论文标题
新生儿皮质表面发育的深层生成模型
A Deep Generative Model of Neonatal Cortical Surface Development
论文作者
论文摘要
已知新生儿皮质表面受早产的影响，随后对皮质组织的变化与较差的神经发育结局有关。深层生成模型有可能导致临床上可解释的疾病模型，但是在皮质表面开发这些模型是充满挑战的，因为已建立的学习卷积过滤器的技术不适当地不适合非弹性拓扑。为了缩小这一差距，我们使用混合模型CNN（MONET）实现了基于表面的自行车，以在皮质成熟度的不同阶段翻译球形的新生儿皮质表面特征（曲率和T1W/T2W皮质髓磷脂）。结果表明，我们的方法能够可靠地预测妊娠后期皮质组织各个模式的变化，并通过与纵向数据进行比较验证。并通过与受过训练的术语/早产子进行比较来验证早产和妊娠期（> 37周妊娠）之间的外观。皮质成熟的模拟差异与文献中的观察结果一致。
The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional filters are inappropriate on non-flat topologies. To close this gap, we implement a surface-based CycleGAN using mixture model CNNs (MoNet) to translate sphericalised neonatal cortical surface features (curvature and T1w/T2w cortical myelin) between different stages of cortical maturity. Results show our method is able to reliably predict changes in individual patterns of cortical organisation at later stages of gestation, validated by comparison to longitudinal data; and translate appearance between preterm and term gestation (> 37 weeks gestation), validated through comparison with a trained term/preterm classifier. Simulated differences in cortical maturation are consistent with observations in the literature.
