README.md

---
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
---
# CT_DeepLesion-MedSAM2 Dataset

<div align="center">
 <table align="center">
   <tr>
     <td><a href="https://arxiv.org/abs/2504.03600" target="_blank"><img src="https://img.shields.io/badge/arXiv-Paper-FF6B6B?style=for-the-badge&logo=arxiv&logoColor=white" alt="Paper"></a></td>
     <td><a href="https://medsam2.github.io/" target="_blank"><img src="https://img.shields.io/badge/Project-Page-4285F4?style=for-the-badge&logoColor=white" alt="Project"></a></td>
     <td><a href="https://github.com/bowang-lab/MedSAM2" target="_blank"><img src="https://img.shields.io/badge/GitHub-Code-181717?style=for-the-badge&logo=github&logoColor=white" alt="Code"></a></td>
     <td><a href="https://huggingface.co/wanglab/MedSAM2" target="_blank"><img src="https://img.shields.io/badge/HuggingFace-Model-FFBF00?style=for-the-badge&logo=huggingface&logoColor=white" alt="HuggingFace Model"></a></td>
   </tr>
   <tr>
     <td><a href="https://medsam-datasetlist.github.io/" target="_blank"><img src="https://img.shields.io/badge/Dataset-List-00B89E?style=for-the-badge" alt="Dataset List"></a></td>
     <td><a href="https://huggingface.co/datasets/wanglab/CT_DeepLesion-MedSAM2" target="_blank"><img src="https://img.shields.io/badge/Dataset-CT__DeepLesion-28A745?style=for-the-badge" alt="CT_DeepLesion-MedSAM2"></a></td>
     <td><a href="https://huggingface.co/datasets/wanglab/LLD-MMRI-MedSAM2" target="_blank"><img src="https://img.shields.io/badge/Dataset-LLD--MMRI-FF6B6B?style=for-the-badge" alt="LLD-MMRI-MedSAM2"></a></td>
     <td><a href="https://github.com/bowang-lab/MedSAMSlicer/tree/MedSAM2" target="_blank"><img src="https://img.shields.io/badge/3D_Slicer-Plugin-e2006a?style=for-the-badge" alt="3D Slicer"></a></td>
   </tr>
   <tr>
     <td><a href="https://github.com/bowang-lab/MedSAM2/blob/main/app.py" target="_blank"><img src="https://img.shields.io/badge/Gradio-Demo-F9D371?style=for-the-badge&logo=gradio&logoColor=white" alt="Gradio App"></a></td>
     <td><a href="https://colab.research.google.com/drive/1MKna9Sg9c78LNcrVyG58cQQmaePZq2k2?usp=sharing" target="_blank"><img src="https://img.shields.io/badge/Colab-CT--Seg--Demo-F9AB00?style=for-the-badge&logo=googlecolab&logoColor=white" alt="CT-Seg-Demo"></a></td>
     <td><a href="https://colab.research.google.com/drive/16niRHqdDZMCGV7lKuagNq_r_CEHtKY1f?usp=sharing" target="_blank"><img src="https://img.shields.io/badge/Colab-Video--Seg--Demo-F9AB00?style=for-the-badge&logo=googlecolab&logoColor=white" alt="Video-Seg-Demo"></a></td>
     <td><a href="https://github.com/bowang-lab/MedSAM2?tab=readme-ov-file#bibtex" target="_blank"><img src="https://img.shields.io/badge/Paper-BibTeX-9370DB?style=for-the-badge&logoColor=white" alt="BibTeX"></a></td>
   </tr>
 </table>
</div>


## Authors

<p align="center">
  <a href="https://scholar.google.com.hk/citations?hl=en&user=bW1UV4IAAAAJ&view_op=list_works&sortby=pubdate">Jun Ma</a><sup>* 1,2</sup>, 
  <a href="https://scholar.google.com/citations?user=8IE0CfwAAAAJ&hl=en">Zongxin Yang</a><sup>* 3</sup>, 
  Sumin Kim<sup>2,4,5</sup>, 
  Bihui Chen<sup>2,4,5</sup>, 
  <a href="https://scholar.google.com.hk/citations?user=U-LgNOwAAAAJ&hl=en&oi=sra">Mohammed Baharoon</a><sup>2,3,5</sup>,<br>
  <a href="https://scholar.google.com.hk/citations?user=4qvKTooAAAAJ&hl=en&oi=sra">Adibvafa Fallahpour</a><sup>2,4,5</sup>, 
  <a href="https://scholar.google.com.hk/citations?user=UlTJ-pAAAAAJ&hl=en&oi=sra">Reza Asakereh</a><sup>4,7</sup>, 
  Hongwei Lyu<sup>4</sup>, 
  <a href="https://wanglab.ai/index.html">Bo Wang</a><sup>† 1,2,4,5,6</sup>
</p>

<p align="center">
  <sup>*</sup> Equal contribution &nbsp;&nbsp;&nbsp; <sup>†</sup> Corresponding author
</p>

<p align="center">
  <sup>1</sup>AI Collaborative Centre, University Health Network, Toronto, Canada<br>
  <sup>2</sup>Vector Institute for Artificial Intelligence, Toronto, Canada<br>
  <sup>3</sup>Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, USA<br>
  <sup>4</sup>Peter Munk Cardiac Centre, University Health Network, Toronto, Canada<br>
  <sup>5</sup>Department of Computer Science, University of Toronto, Toronto, Canada<br>
  <sup>6</sup>Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada<br>
  <sup>7</sup>Roche Canada and Genentech
</p>


## About

[DeepLesion](https://nihcc.app.box.com/v/DeepLesion) dataset contains 32,735 diverse lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients. Each lesion has a bounding box annotation on the key slice, which is derived from the longest diameter and longest
perpendicular diameter. We annotated 5000 lesions with [MedSAM2](https://github.com/bowang-lab/MedSAM2) in a human-in-the-loop pipeline. 

```py
# Install required package
pip install datasets

# Load the dataset
from datasets import load_dataset

# Download and load the dataset
dataset = load_dataset("wanglab/CT_DeepLesion-MedSAM2")

# Access the train split
train_dataset = dataset["train"]

# Display the first example
print(train_dataset[0])
```

Please cite both DeepLesion and MedSAM2 when using this dataset. 

```bash
@article{DeepLesion,
  title={DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning},
  author={Yan, Ke and Wang, Xiaosong and Lu, Le and Summers, Ronald M},
  journal={Journal of Medical Imaging},
  volume={5},
  number={3},
  pages={036501--036501},
  year={2018}
}

@article{MedSAM2,
    title={MedSAM2: Segment Anything in 3D Medical Images and Videos},
    author={Ma, Jun and Yang, Zongxin and Kim, Sumin and Chen, Bihui and Baharoon, Mohammed and Fallahpour, Adibvafa and Asakereh, Reza and Lyu, Hongwei and Wang, Bo},
    journal={arXiv preprint arXiv:2504.63609},
    year={2025}
}
```