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Datasets/ACDC

ACDC

Automated Cardiac Diagnosis Challenge

BiomedicalImagesCC BY-NC-SA 4.0Introduced 2021-09-15

The goal of the Automated Cardiac Diagnosis Challenge (ACDC) challenge is to:

  • compare the performance of automatic methods on the segmentation of the left ventricular endocardium and epicardium as the right ventricular endocardium for both end diastolic and end systolic phase instances;
  • compare the performance of automatic methods for the classification of the examinations in five classes (normal case, heart failure with infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle).

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.

The database is made available to participants through two datasets from the dedicated online evaluation website after a personal registration: i) a training dataset of 100 patients along with the corresponding manual references based on the analysis of one clinical expert; ii) a testing dataset composed of 50 new patients, without manual annotations but with the patient information given above. The raw input images are provided through the Nifti format.

Source: Automated Cardiac Diagnosis Challenge

Image source: Automated Cardiac Diagnosis Challenge

Benchmarks

10-shot image generation/FIDMedical Image Generation/FIDMedical Image Segmentation/Dice Score

Related Benchmarks

ACDC (Adverse Conditions Dataset with Correspondences)/10-shot image generation/mIoUACDC (Adverse Conditions Dataset with Correspondences)/Foggy Scene Segmentation/mIoUACDC (Adverse Conditions Dataset with Correspondences)/Semantic Segmentation/mIoUACDC (Adverse Conditions Dataset with Correspondences)/Unsupervised Semantic Segmentation/mIoUACDC 10% labeled data/Medical Image Segmentation/Dice (Average)ACDC 20% labeled data/Medical Image Segmentation/Dice (Average)ACDC 5% labeled data/Medical Image Segmentation/Dice (Average)ACDC Scribbles/10-shot image generation/Dice (Average)ACDC Scribbles/Semantic Segmentation/Dice (Average)

Statistics

Papers
52
Benchmarks
3

Links

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Tasks

10-shot image generationDiffeomorphic Medical Image RegistrationMedical Image GenerationMedical Image SegmentationSemi-supervised Medical Image Segmentation