| --- |
| 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 |
