The 3C dataset: A comprehensive dataset for COVID-19 cardiac complications diagnosis.
Authors
Affiliations (5)
Affiliations (5)
- University of Tunis El Manar, Higher Institute of Medical Technologies of Tunis, Laboratory of Biophysics and Medical Technology, Tunis, Tunisia.
- Université de Monastir - Laboratoire Technologie Imagerie Médicale - LTIM-LR12ES06, Faculté de Médecine de Monastir 5019 Monastir, Tunisie.
- Université Paris-Est, Laboratoire d'Informatique Gaspard-Monge, Unité Mixte CNRS-UMLV-ESIEE UMR8049, ESIEE Paris Cité Descartes, BP99, 93162 Noisy Le Grand, France.
- Artificial Intelligence Research Centre, Ajman University, United Arab Emirates.
- Military Hospital of Instruction of Tunis, Tunisia.
Abstract
Coronavirus Disease 2019 (or commonly called COVID-19) infections have been associated with numerous severe cardiovascular complications, including myocardial infarction, myocarditis, and arrhythmias, which pose significant risks to long-term cardiac health. Despite extensive research on COVID-19-related cardiovascular diseases, there remains a shortage of curated, annotated medical imaging datasets to support studies on the early detection or prediction of post-COVID cardiac complications. To address this gap, we introduce the 3C dataset (Cardiac-CT-COVID-19), a public benchmark database of cardiac computed tomography (CT) scans from 134 COVID-19-positive patients, including cases both with and without documented cardiovascular complications. All scans were annotated by two radiologists with expertise in cardiothoracic imaging, incorporating (1) pixel-level delineations of cardiac structures and pathological findings (e.g., myocardial scarring and pericardial effusion), and (2) a normalized severity grading scale for cardiovascular manifestations. The dataset also includes demographic and clinical metadata to support holistic analysis. The 3C dataset is the first to offer detailed, expert-validated annotations that link COVID-19 infection to specific cardiac abnormalities observed in CT scans. The database is expected to provide an excellent benchmark for researchers working on tasks such as automated detection, prediction, and characterization of diverse cardiovascular manifestations secondary to COVID-19.