Wen Shen, Zhang Dong, Lei Yuting, Yang Yan
School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
Sci Rep. 2025 Jul 1;15(1):21970. doi: 10.1038/s41598-025-08711-x.
Ultrasound guidance images are widely used for high intensity focused ultrasound (HIFU) therapy; however, the speckles, acoustic shadows, and signal attenuation in ultrasound guidance images hinder the observation of the images by radiologists and make segmentation of ultrasound guidance images more difficult. To address these issues, we proposed the superpixel based attention network, a network integrating superpixels and self-attention mechanisms that can automatically segment tumor regions in ultrasound guidance images. The method is implemented based on the framework of region splitting and merging. The ultrasound guidance image is first over-segmented into superpixels, then features within the superpixels are extracted and encoded into superpixel feature matrices with the uniform size. The network takes superpixel feature matrices and their positional information as input, and classifies superpixels using self-attention modules and convolutional layers. Finally, the superpixels are merged based on the classification results to obtain the tumor region, achieving automatic tumor region segmentation. The method was applied to a local dataset consisting of 140 ultrasound guidance images from uterine fibroid HIFU therapy. The performance of the proposed method was quantitatively evaluated by comparing the segmentation results with those of the pixel-wise segmentation networks. The proposed method achieved 75.95% and 7.34% in mean intersection over union (IoU) and mean normalized Hausdorff distance (NormHD). In comparison to the segmentation transformer (SETR), this represents an improvement in performance by 5.52% for IoU and 1.49% for NormHD. Paired t-tests were conducted to evaluate the significant difference in IoU and NormHD between the proposed method and the comparison methods. All p-values of the paired t-tests were found to be less than 0.05. The analysis of evaluation metrics and segmentation results indicates that the proposed method performs better than existing pixel-wise segmentation networks in segmenting the tumor region on ultrasound guidance images.
超声引导图像广泛应用于高强度聚焦超声（HIFU）治疗；然而，超声引导图像中的斑点、声影和信号衰减阻碍了放射科医生对图像的观察，并使超声引导图像的分割更加困难。为了解决这些问题，我们提出了基于超像素的注意力网络，这是一种集成了超像素和自注意力机制的网络，能够自动分割超声引导图像中的肿瘤区域。该方法基于区域分裂合并框架实现。首先将超声引导图像过度分割为超像素，然后提取超像素内的特征并编码为大小统一的超像素特征矩阵。该网络将超像素特征矩阵及其位置信息作为输入，使用自注意力模块和卷积层对超像素进行分类。最后，根据分类结果合并超像素以获得肿瘤区域，实现肿瘤区域的自动分割。该方法应用于一个局部数据集，该数据集由140张来自子宫肌瘤HIFU治疗的超声引导图像组成。通过将分割结果与逐像素分割网络的结果进行比较，对所提方法的性能进行了定量评估。所提方法在平均交并比（IoU）和平均归一化豪斯多夫距离（NormHD）方面分别达到了75.95%和7.34%。与分割变换器（SETR）相比，IoU性能提高了5.52%，NormHD性能提高了1.49%。进行配对t检验以评估所提方法与比较方法在IoU和NormHD方面的显著差异。发现配对t检验的所有p值均小于0.05。评估指标和分割结果分析表明，所提方法在分割超声引导图像中的肿瘤区域方面比现有的逐像素分割网络表现更好。
