README.md

metadata

license: apache-2.0
task_categories:
  - image-segmentation
tags:
  - medical
  - biology

ACDC-PNG Dataset

Paper | Code

This repository contains a convenient PNG-formatted version of the ACDC dataset, primarily intended for semi-supervised medical image segmentation tasks. This version was converted from the files provided in the SSL4MIS repository.

It is used and introduced in the paper: VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation.

Dataset Structure

The data is organized as follows:

XXX/
│
├── train-label/                # Labeled training set
│   ├── image/                  # Input images (.png)
│   └── mask/                   # Corresponding segmentation masks (.png)
│
├── train-unlabel/              # Unlabeled training set
│   └── image/                  # Images without ground truth masks
│   └── mask/                  
│
├── val/                        # Validation set
│   ├── image/                  # Validation images (.png)
│   └── mask/                   # Validation masks (.png)
│
└── test/                       # Test set
    ├── image/                  # Test images (.png)
    └── mask/                   # Test masks (optional)

• train-label: Paired image–mask samples used for supervised segmentation training.
• train-unlabel: Images without ground-truth annotations, utilized for semi-supervised learning.
• val: Used to monitor and validate model performance during training.
• test: Used for final evaluation and benchmarking.

Citation

If you use this dataset or the VQ-Seg method in your research, please cite the following:

@inproceedings{yangvq,
  title={VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation},
  author={Yang, Sicheng and Xing, Zhaohu and Zhu, Lei},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
}

Please also make sure to cite the original ACDC paper.