Abstract：
Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, ADC, and FLAIR MRI sequences provide for deep learning in infarct segmentation. Recent technologies in model self-configuration have promised greater performance and generalizability through automated optimization. We assessed the utility of DWI, ADC, and FLAIR sequences on ischemic stroke segmentation, compared self-configuring nnU-Net models to conventional U-Net models without manual optimization, and evaluated the generalizability of results on an external clinical dataset. 3D self-configuring nnU-Net models and standard 3D U-Net models with MONAI were trained on 200 infarcts using DWI, ADC, and FLAIR sequences separately and in all combinations. Segmentation results were compared between models using paired t-test comparison on a hold-out test set of 50 cases. The highest performing model was externally validated on a clinical dataset of 50 MRIs. nnU-Net with DWI sequences attained a Dice score of 0.810 ± 0.155. There was no statistically significant difference when DWI sequences were supplemented with ADC and FLAIR images (Dice score of 0.813 ± 0.150; p = 0.15). nnU-Net models significantly outperformed standard U-Net models for all sequence combinations (p < 0.001). On the external dataset, Dice scores measured 0.704 ± 0.199 for positive cases with false positives with intracranial hemorrhage. Highly optimized neural networks such as nnU-Net provide excellent stroke segmentation even when only provided DWI images, without significant improvement from other sequences. This differs from-and significantly outperforms-standard U-Net architectures. Results translated well to the external clinical environment and provide the groundwork for optimized acute stroke segmentation on MRI.
摘要：
梗死的分割在缺血性卒中管理和预后中具有临床重要意义。目前还不清楚DWI的组合扮演什么角色，ADC,FLAIRMRI序列为梗死分割提供了深度学习。模型自配置中的最新技术已通过自动优化承诺了更高的性能和通用性。我们评估了DWI的实用性，ADC,和缺血性中风分割的FLAIR序列，将自配置nnU-Net模型与无需手动优化的常规U-Net模型进行了比较，并评估了结果在外部临床数据集上的普遍性。使用DWI在200条梗塞上训练了3D自配置nnU-Net模型和具有MONAI的标准3DU-Net模型，ADC,和FLAIR序列分别和所有组合。在50例病例的保持测试集上，使用配对t检验比较在模型之间比较分割结果。在50个MRI的临床数据集上外部验证了性能最高的模型。具有DWI序列的nnU-Net获得0.810±0.155的Dice评分。当DWI序列补充ADC和FLAIR图像时，差异无统计学意义（Dice评分为0.813±0.150；p=0.15）。对于所有序列组合,nnU-Net模型显著优于标准U-Net模型(p<0.001)。在外部数据集上，对于颅内出血假阳性的阳性病例，Dice评分为0.704±0.199。高度优化的神经网络，如nnU-Net，即使仅提供DWI图像，也能提供出色的笔划分割，没有其他序列的显着改善。这与标准U-Net体系结构不同，并且明显优于标准U-Net体系结构。结果很好地转化为外部临床环境，并为MRI上优化急性中风分割提供了基础。
