README.md

---
license: mit
task_categories:
- image-segmentation
language:
- en
tags:
- medical
size_categories:
- n<1K
---


# 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]](https://arxiv.org/pdf/2209.11359) [[MICCAI 2024]](https://link.springer.com/chapter/10.1007/978-3-031-72111-3_15) [[GitHub]](https://github.com/ChenLiu-1996/CUTS)

## 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](https://drive.google.com/file/d/1TB5Zu3J4UbEleJUuNf-h1AymOn1jOoQe/view?usp=sharing).

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.