Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

The complexity of cardiac electrophysiology, involving dynamic changes in numerous
components across multiple spatial (from ion channel to organ) and temporal (from …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Atrial fibrillation (AF)—the most common arrhythmia—significantly increases the risk of
stroke and heart failure. Although catheter ablation can restore normal heart rhythms …

International challenges have become the standard for validation of biomedical image
analysis methods. Given their scientific impact, it is surprising that a critical analysis of …

In the chronic stage of myocardial infarction, a significant number of patients develop life-
threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled …

One crucial parameter to evaluate the state of the heart after myocardial infarction (MI) is the
viability of the myocardial segment, ie, if the segment recovers its functionality upon …

Accurate computing, analysis and modeling of the ventricles and myocardium from medical
images are important, especially in the diagnosis and treatment management for patients …

There has been a tidal wave of recent interest in artificial intelligence (AI), machine learning
and deep learning approaches in cardiovascular (CV) medicine. In the era of modern …

In recent times, there have been notable changes in cardiovascular medicine, propelled by
the swift advancements in artificial intelligence (AI). The present work provides an overview …