Ischemic strokes are often caused by large vessel occlusions (LVOs), which can be visualized and diagnosed with Computed Tomography Angiography scans. As time is brain, a fast, accurate and automated diagnosis of these scans is desirable. Human readers compare the left and right hemispheres in their assessment of strokes. A large training data set is required for a standard deep learning-based model to learn this strategy from data. As labeled medical data in this field is rare, other approaches need to be developed. To both include the prior knowledge of side comparison and increase the amount of training data, we propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres or hemisphere subregions from different patients. The subregions cover vessels commonly affected by LVOs, namely the internal carotid artery (ICA) and middle cerebral artery (MCA). In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres. Furthermore, we propose an extension of that architecture to process the individual hemisphere subregions. All configurations predict the presence of an LVO, its side, and the affected subregion. We show the effect of recombination as an augmentation strategy in a 5-fold cross validated ablation study. We enhanced the AUC for patient-wise classification regarding the presence of an LVO of all investigated architectures. For one variant, the proposed method improved the AUC from 0.73 without augmentation to 0.89. The best configuration detects LVOs with an AUC of 0.91, LVOs in the ICA with an AUC of 0.96, and in the MCA with 0.91 while accurately predicting the affected side.
翻译:由于时间是大脑,因此有必要对这些扫描进行快速、准确和自动的诊断。人类读者在评估中风时比较左半球和右半球。为了从数据中学习这一战略,一个标准的深层学习模型需要大型培训数据集。由于该领域的标签医学数据很少,需要开发其他方法。为了包括事先了解侧面比较和增加培训数据的数量,我们建议一种增强的方法,通过对不同病人的半球或半球次区域的船舶树分解进行重组来生成人造培训样本。次区域覆盖受中风影响的船舶,即内部动脉动(ICA)和中脑动脉动(MCA)。根据扩增计划,我们使用一个带有特定任务投入的3D-DeneNet,促进各半球之间的侧比较。此外,我们提议将这一结构扩展至各个半球的组合,从各个半球或半球的分层重新组合,1 LVOA 和LVA 的分流压法。我们用一个更精确的分层结构,用一个更精确的跨半球结构,用一个更精确的LVA 预测一个更精确的次区域。我们预测了LVA的图。