---
task_categories:
- image-segmentation
language:
- es
- en
tags:
- medical
pretty_name: ACDC-Automated Cardiac Diagnosis Challenge
size_categories:
- 1K<n<10K
---

![Screenshot 2023-06-11 at 23.19.31.png](https://s3.amazonaws.com/moonup/production/uploads/6226bae1c8655fec3995a41d/cO9OKcYBO7-MbDZJopF6J.png)

General information

The overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in particular diastolic volume and ejection fraction). The dataset is composed of 150 exams (all from different patients) divided into 5 evenly distributed subgroups (4 pathological plus 1 healthy subject groups) as described below. Furthermore, each patient comes with the following additional information : weight, height, as well as the diastolic and systolic phase instants.

Reference

O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, et al.
"Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved ?" in IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2514-2525, Nov. 2018
doi: 10.1109/TMI.2018.2837502