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 right ventricular assessment is crucial to heart disease diagnosis. Unfortunately, its
segmentation is quite challenging due to its intricate shape, ill‐defined thin edges, large …

In the field of cardiac magnetic resonance (MR) image analysis, the accurate segmentation
of right ventricle (RV) regions plays an important role in the quantitative examination and …

Medical imaging technology has played a vital role in modern medicine and medical care.
Cardiovascular imaging and computing technology are essential for diagnosing and treating …

With the success of U-Net or its variants in automatic medical image segmentation, building
a fully convolutional network (FCN) based on an encoder-decoder structure has become an …

Delineating anatomical structures for cardiac magnetic resonance imaging (CMRI) is crucial
for various medical applications such as medical diagnoses, treatment, and pathological …

The accurate and robust automatic segmentation of cardiac structures in magnetic
resonance imaging (MRI) is significant in calculating cardiac clinical functional indices, and …

Deep neural networks are powerful tools to process medical images, with great potential to
accelerate clinical workflows and facilitate large-scale studies. However, in order to achieve …

Myocardial fibrosis (MF) is a common feature of cardiac disease, characterized by excessive
deposition of collagen (ie, scar tissue) and expansion of the myocardial extracellular volume …

Цифровые технологии стали неотъемлемой частью повседневной жизни. Инновации,
особенно в цифровой сфере, приобретают беспрецедентные масштабы, возможности …