论文标题
使用U-NET和分割平均网络对颈动脉壁进行分割
Segmentation of carotid vessel wall using U-Net and segmentation average network
论文作者
论文摘要
在血管壁体积（VWV）和局部容器壁层厚度（VWT）定量颈动脉中，需要对颈动脉壁进行分割。血管壁的手动分割是耗时的，容易出现观察者间的变异性。在本文中，我们提出了一个卷积神经网络，以从3D颈动脉超声图像中分割颈动脉（CCA）。提出的CNN涉及三个U-NET，这些U-NET在轴向，侧面和额叶方向上分割了3D超声（3DU）图像。我们在本文中提出的新的分割平均网络（SAN）巩固了由三个U-NET产生的分割图。实验结果表明，拟议的CNN将骰子相似性系数（DSC）提高到64.8％至67.5％，敏感性从63.8％到70.5％，而接收器操作员特征曲线（AUC）下的面积从0.89到0.89至0.94。
Segmentation of carotid vessel wall is required in vessel wall volume (VWV) and local vessel-wall-plus-plaque thickness (VWT) quantification of the carotid artery. Manual segmentation of the vessel wall is time-consuming and prone to interobserver variability. In this paper, we proposed a convolution neural network to segment the common carotid artery (CCA) from 3D carotid ultrasound images. The proposed CNN involves three U-Nets that segmented the 3D ultrasound (3DUS) images in the axial, lateral and frontal orientations. The segmentation maps generated by three U-Nets were consolidated by a novel segmentation average network (SAN) we proposed in this paper. The experimental results show that the proposed CNN improved the Dice similarity coefficient (DSC) for vessel wall segmentation from 64.8% to 67.5%, the sensitivity from 63.8% to 70.5%, and the area under receiver operator characteristic curve (AUC) from 0.89 to 0.94.
