CT_DeepLesion-MedSAM2 Dataset

Authors

Jun Ma^{* 1,2}, Zongxin Yang^{* 3}, Sumin Kim^2,4,5, Bihui Chen^2,4,5, Mohammed Baharoon^2,3,5,
Adibvafa Fallahpour^2,4,5, Reza Asakereh^4,7, Hongwei Lyu⁴, Bo Wang^{† 1,2,4,5,6}

^* Equal contribution     ^† Corresponding author

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

About

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 in a human-in-the-loop pipeline.

# 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.

@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}
}