LAUSR

Intravenous ultrasound (IVUS) produces important imaging information for the clinical medical detection of intravascular lesions. However, most information hidden in the IVUS image must be manually annotated by doctors and obtained by professional parameter calculation tools, which is time-consuming and labor-intensive. The existing IVUS segmentation algorithms generally ignore the positive effect of expert prior knowledge on the segmentation results. We, thus, develop a full-scale IVUS Analysis Toolbox (IAT) that can automatically extract more image information through the image segmentation model of intravascular ultrasound (ISM-IVUS), allowing experts to modify suboptimal segmentation results and support the calculation and acquisition of instantaneous parameters. The ISM-IVUS integrates the expert prior knowledge extraction module. By fusing edge operators based on intensity changes to obtain prior matrices and constraining the morphological features of the segmentation results, the failure of complex IVUS image segmentation can be effectively avoided. The model uses the U-Net framework and attention mechanism, combines deep and shallow features, and reduces the loss of important features in the segmentation process. To ensure the generalization and clinical applicability of the toolbox, we used a large-scale professional dataset provided by the hospital and a public dataset to conduct comparison experiments. The mIoU, the dice index, and the Hausdorff distance of the segmentation results in IAT reached 0.8616, 0.9343, and 6.0799, respectively, which verified the effectiveness of the model. In the comparison experiments of the number of parameters, FLOPs, and robustness, ISM-IVUS also has certain advantages. The correlation and p-value of the calculated parameters also achieved good results. A series of experiments have shown that IAT is in line with clinical needs and provides an application basis for the clinical practice of subsequent IVUS research.