Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary
angiography (XCA) images, challenging problems such as inefficient vessel-sparsity …

With convolutional neural networks' remarkable performance in computer vision, more and
more studies are applying deep learning to vessel image segmentation tasks. This work …

Objective: To facilitate the analysis and diagnosis of X-ray coronary angiography in
interventional surgery, it is necessary to extract vessel from X-ray coronary angiography …

This paper proposes an effective method for accurately recovering vessel structures and
intensity information from the X-ray coronary angiography (XCA) images of moving organs …

Coronary artery disease (CAD) is the leading cause of death around the world. One of the
most common imaging methods for diagnosing CAD is the X-ray angiography (XRA) …

Locating the start, apex and end keyframes of moving contrast agents for keyframe counting
in X-ray coronary angiography (XCA) is very important for the diagnosis and treatment of …

Sufficient annotated data is critical to the success of deep learning methods. Annotating for
vessel segmentation in X-ray coronary angiograms is extremely difficult because of the small …

In interventional radiology, short video sequences of vein structure in motion are captured in
order to help medical personnel identify vascular issues or plan intervention. Semantic …

The extraction of contrast-filled vessels from X-ray coronary angiography (XCA) image
sequence has important clinical significance for intuitively diagnosis and therapy. In this …

This letter proposes to extract contrast-filled vessels from overlapped noisy complex
backgrounds in an x-ray coronary angiogram image sequence using low-rank and sparse …