Datasets:
File size: 3,678 Bytes
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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/).
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
[Chehab Lab](https://chehablab.com) @ 2026 |