The CUTS Dataset

This is the dataset released along with the publication:

CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation

[ArXiv] [MICCAI 2024] [GitHub]

Citation

If you use this dataset, please cite our paper

@inproceedings{Liu_CUTS_MICCAI2024,
    title = { { CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation } },
    author = { Liu, Chen and Amodio, Matthew and Shen, Liangbo L. and Gao, Feng and Avesta, Arman and Aneja, Sanjay and Wang, Jay C. and Del Priore, Lucian V. and Krishnaswamy, Smita},
    booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
    publisher = {Springer Nature Switzerland},
    volume = {LNCS 15008},
    page = {155–165},
    year = {2024},
    month = {October},
}

Data Directory

The following data directories belong here:

├── berkeley_natural_images
├── brain_tumor
├── brain_ventricles
└── retina

As some background info, I inherited the datasets from a graduated member of the lab when I worked on this project. These datasets are already preprocessed by the time I had them. For reproducibility, I have included the berkeley_natural_images, brain_tumor and retina datasets in zip format in this directory. The brain_ventricles dataset exceeds the GitHub size limits, and can be found on Google Drive.

Please be mindful that these datasets are relatively small in sample size. If big sample size is a requirement, you can look into bigger datasets such as the BraTS challenge.