论文标题
几何深度学习的医学应用诊断青光眼
Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma
论文作者
论文摘要
目的：（1）评估从单个光学相干断层扫描（OCT）3D扫描视神经头（ONH）诊断青光眼中几何深度学习（PointNet）的性能； （2）将其性能与标准3D卷积神经网络（CNN）和金标准青光眼参数（即视网膜神经纤维层（RNFL）厚度）进行比较。 方法：在新加坡国家眼中，使用Spectralis OCT进行了477个青光眼和2,296名非糖果瘤受试者的ONH的3D栅格扫描。使用深度学习将所有体积自动分割，以识别7个主要的神经和结缔组织，包括RNFL，预序和lamina cribrosa（LC）。然后，每个ONH被表示为3D点云，从所有组织边界随机选择了1,000点。为了简化问题，所有ONH点云都相对于Bruch膜开口的平面和中心对齐。然后使用几何深度学习（PointNet）从单个OCT点云中提供青光眼诊断。将我们的方法的性能与3D CNN获得的方法和RNFL厚度进行了比较。 结果：PointNet能够提供仅代表3D点云的ONH（AUC = 95％）的强大青光眼诊断。 PointNet的性能优于标准3D CNN（AUC = 87％）获得的性能，并且单独从RNFL厚度获得（AUC = 80％）。 讨论：我们为在青光眼领域的几何深学习应用提供了原则证明。我们的技术需要明显较少的信息，即可表现出比3D CNN更好的，并且AUC优于仅从RNFL厚度获得的AUC。几何深度学习可能在眼科领域具有广泛的适用性。
Purpose: (1) To assess the performance of geometric deep learning (PointNet) in diagnosing glaucoma from a single optical coherence tomography (OCT) 3D scan of the optic nerve head (ONH); (2) To compare its performance to that obtained with a standard 3D convolutional neural network (CNN), and with a gold-standard glaucoma parameter, i.e. retinal nerve fiber layer (RNFL) thickness. Methods: 3D raster scans of the ONH were acquired with Spectralis OCT for 477 glaucoma and 2,296 non-glaucoma subjects at the Singapore National Eye Centre. All volumes were automatically segmented using deep learning to identify 7 major neural and connective tissues including the RNFL, the prelamina, and the lamina cribrosa (LC). Each ONH was then represented as a 3D point cloud with 1,000 points chosen randomly from all tissue boundaries. To simplify the problem, all ONH point clouds were aligned with respect to the plane and center of Bruch's membrane opening. Geometric deep learning (PointNet) was then used to provide a glaucoma diagnosis from a single OCT point cloud. The performance of our approach was compared to that obtained with a 3D CNN, and with RNFL thickness. Results: PointNet was able to provide a robust glaucoma diagnosis solely from the ONH represented as a 3D point cloud (AUC=95%). The performance of PointNet was superior to that obtained with a standard 3D CNN (AUC=87%) and with that obtained from RNFL thickness alone (AUC=80%). Discussion: We provide a proof-of-principle for the application of geometric deep learning in the field of glaucoma. Our technique requires significantly less information as input to perform better than a 3D CNN, and with an AUC superior to that obtained from RNFL thickness alone. Geometric deep learning may have wide applicability in the field of Ophthalmology.
