论文标题
急性缺血性中风患者的多输入分割使用Skip Connection缓慢融合
Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection
论文作者
论文摘要
时间是中风治疗过程中的基本因素。一种快速自动的方法，可以分割缺血区域有助于治疗决策。在今天的临床用途中，手动研究了一组由计算机断层扫描（CTP）图像产生的颜色编码的参数图，以决定治疗计划。我们提出了一种基于神经网络的自动方法，使用一组参数图，以分割受急性缺血性中风患者的两个缺血区域（核心和阴茎）。我们的模型基于具有多输入和缓慢融合的卷积卷积瓶颈结构。基于焦点TVERSKY索引的损失函数解决了数据不平衡问题。所提出的结构表明，有效的性能和结果与神经放射学家注释的地面真相相当。在大容器闭塞测试集上，半月将骰子系数为0.81，核心的骰子系数为0.52。完整的实现可在以下网址获得：https：//git.io/jtfgb。
Time is a fundamental factor during stroke treatments. A fast, automatic approach that segments the ischemic regions helps treatment decisions. In clinical use today, a set of color-coded parametric maps generated from computed tomography perfusion (CTP) images are investigated manually to decide a treatment plan. We propose an automatic method based on a neural network using a set of parametric maps to segment the two ischemic regions (core and penumbra) in patients affected by acute ischemic stroke. Our model is based on a convolution-deconvolution bottleneck structure with multi-input and slow fusion. A loss function based on the focal Tversky index addresses the data imbalance issue. The proposed architecture demonstrates effective performance and results comparable to the ground truth annotated by neuroradiologists. A Dice coefficient of 0.81 for penumbra and 0.52 for core over the large vessel occlusion test set is achieved. The full implementation is available at: https://git.io/JtFGb.
