Liu Changhua, Qin Tao, Liu Liangjin
Department of Radiology, Hanyang Hospital Affiliated of Wuhan University of Science and Technology, Wuhan 430050, China.
Hubei No. 3 People's Hospital of Jianghan University, Department of Radiology, Wuhan, China.
J Healthc Eng. 2021 Nov 30;2021:3215107. doi: 10.1155/2021/3215107. eCollection 2021.
In order to investigate the value of multimodal CT for quantitative assessment of collateral circulation, ischemic semidark zone, core infarct volume in patients with acute ischemic stroke (AIS), and prognosis assessment in intravenous thrombolytic therapy, segmentation model which is based on the self-attention mechanism is prone to generate attention coefficient maps with incorrect regions of interest. Moreover, the stroke lesion is not clearly characterized, and lesion boundary is poorly differentiated from normal brain tissue, thus affecting the segmentation performance. To address this problem, a primary and secondary path attention compensation network structure is proposed, which is based on the improved global attention upsampling U-Net model. The main path network is responsible for performing accurate lesion segmentation and outputting segmentation results. Likewise, the auxiliary path network generates loose auxiliary attention compensation coefficients, which compensate for possible attention coefficient errors in the main path network. Two hybrid loss functions are proposed to realize the respective functions of main and auxiliary path networks. It is experimentally demonstrated that both the improved global attention upsampling U-Net and the proposed primary and secondary path attention compensation networks show significant improvement in segmentation performance. Moreover, patients with good collateral circulation have a small final infarct area volume and a good clinical prognosis after intravenous thrombolysis. Quantitative assessment of collateral circulation and ischemic semidark zone by multimodal CT can better predict the clinical prognosis of intravenous thrombolysis.
为了探讨多模态CT在急性缺血性脑卒中（AIS）患者侧支循环、缺血半暗带、核心梗死体积定量评估及静脉溶栓治疗预后评估中的价值，基于自注意力机制的分割模型容易生成感兴趣区域错误的注意力系数图。此外，脑卒中病变特征不明显，病变边界与正常脑组织区分度差，从而影响分割性能。为解决这一问题，提出了一种基于改进的全局注意力上采样U-Net模型的主次路径注意力补偿网络结构。主路径网络负责进行准确的病变分割并输出分割结果。同样，辅助路径网络生成宽松的辅助注意力补偿系数，以补偿主路径网络中可能存在的注意力系数误差。提出了两种混合损失函数来实现主路径和辅助路径网络的各自功能。实验表明，改进的全局注意力上采样U-Net和所提出的主次路径注意力补偿网络在分割性能上均有显著提高。此外，侧支循环良好的患者静脉溶栓后最终梗死面积体积较小，临床预后良好。多模态CT对侧支循环和缺血半暗带的定量评估可以更好地预测静脉溶栓的临床预后。
