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-# 3DLAND
-<<<<<<< HEAD
-Organ-aware 3D lesion segmentation dataset and pipeline for abdominal CT analysis (ACM Multimedia 2025)
+<h1 align="center">3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset</h1>
 
-# 3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset
+<div align="center">
 
-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*.
+![visitors](https://visitor-badge.laobi.icu/badge?page_id=mehran.advand/3DLAND&left_color=%234CAF50&right_color=%23FFC107)
+[![GitHub Repo stars](https://img.shields.io/github/stars/mehran.advand/3DLAND?style=social)](https://git.dml.ir/mehran.advand/3DLAND/stargazers)
+<a href="https://twitter.com/bodymaps317">
+        <img src="https://img.shields.io/twitter/follow/BodyMaps?style=social" alt="Follow on Twitter" />
+</a><br/>
+**Subscribe for updates: [3DLAND Google Group](https://groups.google.com/g/3dland-dataset)**
 
-## 🌐 Overview
+</div>
 
-- 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
+We introduce **3DLAND**, the first large-scale, **organ-aware** 3D lesion segmentation dataset for contrast-enhanced **abdominal CT scans**. 
 
-## 🧠 Pipeline
+- 📦 **6,000+** CT volumes  
+- 🧠 3D Lesions labeled across **7 abdominal organs**: liver, kidneys, spleen, pancreas, stomach, gallbladder  
+- 🤖 Built using a **prompt-driven**, expert-verified segmentation pipeline  
+- ⚙️ Designed for tasks such as anomaly detection, lesion retrieval, and multimedia-driven clinical AI  
 
-The lesion segmentation pipeline includes:
-1. Organ segmentation via MONAI
-2. Lesion-to-organ assignment
-3. 2D mask generation using SAM prompts
-4. 3D mask propagation using MedSAM2
+This level of structured annotation would traditionally take **25+ years** of expert effort — 3DLAND accomplishes it in weeks using automated reasoning and deep learning.
 
-## 📦 Dataset
-
-The dataset (metadata + mask annotations) is hosted at:  
-👉 [Download via Zenodo](https://zenodo.org/...) *(or your link)*
-
-## 📄 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/)
-
-## 🚀 Getting Started
-
-```bash
-git clone https://github.com/yourusername/3DLAND.git
-cd 3DLAND
-pip install -r requirements.txt
-
-=======
-
-Organ-aware 3D lesion segmentation dataset and pipeline for abdominal CT analysis (ACM Multimedia 2025 candidate)
->>>>>>> c4a39cda4d97821fccd529e0c809c83af1abfc1c
+<p align="center"><img width="90%" src="assets/3DLAND_pipeline_overview.jpg" /></p>
+<p align="center"><img width="100%" src="assets/3DLAND_organs_chart.jpg" /></p>
+<p align="center"><img width="60%" src="assets/3DLAND_prompt_strategy.png" /></p>