Accurate tissue segmentation of thick-slice fetal brain magnetic resonance
(MR) scans is crucial for both reconstruction of isotropic brain MR volumes and
the quantification of fetal brain development. However, this task is
challenging due to the use of thick-slice scans in clinically-acquired fetal
brain data. To address this issue, we propose to leverage high-quality
isotropic fetal brain MR volumes (and also their corresponding annotations) as
guidance for segmentation of thick-slice scans. Due to existence of significant
domain gap between high-quality isotropic volume (i.e., source data) and
thick-slice scans (i.e., target data), we employ a domain adaptation technique
to achieve the associated knowledge transfer (from high-quality <source>
volumes to thick-slice <target> scans). Specifically, we first register the
available high-quality isotropic fetal brain MR volumes across different
gestational weeks to construct longitudinally-complete source data. To capture
domain-invariant information, we then perform Fourier decomposition to extract
image content and style codes. Finally, we propose a novel Cycle-Consistent
Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge
learned from high-quality isotropic volumes for accurate tissue segmentation of
thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated
isotropic volumes to guide tissue segmentation on unannotated thick-slice
scans. Extensive experiments on a large-scale dataset of 372 clinically
acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much
better performance than cutting-edge methods quantitatively and qualitatively.

利用高质量等间隔胎儿脑磁共振体积（以及对应的注释）指导厚层扫描的组织分割，通过域自适应技术实现从高质量等间隔体积到厚层扫描的知识迁移，通过 Fourier 分解提取图像信息和样式编码，最后使用 Cycle-Consistent Domain Adaptation Network（C2DA-Net）在未注释的厚层扫描上进行准确的组织分割。在大规模临床数据集上的广泛实验证明，我们的 C2DA-Net 在定量和定性上都比领先的方法表现出更好的性能。