Accurate tissue segmentation in fetal brain MRI remains challenging due to the dynamically changing anatomical anatomy and contrast during fetal development. To enhance segmentation accuracy throughout gestation, we introduced AtlasSeg, a dual-U-shape convolution network incorporating gestational age (GA) specific information as guidance. By providing a publicly available fetal brain atlas with segmentation label at the corresponding GA, AtlasSeg effectively extracted the contextual features of age-specific patterns in atlas branch and generated tissue segmentation in segmentation branch. Multi-scale attentive atlas feature fusions were constructed in all stages during encoding and decoding, giving rise to a dual-U-shape network to assist feature flow and information interactions between two branches. AtlasSeg outperformed six well-known segmentation networks in both our internal fetal brain MRI dataset and the external FeTA dataset. Ablation experiments demonstrate the efficiency of atlas guidance and the attention mechanism. The proposed AtlasSeg demonstrated superior segmentation performance against other convolution networks with higher segmentation accuracy, and may facilitate fetal brain MRI analysis in large-scale fetal brain studies.

翻译：胎儿脑磁共振成像中的精确组织分割仍然具有挑战性，这主要源于胎儿发育过程中解剖结构及对比度的动态变化。为提高整个妊娠期的分割精度，我们提出了AtlasSeg——一种融合孕龄特异性信息作为引导的双U形卷积网络。通过提供包含对应孕龄分割标签的公开胎儿脑图谱，AtlasSeg在图谱分支中有效提取了年龄特异性模式的上下文特征，并在分割分支中生成组织分割结果。网络在编码与解码的所有阶段构建了多尺度注意力图谱特征融合机制，形成双U形结构以促进双分支间的特征流动与信息交互。在内部胎儿脑MRI数据集及外部FeTA数据集上，AtlasSeg均优于六种主流分割网络。消融实验验证了图谱引导与注意力机制的有效性。所提出的AtlasSeg展现出优于其他卷积网络的分割性能，具有更高的分割精度，有望为大规模胎儿脑研究中的MRI分析提供有力支持。