U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Multi-modality cardiac imaging plays a key role in the management of patients with
cardiovascular diseases. It allows a combination of complementary anatomical …

Assessment of myocardial viability is essential in diagnosis and treatment management of
patients suffering from myocardial infarction, and classification of pathology on the …

Myocardial pathology segmentation is essential for the diagnosis and treatment of patients
suffering from myocardial infarction. In this work, we propose an end-to-end deep learning …

Background: Myocardial pathology segmentation plays an utmost role in the diagnosis and
treatment of myocardial infarction (MI). However, manual segmentation is time-consuming …

Automated segmentation of cardiac pathology in MRI plays a significant role for diagnosis
and treatment of some cardiac disease. In clinical practice, multi-modality MRI is widely used …

Segmentation of multi-modal myocardial pathology images is a challenging task, due to
factors such as the heterogeneity caused by large inter-modality and intra-modality intensity …

Atherosclerosis is a chronic, progressive disease that primarily affects the arterial walls. It is
one of the major causes of cardiovascular disease. Magnetic Resonance (MR) black-blood …

In recent years, the attention mechanism has received much attention and application in
medical image segmentation. Moreover, many studies have obtained specific results by …

U-net is an image segmentation technique developed primarily for medical image analysis
that can precisely segment images using a scarce amount of training data. These traits …