Huang Zhili, Sun Jingyi, Shao Yifan, Wang Zixuan, Wang Su, Li Qiyong, Li Jinsong, Yu Qian
IEEE Trans Med Imaging. 2024 Dec;43(12):4190-4199. doi: 10.1109/TMI.2024.3417007. Epub 2024 Dec 2.
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.
已经提出了几种基于深度学习的方法，用于从血管内光学相干断层扫描（OCT）图像中提取单一类别的易损斑块。然而，由于缺乏带有多类别易损斑块注释的公开可用大规模血管内OCT数据集，进一步的研究受到限制。此外，由于斑块分布不规则、独特的几何形状和模糊边界，多类别易损斑块分割极具挑战性。现有方法尚未充分解决易损斑块的几何特征和空间先验信息。为了解决这些问题，我们收集了一个包含70个回撤数据的数据集，并开发了一个多类别易损斑块分割模型，称为PolarFormer，该模型在空间分布中纳入了易损斑块的先验知识。我们提出的模型的关键模块是Polar Attention，它对易损斑块在径向方向上的空间关系进行建模。在新数据集上进行的大量实验表明，我们提出的方法优于其他基线方法。代码和数据可通过此链接访问：https://github.com/sunjingyi0415/IVOCT-segementaion。
