# MedCAGD-Dataset-Collection - Medical Image Segmentation Datasets

This repo contains multiple publicly available medical image segmentation (Semantic Segmentation) datasets used for training and evaluation of the segmentation models. Each subfolder corresponds to a specific dataset and follows its original structure or a standardized format used in this project. These datasets are used for benchmarking segmentation performance across multiple medical imaging modalities including CT, MRI, dermoscopy, endoscopy, ultrasound, fundus imaging, and microscopy.

## Citation

If you use this dataset collection or the benchmark results in your research, please cite the following paper:

```bibtex
@inproceedings{wazir2025rethinking,
  title={Rethinking decoder design: Improving biomarker segmentation using depth-to-space restoration and residual linear attention},
  author={Wazir, Saad and Kim, Daeyoung},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={30861--30871},
  year={2025},
  doi = {10.48550/arXiv.2506.18335},
  url = {https://doi.org/10.48550/arXiv.2506.18335}
}
```

## Research Note

This dataset collection provides early access to the datasets used for benchmarking segmentation models across multiple medical imaging datasets. The benchmark results for the methods evaluated on these datasets are currently under review and have not yet been published.
The segmentation benchmarks associated with this dataset collection are part of ongoing research related to the MCADS decoder and the upcoming MedCAGD framework. The full benchmark results and evaluation protocols will appear in the MedCAGD paper, which is currently under review, and additional results will be released after the review process.
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## Included Datasets:

ACDC-2D-Slices
2D cardiac MRI slices from the ACDC dataset used for cardiac structure segmentation.

Synapse Multi-Organ Segmentation Dataset-8 abdominal organs-2D-Slices  
2D slices from the Synapse multi-organ CT segmentation dataset containing annotations for eight abdominal organs.

ThyroidXL  
Thyroid ultrasound dataset for thyroid nodule segmentation.

ISIC2017  
Skin lesion segmentation dataset from the ISIC 2017 challenge.

ISIC2018  
Skin lesion segmentation dataset from the ISIC 2018 challenge.

BKAI  
Gastrointestinal polyp segmentation dataset released by BKAI.

ClinicDB  
Colonoscopy polyp segmentation dataset from the CVC-ClinicDB benchmark.

ColonDB  
Colonoscopy polyp segmentation dataset commonly used for evaluating polyp detection models.

ETIS  
ETIS-Larib polyp dataset containing challenging colonoscopy images with pixel-level annotations.

Kvasir  
Kvasir-SEG dataset for gastrointestinal polyp segmentation.

BUSI  
Breast ultrasound dataset used for tumor segmentation.

DRIVE  
Retinal vessel segmentation dataset from the DRIVE challenge.

FIVES  
Fundus Image Vessel Segmentation dataset for retinal blood vessel analysis.

CHASEDB
Retinal vessel segmentation dataset from the CHASE_DB1 benchmark.

LES-AV  
Dataset for retinal artery and vein segmentation.

STARE  
Retinal vessel segmentation dataset from the STARE benchmark.

UOA-DR  
Diabetic retinopathy dataset with retinal lesion annotations.

CellSeg  
Microscopy cell segmentation dataset used for cell instance or semantic segmentation.

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## Acknowledgement
We gratefully acknowledge the prior contributions of the research community, which have provided the foundation for our framework.


---
license: mit
language:
- en
tags:
- MCADS-Decoder
- medical-image-segmentation
- semantic-segmentation
- computer-vision
- multi-class-segmentation
- Synapse
- ThyroidXL
- ISIC2017
- ISIC2018
- BKAI
- ClinicDB
- ColonDB
- ETIS
- Kvasir
- BUSI
- DRIVE
- FIVES
- CHASEDB
- LES-AV
- STARE
- UOA-DR
- CellSeg

task_categories:
- image-segmentation
---