PolarFormer: A Transformer-Based Method for Multi-Lesion Segmentation in Intravascular OCT

Several deep learning-based methods have been proposed to extract vulnerable plaques of a single class from intravascular optical coherence tomography (OCT) images. However, further research is limited by the lack of publicly available large-scale intravascular OCT datasets with multi-class vulnerable plaque annotations. Additionally, multi-class vulnerable plaque segmentation is extremely challenging due to the irregular distribution of plaques, their unique geometric shapes, and fuzzy boundaries. Existing methods have not adequately addressed the geometric features and spatial prior information of vulnerable plaques. To address these issues, we collected a dataset containing 70 pullback data and developed a multi-class vulnerable plaque segmentation model, called PolarFormer, that incorporates the prior knowledge of vulnerable plaques in spatial distribution. The key module of our proposed model is Polar Attention, which models the spatial relationship of vulnerable plaques in the radial direction. Extensive experiments conducted on the new dataset demonstrate that our proposed method outperforms other baseline methods. Code and data can be accessed via this link: https://github.com/sunjingyi0415/IVOCT-segementaion.