用深度学习生成随孕周变化的胎儿脑图谱，实现快速精准组织分割。
Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation
- 基于条件判别器与直接配准结合，生成连续孕周特异的脑图谱。
- 平均骰子系数达86.3%，六类脑组织分割准确率高。
- 适合产前神经发育研究与临床辅助诊断，支持实时处理。
胎儿脑部磁共振成像已成为在体研究脑发育的重要工具，但受胎龄不确定性、发育差异及成像协议多样性的挑战。为此，脑图谱提供标准化参考框架，通过将图谱与个体影像配准至统一坐标系，实现客观比较。本文提出一种新型深度学习框架，用于生成连续、孕周特异的胎儿脑图谱，支持实时组织分割。该框架结合直接配准模型与条件判别器，在包含219例正常胎儿MRI（孕周21至37周）的高质量数据集上训练。方法在配准精度、结构细节保留和分割性能方面表现优异，六类脑组织平均骰子系数（DSC）达到86.3%。生成图谱的体积分析揭示了典型神经发育轨迹，为胎儿脑成熟机制提供深入洞察。该方法仅需极少预处理，具备实时性能，适用于科研与临床场景。代码已开源：https://github.com/cirmuw/fetal-brain-atlas。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging (MRI) of the fetal brain has become a key tool for studying brain development in vivo. Yet, its assessment remains challenging due to variability in brain maturation, imaging protocols, and uncertain estimates of Gestational Age (GA). To overcome these, brain atlases provide a standardized reference framework that facilitates objective evaluation and comparison across subjects by aligning the atlas and subjects in a common coordinate system. In this work, we introduce a novel deep-learning framework for generating continuous, age-specific fetal brain atlases for real-time fetal brain tissue segmentation. The framework combines a direct registration model with a conditional discriminator. Trained on a curated dataset of 219 neurotypical fetal MRIs spanning from 21 to 37 weeks of gestation. The method achieves high registration accuracy, captures dynamic anatomical changes with sharp structural detail, and robust segmentation performance with an average Dice Similarity Coefficient (DSC) of 86.3% across six brain tissues. Furthermore, volumetric analysis of the generated atlases reveals detailed neurotypical growth trajectories, providing valuable insights into the maturation of the fetal brain. This approach enables individualized developmental assessment with minimal pre-processing and real-time performance, supporting both research and clinical applications. The model code is available at https://github.com/cirmuw/fetal-brain-atlas
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