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# 3DLAND |
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Organ-aware 3D lesion segmentation dataset and pipeline for abdominal CT analysis (ACM Multimedia 2025) |
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# 3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset |
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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*. |
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## 🌐 Overview |
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- 6,000+ contrast-enhanced CT studies |
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- 3D lesion masks aligned with 7 abdominal organs |
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- Prompt-based annotation and propagation pipeline |
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- Applications: anomaly detection, lesion retrieval, organ-aware analysis |
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## 🧠 Pipeline |
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The lesion segmentation pipeline includes: |
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1. Organ segmentation via MONAI |
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2. Lesion-to-organ assignment |
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3. 2D mask generation using SAM prompts |
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4. 3D mask propagation using MedSAM2 |
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## 📦 Dataset |
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The dataset (metadata + mask annotations) is hosted at: |
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👉 [Download via Zenodo](https://zenodo.org/...) *(or your link)* |
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## 🚀 Getting Started |
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```bash |
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git clone https://github.com/yourusername/3DLAND.git |
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cd 3DLAND |
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pip install -r requirements.txt |
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