  Coronary angiography is an indispensable assistive technique for cardiac
interventional surgery. Segmentation and extraction of blood vessels from
coronary angiography videos are very essential prerequisites for physicians to
locate, assess and diagnose the plaques and stenosis in blood vessels. This
article proposes a new video segmentation framework that can extract the
clearest and most comprehensive coronary angiography images from a video
sequence, thereby helping physicians to better observe the condition of blood
vessels. This framework combines a 3D convolutional layer to extract
spatial--temporal information from a video sequence and a 2D CE--Net to
accomplish the segmentation task of an image sequence. The input is a few
continuous frames of angiographic video, and the output is a mask of
segmentation result. From the results of segmentation and extraction, we can
get good segmentation results despite the poor quality of coronary angiography
video sequences.
