---
license: cc-by-nc-sa-4.0
---

````markdown
---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- medical
- ct
- segmentation
- lesion-segmentation
---

# ULS+ Training Datasets

This repository contains preprocessed versions of the public datasets used to train the **ULS+** model. These datasets have been standardized for the Universal Lesion Segmentation task, which includes:
1. Cropping a VOI around each lesion  
2. Binarizing the lesion masks  
3. Formatting the dataset into nnUNet format

If you use this data, please ensure you **cite the original authors** listed below.

## Dataset Overview

| Dataset | Type / Location | Original Source |
| :--- | :--- | :--- |
| **ULS23** | Whole Body (Benchmark) | https://uls23.grand-challenge.org/ |
| **AutoPET (MAST)** | Whole Body | https://autopet.grand-challenge.org/Dataset/ |
| **MSD** | Liver, Pancreas, Colon, Lung | http://medicaldecathlon.com/ |
| **WORC** | GIST & Liver Metastases | https://xnat.health-ri.nl/data/projects/worc |
| **CLM** | Colorectal Liver Metastases | https://doi.org/10.7937/QXK2-QG03 |
| **WAW-TACE** | HCC (Liver) | https://doi.org/10.5281/zenodo.11063785 |
| **CECT** | Primary Liver Cancer | https://doi.org/10.57760/sciencedb.12207 |
| **MSWAL** | Abdominal Lesions | https://arxiv.org/abs/2503.13560 |

---

## Citations & References

### ULS23

```bibtex
@article{DeGrauw2025,
  title    = {The ULS23 challenge: A baseline model and benchmark dataset for 3D universal lesion segmentation in computed tomography},
  author   = {M.J.J. {de Grauw} and others},
  journal  = {Medical Image Analysis},
  volume   = {102},
  pages    = {103525},
  year     = {2025},
  doi      = {10.1016/j.media.2025.103525}
}
````

### AutoPET (MAST)

```bibtex
@article{Gatidis2022,
  title     = {A whole-body {FDG-PET/CT} Dataset with manually annotated Tumor Lesions},
  author    = {Gatidis, Sergios and K{\"u}stner, Thomas and others},
  journal   = {Sci. Data},
  volume    = {9},
  number    = {1},
  pages     = {601},
  year      = {2022},
  publisher = {Springer Nature},
  doi       = {10.1038/s41597-022-01718-3}
}
```

### Medical Segmentation Decathlon (MSD)

```bibtex
@article{Antonelli2022,
  title={The Medical Segmentation Decathlon},
  author={Antonelli, Michela and Reinke, Annika and others},
  journal={Nature Communications},
  volume={13},
  pages={4128},
  year={2022},
  doi={10.1038/s41467-022-30695-9}
}
```

### WORC Database

```bibtex
@unpublished{Starmans2021,
  title    = {The {WORC} database: {MRI} and {CT} scans, segmentations, and clinical labels for 930 patients from six radiomics studies},
  author   = {Starmans, Martijn P A and others},
  journal  = {bioRxiv},
  year     = {2021},
  doi      = {10.1101/2021.08.19.21262238}
}
```

### Colorectal Liver Metastases (CLM)

```bibtex
@article{Simpson2024,
  title     = {Preoperative {CT} and survival data for patients undergoing resection of colorectal liver metastases},
  author    = {Simpson, Amber L and others},
  journal   = {Sci. Data},
  volume    = {11},
  pages     = {172},
  year      = {2024},
  doi       = {10.1038/s41597-024-03004-8}
}
```

### WAW-TACE

```bibtex
@article{Bartnik2024,
  title   = {{WAW-TACE}: A Hepatocellular Carcinoma Multiphase {CT} Dataset with Segmentations, Radiomics Features, and Clinical Data},
  author  = {Bartnik, Krzysztof and others},
  journal = {Radiol Artif Intell},
  volume  = {6},
  number  = {6},
  pages   = {e240296},
  year    = {2024},
  doi     = {10.1148/ryai.240296}
}
```

### Primary Liver Cancer CECT

```bibtex
@article{Luo2025,
  title     = {Comprehensive multi-phase {3D} contrast-enhanced {CT} imaging for primary liver cancer},
  author    = {Luo, Jiawei and others},
  journal   = {Sci. Data},
  volume    = {12},
  pages     = {768},
  year      = {2025},
  doi       = {10.1038/s41597-025-05125-2}
}
```

### MSWAL

```bibtex
@incollection{Wu2026,
  title     = {{MSWAL}: {3D} multi-class segmentation of whole abdominal lesions dataset},
  author    = {Wu, Zhaodong and others},
  booktitle = {Lecture Notes in Computer Science},
  pages     = {378--388},
  year      = {2026},
  publisher = {Springer Nature Switzerland}
}
```