2D/3D级联卷积在分割CT肺动脉上的应用研究

Application Research of 2D/3D Cascade Convolution in Segmentation of CT Pulmonary Artery

医学影像分割是计算机辅助诊断的重要组成部分。针对CT影像的三维特性,提出了一种基于2D/3D级联卷积的Unet网络结构用来分割肺动脉。该结构相比基于传统2D卷积的方法,关联了第三维度信息,提高了分割准确度和泛化能力,相比基于传统3D卷积的方法提高了准确度和执行效率。实验对多套肺动脉增强CT数据集做了验证,分割准确率达到85.7%,高于传统2D和3DUnet网络,同时执行效率较3DUnet提高近30%,在CT影像分割上做到了效率和准确度的兼顾。

The segmentation of medical images is an important part of computer-aided diagnosis. In this paper, based on the threedimensional characteristics of CT images, a Unet network structure based on 2D/3D cascade convolution structure is proposed to segment the pulmonary artery. Compared with the traditional 2D convolution method, the structure is associated with the third dimension information, which improves the segmentation accuracy and the ability of generalization, and improves the accuracy and execution efficiency compared with the traditional 3D convolution method. The CT datasets of multiple sets of pulmonary vascular enhancement were validated, and the segmentation accuracy rate was 85.7%, which was higher than that of traditional 2D and 3D Unet networks. At the same time, the execution efficiency was improved by about 30% compared with 3D Unet. The method make great progress of both efficiency and accuracy in CT image segmentation.