  In recent years, deep convolution neural networks (DCNNs) have achieved great
prospects in coronary artery vessel segmentation. However, it is difficult to
deploy complicated models in clinical scenarios since high-performance
approaches have excessive parameters and high computation costs. To tackle this
problem, we propose \textbf{LightVessel}, a Similarity Knowledge Distillation
Framework, for lightweight coronary artery vessel segmentation. Primarily, we
propose a Feature-wise Similarity Distillation (FSD) module for semantic-shift
modeling. Specifically, we calculate the feature similarity between the
symmetric layers from the encoder and decoder. Then the similarity is
transferred as knowledge from a cumbersome teacher network to a non-trained
lightweight student network. Meanwhile, for encouraging the student model to
learn more pixel-wise semantic information, we introduce the Adversarial
Similarity Distillation (ASD) module. Concretely, the ASD module aims to
construct the spatial adversarial correlation between the annotation and
prediction from the teacher and student models, respectively. Through the ASD
module, the student model obtains fined-grained subtle edge segmented results
of the coronary artery vessel. Extensive experiments conducted on Clinical
Coronary Artery Vessel Dataset demonstrate that LightVessel outperforms various
knowledge distillation counterparts.
