BiomedParseData

# **BiomedParseData**

This is the official dataset repository for "A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities".

[[`Code`](https://github.com/microsoft/BiomedParse)] [[`Paper`](https://aka.ms/biomedparse-paper)] [[`Demo`](https://microsoft.github.io/BiomedParse/)] [[`Model`](https://huggingface.co/microsoft/BiomedParse)] [[`Data`](https://huggingface.co/datasets/microsoft/BiomedParseData)]

We processed from the below public segmentation datasets, and host a subset of our processed datasets as ZIP files here. Each instance include a 1024x1024 PNG image, a list of textual description for the segmentation target, and a binary groundtruth mask also in 1024x1024 PNG.

You are welcome to use any subset of the datasets to train or evaluate BiomedParse, as well as develop your new model. Please cite our paper and the original dataset that you used.

Zhao, T., Gu, Y., Yang, J. et al. A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities. Nat Methods 22, 166–176 (2025). https://doi.org/10.1038/s41592-024-02499-w

```
@article{zhao2025foundation,
title={A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities},
author={Zhao, Theodore and Gu, Yu and Yang, Jianwei and Usuyama, Naoto and Lee, Ho Hin and Kiblawi, Sid and Naumann, Tristan and Gao, Jianfeng and Crabtree, Angela and Abel, Jacob and others},
journal={Nature methods},
volume={22},
number={1},
pages={166--176},
year={2025},
publisher={Nature Publishing Group US New York}
}
```

BiomedParseData was created from preprocessing publicly available biomedical image segmentation datasets. These datasets are provided pre-formatted for convenience. For additional information about the datasets or their licenses, please reach out to the owners:
| Dataset | URL |
|---------------------------------------|-----|
| amos22 | [https://amos22.grand-challenge.org/](https://amos22.grand-challenge.org/) |
| MSD (Medical Segmentation Decathlon) | [http://medicaldecathlon.com/](http://medicaldecathlon.com/) |
| KiTS23 | [https://github.com/neheller/kits23](https://github.com/neheller/kits23) |
| BTCV | [https://www.synapse.org/#!Synapse:syn3193805/wiki/217790](https://www.synapse.org/#!Synapse:syn3193805/wiki/217790) |
| COVID-19 CT | [https://www.kaggle.com/datasets/andrewmvd/covid19-ct-scans](https://www.kaggle.com/datasets/andrewmvd/covid19-ct-scans) |
| LIDR-IDRI | [https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI) |
| ACDC | [https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html](https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html) |
| M&Ms | [https://www.ub.edu/mnms/](https://www.ub.edu/mnms/) |
| PROMISE12 | [cite https://doi.org/10.1016/j.media.2013.12.002](https://doi.org/10.1016/j.media.2013.12.002) |
| LGG | [https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation) |
| COVID-QU-Ex | [https://www.kaggle.com/datasets/anasmohammedtahir/covidqu](https://www.kaggle.com/datasets/anasmohammedtahir/covidqu) |
| QaTa-COV19 | [https://www.kaggle.com/datasets/aysendegerli/qatacov19-dataset](https://www.kaggle.com/datasets/aysendegerli/qatacov19-dataset) |
| SIIM-ACR Pneumothorax Segmentation | [https://www.kaggle.com/datasets/vbookshelf/pneumothorax-chest-xray-images-and-masks](https://www.kaggle.com/datasets/vbookshelf/pneumothorax-chest-xray-images-and-masks) |
| Chest Xray Masks and Labels Dataset | [https://datasetninja.com/chest-xray](https://datasetninja.com/chest-xray) |
| COVID-19 Radiography Database | [https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database) |
| CAMUS | [https://www.creatis.insa-lyon.fr/Challenge/camus/index.html](https://www.creatis.insa-lyon.fr/Challenge/camus/index.html) |
| BUSI | [https://scholar.cu.edu.eg/?q=afahmy/pages/dataset](https://scholar.cu.edu.eg/?q=afahmy/pages/dataset) |
| FH-PS-AOP | [https://zenodo.org/records/7851339#.ZEH6eHZBztU](https://zenodo.org/records/7851339#.ZEH6eHZBztU) |
| CDD-CESM | [https://www.cancerimagingarchive.net/collection/cdd-cesm/](https://www.cancerimagingarchive.net/collection/cdd-cesm/) |
| PolypGen | [https://www.synapse.org/#!Synapse:syn26376615/wiki/613312](https://www.synapse.org/#!Synapse:syn26376615/wiki/613312) |
| NeoPolyp | [https://www.kaggle.com/c/bkai-igh-neopolyp/data](https://www.kaggle.com/c/bkai-igh-neopolyp/data) |
| ISIC 2018 | [https://challenge2018.isic-archive.com/task1/](https://challenge2018.isic-archive.com/task1/) |
| UwaterlooSkinCancer | [Skin Cancer Detection \| Vision and Image Processing Lab \| University of Waterloo](https://uwaterloo.ca) |
| OCT-CME | [https://www.kaggle.com/datasets/zeeshanahmed13/intraretinal-cystoid-fluid](https://www.kaggle.com/datasets/zeeshanahmed13/intraretinal-cystoid-fluid) |
| REFUGE | [https://bitbucket.org/woalsdnd/refuge/src](https://bitbucket.org/woalsdnd/refuge/src) |
| G1020 | [https://www.dfki.uni-kl.de/g1020](https://www.dfki.uni-kl.de/g1020) |
| DRIVE | [https://drive.grand-challenge.org/](https://drive.grand-challenge.org/) |
| GlaS | [https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/](https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/) |
| PanNuke | [https://jgamper.github.io/PanNukeDataset/](https://jgamper.github.io/PanNukeDataset/) |
| FUMPE | [https://figshare.com/collections/FUMPE/4107803/1](https://figshare.com/collections/FUMPE/4107803/1) |
| TotalSegmentator | [https://github.com/wasserth/TotalSegmentator](https://github.com/wasserth/TotalSegmentator) |
| BraTS2023 | [https://www.synapse.org/#!Synapse:syn51156910/wiki/621282](https://www.synapse.org/#!Synapse:syn51156910/wiki/621282) |
| AbdomenCT-1K | [https://github.com/JunMa11/AbdomenCT-1K](https://github.com/JunMa11/AbdomenCT-1K) |
| US Simulation & Segmentation | [https://www.kaggle.com/datasets/ignaciorlando/ussimandsegm](https://www.kaggle.com/datasets/ignaciorlando/ussimandsegm) |
| CDD-CESM | [https://www.cancerimagingarchive.net/collection/cdd-cesm/](https://www.cancerimagingarchive.net/collection/cdd-cesm/) |

# **BiomedParseData**
本仓库为《面向九模态生物医学对象联合分割、检测与识别的基础模型》的官方数据集仓库。

「[代码](https://github.com/microsoft/BiomedParse)」「[论文](https://aka.ms/biomedparse-paper)」「[演示](https://microsoft.github.io/BiomedParse/)」「[模型](https://huggingface.co/microsoft/BiomedParse)」「[数据集](https://huggingface.co/datasets/microsoft/BiomedParseData)」

本数据集基于下述公开分割数据集预处理得到，我们在此以ZIP压缩包形式托管部分预处理后的数据集子集。每个数据样本均包含一张分辨率为1024×1024的PNG图像、一份用于描述分割目标的文本列表，以及一张同样为1024×1024分辨率的PNG格式二进制真值掩码（groundtruth mask）。

欢迎使用本数据集的任意子集训练或评估BiomedParse模型，亦可基于此开发全新的模型。使用本数据集时，请引用本文及所使用的原始数据集。

Zhao, T., Gu, Y., Yang, J. 等. 面向九模态生物医学对象联合分割、检测与识别的基础模型. 《自然·方法学》（Nature Methods）, 22, 166–176 (2025). https://doi.org/10.1038/s41592-024-02499-w

@article{zhao2025foundation,
title={面向九模态生物医学对象联合分割、检测与识别的基础模型},
author={Zhao, Theodore and Gu, Yu and Yang, Jianwei and Usuyama, Naoto and Lee, Ho Hin and Kiblawi, Sid and Naumann, Tristan and Gao, Jianfeng and Crabtree, Angela and Abel, Jacob and others},
journal={自然·方法学（Nature Methods）},
volume={22},
number={1},
pages={166--176},
year={2025},
publisher={Nature Publishing Group US New York}
}

BiomedParseData数据集由公开可用的生物医学图像分割数据集预处理生成。为方便使用，我们提供了预格式化的数据集版本。若需了解数据集相关的更多信息或其授权协议，请联系数据集原所有者：

| 数据集名称 | 链接地址 |
|---------------------------------------|-----|
| amos22 | [https://amos22.grand-challenge.org/](https://amos22.grand-challenge.org/) |
| MSD（Medical Segmentation Decathlon，医学分割十项全能挑战） | [http://medicaldecathlon.com/](http://medicaldecathlon.com/) |
| KiTS23 | [https://github.com/neheller/kits23](https://github.com/neheller/kits23) |
| BTCV | [https://www.synapse.org/#!Synapse:syn3193805/wiki/217790](https://www.synapse.org/#!Synapse:syn3193805/wiki/217790) |
| COVID-19 CT | [https://www.kaggle.com/datasets/andrewmvd/covid19-ct-scans](https://www.kaggle.com/datasets/andrewmvd/covid19-ct-scans) |
| LIDR-IDRI | [https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI) |
| ACDC | [https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html](https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html) |
| M&Ms | [https://www.ub.edu/mnms/](https://www.ub.edu/mnms/) |
| PROMISE12 | [cite https://doi.org/10.1016/j.media.2013.12.002](https://doi.org/10.1016/j.media.2013.12.002) |
| LGG | [https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation) |
| COVID-QU-Ex | [https://www.kaggle.com/datasets/anasmohammedtahir/covidqu](https://www.kaggle.com/datasets/anasmohammedtahir/covidqu) |
| QaTa-COV19 | [https://www.kaggle.com/datasets/aysendegerli/qatacov19-dataset](https://www.kaggle.com/datasets/aysendegerli/qatacov19-dataset) |
| SIIM-ACR Pneumothorax Segmentation | [https://www.kaggle.com/datasets/vbookshelf/pneumothorax-chest-xray-images-and-masks](https://www.kaggle.com/datasets/vbookshelf/pneumothorax-chest-xray-images-and-masks) |
| Chest Xray Masks and Labels Dataset | [https://datasetninja.com/chest-xray](https://datasetninja.com/chest-xray) |
| COVID-19 Radiography Database | [https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database) |
| CAMUS | [https://www.creatis.insa-lyon.fr/Challenge/camus/index.html](https://www.creatis.insa-lyon.fr/Challenge/camus/index.html) |
| BUSI | [https://scholar.cu.edu.eg/?q=afahmy/pages/dataset](https://scholar.cu.edu.eg/?q=afahmy/pages/dataset) |
| FH-PS-AOP | [https://zenodo.org/records/7851339#.ZEH6eHZBztU](https://zenodo.org/records/7851339#.ZEH6eHZBztU) |
| CDD-CESM | [https://www.cancerimagingarchive.net/collection/cdd-cesm/](https://www.cancerimagingarchive.net/collection/cdd-cesm/) |
| PolypGen | [https://www.synapse.org/#!Synapse:syn26376615/wiki/613312](https://www.synapse.org/#!Synapse:syn26376615/wiki/613312) |
| NeoPolyp | [https://www.kaggle.com/c/bkai-igh-neopolyp/data](https://www.kaggle.com/c/bkai-igh-neopolyp/data) |
| ISIC 2018 | [https://challenge2018.isic-archive.com/task1/](https://challenge2018.isic-archive.com/task1/) |
| UwaterlooSkinCancer | [皮肤癌检测 | 滑铁卢大学视觉与图像处理实验室](https://uwaterloo.ca) |
| OCT-CME | [https://www.kaggle.com/datasets/zeeshanahmed13/intraretinal-cystoid-fluid](https://www.kaggle.com/datasets/zeeshanahmed13/intraretinal-cystoid-fluid) |
| REFUGE | [https://bitbucket.org/woalsdnd/refuge/src](https://bitbucket.org/woalsdnd/refuge/src) |
| G1020 | [https://www.dfki.uni-kl.de/g1020](https://www.dfki.uni-kl.de/g1020) |
| DRIVE | [https://drive.grand-challenge.org/](https://drive.grand-challenge.org/) |
| GlaS | [https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/](https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/) |
| PanNuke | [https://jgamper.github.io/PanNukeDataset/](https://jgamper.github.io/PanNukeDataset/) |
| FUMPE | [https://figshare.com/collections/FUMPE/4107803/1](https://figshare.com/collections/FUMPE/4107803/1) |
| TotalSegmentator | [https://github.com/wasserth/TotalSegmentator](https://github.com/wasserth/TotalSegmentator) |
| BraTS2023 | [https://www.synapse.org/#!Synapse:syn51156910/wiki/621282](https://www.synapse.org/#!Synapse:syn51156910/wiki/621282) |
| AbdomenCT-1K | [https://github.com/JunMa11/AbdomenCT-1K](https://github.com/JunMa11/AbdomenCT-1K) |
| US Simulation & Segmentation | [https://www.kaggle.com/datasets/ignaciorlando/ussimandsegm](https://www.kaggle.com/datasets/ignaciorlando/ussimandsegm) |
| CDD-CESM | [https://www.cancerimagingarchive.net/collection/cdd-cesm/](https://www.cancerimagingarchive.net/collection/cdd-cesm/) |