MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Paper
•
2503.13560
•
Published
Datasets:
ArXiv:
License:
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
---
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)
@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)
@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
@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)
@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
@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
@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
@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}
}
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