Computed Tomography Angiography is a key modality providing insights into the cerebrovascular vessel tree that are crucial for the diagnosis and treatment of ischemic strokes, in particular in cases of large vessel occlusions (LVO). Thus, the clinical workflow greatly benefits from an automated detection of patients suffering from LVOs. This work uses convolutional neural networks for case-level classification trained with elastic deformation of the vessel tree segmentation masks to artificially augment training data. Using only masks as the input to our model uniquely allows us to apply such deformations much more aggressively than one could with conventional image volumes while retaining sample realism. The neural network classifies the presence of an LVO and the affected hemisphere. In a 5-fold cross validated ablation study, we demonstrate that the use of the suggested augmentation enables us to train robust models even from few data sets. Training the EfficientNetB1 architecture on 100 data sets, the proposed augmentation scheme was able to raise the ROC AUC to 0.85 from a baseline value of 0.57 using no augmentation. The best performance was achieved using a 3D-DenseNet yielding an AUC of 0.88. The augmentation had positive impact in classification of the affected hemisphere as well, where the 3D-DenseNet reached an AUC of 0.93 on both sides.

翻译：合成成像成像成像成像仪是一种关键模式,它能提供对脑血管容器树的深入了解,对于诊断和治疗缺血性中风至关重要,特别是对于大型船舶封闭(LVO)而言。因此,临床工作流程极大地受益于对受LVO影响的病人的自动检测。这项工作使用对容器树分解面罩弹性变形进行病例分类培训的进化神经神经网络,人工增加培训数据。只有将面具作为我们模型的独特输入,才能使我们在保存样本真实主义的同时,对常规图像量应用这种变形能力要大得多。神经网络将LVO和受影响半球的存在分类。在一项经过5倍交叉验证的扩展研究中,我们证明,建议的扩增能使我们甚至能够从几套数据集中培养稳健的模型。在100个数据集上培训高效的NetB1结构,拟议的扩增计划能够将ROC ACUC从一个0.57的基线值提高到0.85,而不用加增扩增。利用3的常规图像量来取得最佳的性表现,利用3-DE网络在ANS3号上产生了积极影响。