---
dataset_info:
  features:
  - name: volume_id
    dtype: int32
  - name: time_id
    dtype: int32
  - name: slice_id
    dtype: int32
  - name: hight
    dtype: float32
  - name: weight
    dtype: float32
  - name: group
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 1100824556.33
    num_examples: 38346
  download_size: 1091284591
  dataset_size: 1100824556.33
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-nc-sa-4.0
task_categories:
- image-classification
language:
- en
tags:
- mri
- medical
- cardiac
- imaging
pretty_name: Automated Cardiac Diagnosis Challenge
size_categories:
- 10K<n<100K
---

# Automated Cardiac Diagnosis Challenge (ACDC)

This dataset contains materials from the *Automated Cardiac Diagnosis Challenge (ACDC)* introduced during MICCAI 2017 by Bernard et al., designed to advance research in **cardiac MRI analysis, representation learning, and automated cardiac disease understanding**.

The dataset includes cine cardiac MRI acquisitions from healthy subjects and patients with multiple cardiac pathologies.


## 🫀 Dataset Description

This repository provides a **2D slice-based version** of the original ACDC cardiac MRI dataset, designed for efficient deep learning and representation learning workflows.

Each entry corresponds to a **single 2D cardiac MRI slice** extracted from a cine cardiac MRI sequence.


## 📦 Dataset Structure

Each dataset entry contains:

- `volume_id` → Unique identifier for the MRI volume/patient  
- `time_id` → Temporal frame index within the cardiac cycle  
- `slice_id` → Slice index within the volume  
- `image` → 2D cardiac MRI slice  
- `width` → Image width  
- `height` → Image height  

## Split
The original dataset marks patients from 1 to 100 as training and 101 to 150 as testing.
In this dataset the train split contains all the patients and it is for the user to decide the split.

## ❤️ Clinical Categories

The original ACDC dataset includes subjects from the following groups:

- Healthy controls (NOR) (20 Training + 10 Testing)
- Myocardial infarction (MINF) (20 Training + 10 Testing)
- Dilated cardiomyopathy (DCM) (20 Training + 10 Testing)
- Hypertrophic cardiomyopathy (HCM) (20 Training + 10 Testing)
- Abnormal right ventricle (RV) (20 Training + 10 Testing)


## Use

```python
from datasets import load_dataset
import matplotlib.pyplot as plt

dataset = load_dataset("chehablaborg/acdc_2d", split="train")

sample_id = 314

image = dataset[sample_id]["image"]
time_id = dataset[sample_id]["time_id"]
slice_id = dataset[sample_id]["slice_id"]

plt.imshow(image, cmap="gray")
plt.title(f"time={time_id}, slice={slice_id}")
plt.axis("off")
plt.show()
```


## 📚 Citation

If you use this dataset, please mention us https://chehablab.com in an acknowledgement and cite the original publication:

```bibtex
@article{bernard2018deep,
  title={Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved?},
  author={Bernard, Olivier and Lalande, Alain and Zotti, Cl{\'e}ment and Cervenansky, Fr{\'e}d{\'e}ric and others},
  journal={IEEE Transactions on Medical Imaging},
  volume={37},
  number={11},
  pages={2514--2525},
  year={2018},
  month={nov},
  doi={10.1109/TMI.2018.2837502}
}
```


### License
This work is licensed under a [Creative Commons CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
[![CC BY-NC-SA 4.0](https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png)](https://creativecommons.org/licenses/by-nc-sa/4.0/)


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