In recent years, several deep learning models have been proposed to accurately quantify
and diagnose cardiac pathologies. These automated tools heavily rely on the accurate …

Combining information from multi-view images is crucial to improve the performance and
robustness of automated methods for disease diagnosis. However, due to the non-alignment …

Cardiac magnetic resonance (CMR) image segmentation is an integral step in the analysis
of cardiac function and diagnosis of heart related diseases. While recent deep learning …

Automated Cardiac Magnetic Resonance segmentation serves as a crucial tool for the
evaluation of cardiac function, facilitating faster clinical assessments that prove …

Diagnosis of cardiovascular disease using automated methods often relies on the critical
task of cardiac image segmentation. We propose a novel strategy that performs …

Cardiac structure segmentation from echocardiogram videos plays a crucial role in
diagnosing heart disease. The combination of multi-view echocardiogram data is essential …

Abstract Convolutional Neural Networks(CNNs) and Transformer-based self-attention
models have become the standard for medical image segmentation. This paper …

Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment
planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS …

We propose a novel multi-stage trans-dimensional architecture for multi-view cardiac image
segmentation. Our method exploits the relationship between long-axis (2D) and short-axis …

In clinical practice of echocardiography examinations, multiple planes containing the heart
structures of different view are usually required in screening, diagnosis and treatment of …