  This paper develops a novel encoder-decoder deep network architecture which
exploits the several contextual frames of 2D+t sequential images in a sliding
window centered at current frame to segment 2D vessel masks from the current
frame. The architecture is equipped with temporal-spatial feature extraction in
encoder stage, feature fusion in skip connection layers and channel attention
mechanism in decoder stage. In the encoder stage, a series of 3D convolutional
layers are employed to hierarchically extract temporal-spatial features. Skip
connection layers subsequently fuse the temporal-spatial feature maps and
deliver them to the corresponding decoder stages. To efficiently discriminate
vessel features from the complex and noisy backgrounds in the XCA images, the
decoder stage effectively utilizes channel attention blocks to refine the
intermediate feature maps from skip connection layers for subsequently decoding
the refined features in 2D ways to produce the segmented vessel masks.
Furthermore, Dice loss function is implemented to train the proposed deep
network in order to tackle the class imbalance problem in the XCA data due to
the wide distribution of complex background artifacts. Extensive experiments by
comparing our method with other state-of-the-art algorithms demonstrate the
proposed method's superior performance over other methods in terms of the
quantitative metrics and visual validation. The source codes are at
https://github.com/Binjie-Qin/SVS-net
