使用BiFormer注意力机制和多路径膨胀卷积的耻骨联合-胎头分割网络
Pubic Symphysis-Fetal Head Segmentation Network Using BiFormer Attention Mechanism and Multipath Dilated Convolution
AI总结：
针对超声图像中耻骨联合-胎头分割任务，提出基于双层路由注意力和倒置瓶颈块扩展模块的BRAU-Net网络，有效学习局部全局语义信息并减少上采样信息丢失，在FH-PS-AoP和HC18数据集上取得优异分割效果。
摘要：
经会阴超声图像中的耻骨联合-胎头分割在评估胎头下降和进展中起着关键作用。现有的基于稀疏注意力机制的transformer分割方法使用手工制作的静态模式，导致在特定数据集上的分割性能存在很大差异。为了解决这个问题，我们引入了一种动态的、查询感知的稀疏注意力机制用于超声图像分割。具体来说，我们提出了一种名为BRAU-Net的新方法来解决本文的耻骨联合-胎头分割任务。该方法采用类似U-Net的编码器-解码器架构，结合双层路由注意力和跳跃连接，有效学习局部-全局语义信息。此外，我们提出了一个倒置瓶颈块扩展（IBPE）模块，以减少执行上采样操作时的信息丢失。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出色的分割结果。代码可在GitHub上获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
