Enhanced pixel-wise style fusion network for stent malapposition recognition with re-parameterizing technique in OCT

Percutaneous coronary intervention with stent implantation has become a widely used strategy to treat coronary artery disease. Stent malapposition (SM) may increase the risk of late stent thrombosis due to stent tissue coverage reduction, attracting much attention clinically. Recently, optical coherence tomography (OCT) images have been utilized to visually assess the stent apposition/malapposition. However, automated OCT-based SM recognition has been under-explored previously. Therefore, this paper proposes a novel enhanced pixel-wise style fusion network (EPSF-Net) to recognize SM automatically from OCT images. In the EPSF-Net, considering SM information is subtle, we design a novel enhanced pixel-wise style fusion (EPSF) block, which first applies the pixel-wise style pooling to aggregate pixel-wise style context, then enhances pixel-wise style context with multi-scale learning, and finally fuses enhanced pixel-wise style context via a pixel-wise fusion operator. Moreover, the re-parameterizing technique is utilized to reduce the parameters and computational cost of EPSF at the inference stage. Additionally, considering there is no publicly available OCT dataset for SM recognition, we construct an OCT image dataset of SM, named SM-OCT, to validate the effectiveness of our method, which will be available. The extensive experiments on the SM-OCT dataset show that our proposed EPSF-Net achieves better SM recognition performance than state-of-the-art methods. Additionally, two publicly available OCT datasets are employed to verify the generalization of our method.