  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 segmentation can greatly improve the usefulness
of these videos by indicating exact position of vessels and instruments, thus
reducing the ambiguity. We propose a real-time segmentation method for these
tasks, based on U-Net network trained in a Siamese architecture from
automatically generated annotations. We make use of noisy low level binary
segmentation and optical flow to generate multi class annotations that are
successively improved in a multistage segmentation approach. We significantly
improve the performance of a state of the art U-Net at the processing speeds of
90fps.
