Pubic symphysis-fetal head segmentation in transperineal ultrasound images plays a critical role for the assessment of fetal head descent and progression. Existing transformer \iffalse-based\fi segmentation methods based on sparse attention mechanism use handcrafted static patterns, which leads to great differences \iffalse in \fi in terms of segmentation performance on specific datasets. To address this issue, we introduce a dynamic, query-aware sparse attention mechanism for ultrasound image segmentation. Specifically, we propose a novel method, named BRAU-Net to solve the pubic symphysis-fetal head segmentation task in this paper. The method adopts a U-Net-like encoder-decoder architecture with bi-level routing attention and skip connections, which effectively learns local-global semantic information. In addition, we propose an inverted bottleneck patch expanding (IBPE) module to reduce information loss while performing up-sampling operations. The proposed BRAU-Net is evaluated on FH-PS-AoP and HC18 datasets. The results demonstrate that our method could achieve excellent segmentation results. The code is available on GitHub.

翻译：经会阴超声图像中的耻骨联合-胎头分割对于评估胎头下降与产程进展具有关键作用。现有基于稀疏注意力机制的Transformer分割方法采用人工设计的静态模式，导致其在特定数据集上的分割性能存在显著差异。为解决该问题，我们引入了一种动态、查询感知的稀疏注意力机制用于超声图像分割。具体而言，本文提出一种名为BRAU-Net的新方法来解决耻骨联合-胎头分割任务。该方法采用具有双层路由注意力与跳跃连接的类U-Net编码器-解码器架构，能有效学习局部-全局语义信息。此外，我们提出了一种倒置瓶颈块扩展（IBPE）模块，以在执行上采样操作时减少信息损失。所提出的BRAU-Net在FH-PS-AoP和HC18数据集上进行了评估，结果表明我们的方法能够取得优异的分割效果。代码已在GitHub上开源。