--- language: - en tags: - medical size_categories: - 1K

## 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](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} } ```