Purpose: To show a deep learning model that segments acute ischemic stroke on NCCT at a level comparable to neuroradiologists. Materials and Methods: This included 227 Head NCCT examinations from 200 patients enrolled in the multi-center DEFUSE 3 trial. Three experienced neuroradiologists independently segmented the acute infarct on each scan. The neuroradiologists were divided into training experts (A) and test experts (B and C). The dataset was randomly split, by patient, into 5 folds with training and validation cases. A 3D deep Convolutional Neural Network (CNN) architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics. The optimized model was compared to the test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. Results: The model-expert agreement was non-inferior to the inter-expert agreement as evaluated with a paired one-sided test procedure for differences in medians with lower boundaries of 10%, 2ml, and 5mm, p < 0.05, n=160. Conclusion: The 3d CNN trained on one neuroradiologist generalizes to acute ischemic stroke segmentation on NCCT of other neuroradiologists.
翻译:· 材料和方法:这包括200名在多中心DEFUSE 3试验中注册的200名病人的227次首席NCT测试; 3名有经验的神经放射学家在每次扫描中单独分割了急性皮肤。 神经放射学家分为培训专家(A)和测试专家(B和C)。 数据集按病人随机分为5个与培训和鉴定案例的折叠。 3D深革命神经网络架构(CNN)经过培训和优化,以预测NCCT专家A的分块。模型的性能是用数量、重叠和距离等一套衡量标准来评估的。 最优化模型与测试专家B和C进行了比较。 我们用单面的威尔科松签级测试,以测试模型专家与培训和鉴定案例之间的非过分性与培训和鉴定情况。 结果:模型专家协议与NCNCCT专家协议的分级协议并不过分,用来预测A专家A分部专家的分块。 模型的性能是用数量、重叠和距离度尺度为1至1级的直径的神经测试程序。