提出BRAU-Net模型，提升会阴超声中胎儿头与耻骨联合的分割精度。
Pubic Symphysis-Fetal Head Segmentation Network Using BiFormer Attention Mechanism and Multipath Dilated Convolution
- 采用动态查询感知注意力与多路径空洞卷积，融合局部全局信息。
- 在FH-PS-AoP和HC18数据集上达最佳性能，显著优于传统Transformer方法。
- 适合医学图像分割研究者，尤其关注超声影像分析与注意力机制改进者。
经会阴超声图像中耻骨联合与胎儿头的分割对评估胎儿头部下降程度至关重要。现有基于稀疏注意力机制的Transformer分割方法依赖手工设计的静态模式，在特定数据集上表现差异大。为此，本文提出一种动态、查询感知的稀疏注意力机制，构建名为BRAU-Net的新方法。该方法采用类似U-Net的编码器-解码器结构，结合双层路由注意力与跳跃连接，有效学习局部-全局语义信息。此外，提出反向瓶颈块扩展（IBPE）模块，在上采样过程中减少信息损失。在FH-PS-AoP与HC18数据集上的实验表明，所提方法取得优异分割效果。代码已开源。
原文摘要 · Abstract (English)
Pubic symphysis-fetal head segmentation in transperineal ultrasound images plays a critical role for the assessment of fetal head descent and progression. Existing transformer segmentation methods based on sparse attention mechanism use handcrafted static patterns, which leads to great differences 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.
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