IEEE Trans Med Imaging. 2021 Sep;40(9):2380-2391. doi: 10.1109/TMI.2021.3077113. Epub 2021 Aug 31.
The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter- and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.
血栓溶解治疗急性缺血性脑卒中（TICI）评分是评估再灌注治疗的一个重要指标。它通常作为血管内治疗（EVT）后的技术结果衡量标准。现有的 TICI 评分是基于视觉检查的粗略有序等级定义的，因此存在观察者间和观察者内的差异。在这项工作中，我们提出了一种自动和定量的 TICI 评分方法，称为 autoTICI。首先，使用多路径卷积神经网络（CNN）将每个数字减影血管造影（DSA）采集分为四个阶段（非对比、动脉、实质和静脉期），该网络利用时空特征。该网络还以状态转移矩阵的形式结合了序列级标签依赖关系。接下来，使用运动校正的动脉和实质帧计算最小强度图（MINIP）。在 MINIP 图像上，分割血管、灌注和背景像素。最后，我们将 EVT 后再灌注像素的比例量化为 autoTICI 评分。在常规采集的多中心数据集上，所提出的 autoTICI 与扩展 TICI（eTICI）参考具有良好的相关性，平均曲线下面积（AUC）评分为 0.81。与二分的 eTICI 相比，AUC 评分为 0.90。在临床结果预测方面，我们证明了 autoTICI 总体上与 eTICI 相当。
