论文标题
基于新生儿皮质表面的月经后年龄预测的几何深度学习
Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface
论文作者
论文摘要
对新生儿年龄的准确估计对于测量神经发育，医学和生长结果至关重要。在本文中，我们提出了一种新的方法，可以使用基于新生儿白质皮质表面的几何深度学习技术来预测扫描后月经后年龄（PA）。我们利用和比较了使用皮质表面的不同几何表示预测年龄的多个专业神经网络体系结构；我们比较了Meshcnn，PointNet ++，GraphCNN和体积基准。数据集是发展中的人类连接项目（DHCP）的一部分，并且是健康和过早的新生儿的队列。我们评估了650名受试者（727码）的方法，而PA范围为27至45周。我们的结果显示了估计的PA的准确预测，平均误差少于一周。
Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the cortical surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.
