提出高效分割胎儿头与耻骨联合的U型网络，助力分娩过程精准评估。
DSSAU-Net:U-Shaped Hybrid Network for Pubic Symphysis and Fetal Head Segmentation
- 采用稀疏自注意力块构建对称U型网络，兼顾精度与效率。
- 在IUGC 2024测试集上分类与分割任务均获第四名。
- 适合医学影像分析、产科辅助诊断研究者参考。
分娩过程中，传统阴道检查主观性强且不准确。超声辅助诊断可通过角进展（AoP）和头-耻骨距离（HSD）客观评估胎儿头位，这两个参数依赖于胎儿头（FH）和耻骨联合（PS）的精确分割，有助于确保顺利分娩。为此，本文提出一种高性能、高计算效率的稀疏自注意力网络DSSAU-Net，用于FH与PS分割。该网络采用对称U型编码器-解码器结构，在每一阶段堆叠不同数量的双稀疏选择注意力（DSSA）模块。针对给定查询，DSSA在区域和像素层面分别执行一次稀疏标记选择，有效降低计算复杂度并提取关键特征。为弥补上采样过程中的信息损失，设计了带卷积的跳跃连接；同时引入多尺度特征融合以增强模型的全局与局部感知能力。DSSAU-Net在MICCAI IUGC 2024竞赛组织方提供的Intrapartum Ultrasound Grand Challenge（IUGC）2024测试集上进行验证，分类与分割任务均获得第四名，证明其有效性。代码将开源于https://github.com/XiaZunhui/DSSAU-Net。
原文摘要 · Abstract (English)
In the childbirth process, traditional methods involve invasive vaginal examinations, but research has shown that these methods are both subjective and inaccurate. Ultrasound-assisted diagnosis offers an objective yet effective way to assess fetal head position via two key parameters: Angle of Progression (AoP) and Head-Symphysis Distance (HSD), calculated by segmenting the fetal head (FH) and pubic symphysis (PS), which aids clinicians in ensuring a smooth delivery process. Therefore, accurate segmentation of FH and PS is crucial. In this work, we propose a sparse self-attention network architecture with good performance and high computational efficiency, named DSSAU-Net, for the segmentation of FH and PS. Specifically, we stack varying numbers of Dual Sparse Selection Attention (DSSA) blocks at each stage to form a symmetric U-shaped encoder-decoder network architecture. For a given query, DSSA is designed to explicitly perform one sparse token selection at both the region and pixel levels, respectively, which is beneficial for further reducing computational complexity while extracting the most relevant features. To compensate for the information loss during the upsampling process, skip connections with convolutions are designed. Additionally, multiscale feature fusion is employed to enrich the model's global and local information. The performance of DSSAU-Net has been validated using the Intrapartum Ultrasound Grand Challenge (IUGC) 2024 \textit{test set} provided by the organizer in the MICCAI IUGC 2024 competition\footnote{\href{https://codalab.lisn.upsaclay.fr/competitions/18413\#learn\_the\_details}{https://codalab.lisn.upsaclay.fr/competitions/18413\#learn\_the\_details}}, where we win the fourth place on the tasks of classification and segmentation, demonstrating its effectiveness. The codes will be available at https://github.com/XiaZunhui/DSSAU-Net.
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