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Abstract：
Cortical surface parcellation for fetal brains is essential for the understanding of neurodevelopmental trajectories during gestations with regional analyses of brain structures and functions. This study proposes the attention-gated spherical U-net, a novel deep-learning model designed for automatic cortical surface parcellation of the fetal brain. We trained and validated the model using MRIs from 55 typically developing fetuses [gestational weeks: 32.9 ± 3.3 (mean ± SD), 27.4-38.7]. The proposed model was compared with the surface registration-based method, SPHARM-net, and the original spherical U-net. Our model demonstrated significantly higher accuracy in parcellation performance compared to previous methods, achieving an overall Dice coefficient of 0.899 ± 0.020. It also showed the lowest error in terms of the median boundary distance, 2.47 ± 1.322 (mm), and mean absolute percent error in surface area measurement, 10.40 ± 2.64 (%). In this study, we showed the efficacy of the attention gates in capturing the subtle but important information in fetal cortical surface parcellation. Our precise automatic parcellation model could increase sensitivity in detecting regional cortical anomalies and lead to the potential for early detection of neurodevelopmental disorders in fetuses.
摘要：
胎儿大脑的皮质表面分裂对于通过对大脑结构和功能的区域分析来理解妊娠期间的神经发育轨迹至关重要。本研究提出了注意门控球形U网，一种新颖的深度学习模型，设计用于胎儿大脑的自动皮质表面分割。我们使用来自55个典型发育胎儿的MRI训练和验证模型[孕周：32.9±3.3（平均值±SD），27.4-38.7].将所提出的模型与基于表面配准的方法进行了比较，SPHARM-net,和原来的球形U形网。与以前的方法相比，我们的模型在分割性能方面表现出明显更高的准确性，实现整体骰子系数为0.899±0.020。它还显示了中值边界距离方面的最低误差，2.47±1.322(mm),和表面积测量的平均绝对百分比误差，10.40±2.64(%)。在这项研究中,我们显示了注意门在捕获胎儿皮质表面分裂中微妙但重要的信息方面的功效。我们精确的自动分割模型可以提高检测区域皮层异常的灵敏度，并导致早期检测胎儿神经发育障碍的潜力。
