摘要
为了快速准确地对计算机断层扫描(CT)影像中的器官进行分割,提出基于级联Vnet-S网络的单一器官自动分割算法。首先,使用第一个Vnet-S网络对CT影像中的器官进行粗分割;然后,选择分割结果中的最大连接通量做两次膨胀,根据膨胀后的最大连接通量确定器官边界并提取器官区域;最后,使用第二个Vnet-S网络对器官进行细分割。为了验证算法的性能,采用MICCAI 2017 Liver Tumor Segmentation Challenge(LiTS)数据集进行肝脏分割实验,采用ISBI LUng Nodule Analysis 2016(LUNA16)数据集进行肺分割实验。级联Vnet-S算法在LiTS的70例线上测试数据上的Dice系数为0.9600,在LUNA16的288例测试数据上的Dice系数为0.9810,均高于Vnet-S网络和Vnet网络。实验结果表明,基于级联Vnet-S网络的单一器官分割算法可以准确地对器官进行分割,而且级联Vnet-S算法的计算量小于Unet网络和Vnet网络。
In order to realize fast and accurate segmentation of organs in Computed Tomography (CT) images,a automatic segmentation algorithm for single organ based on cascaded Vnet-S network was proposed.Firstly,the organ in the CT image was coarsely segmented by using the first Vnet-S network.Then,the maximum connection flux in the segmentation result was selected and expanded twice,and the organ boundary was determined and the organ area was extracted according to the maximum connection flux after expansion.Finally,the organ was finely segmented by using the second Vnet-S network.In order to verify the performance of the proposed algorithm,a liver segmentation experiment was carried out on the MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) dataset,and a lung segmentation experiment was carried out on the ISBI LUng Nodule Analysis 2016 (LUNA16) dataset.The cascaded Vnet-S algorithm has a Dice coefficient of 0.960 0 on the online test data of 70 cases in LiTS and a Dice coefficient of 0.981 0 on the 288 cases in LUNA16,which are higher than those of Vnet-S network and Vnet network.Experimental results show that the single organ segmentation algorithm based on cascaded Vnet-S network can accurately segment organs with lower computational complexity compared with Vnet and Unet networks.
作者
徐宝泉
凌彤辉
XU Baoquan;LING Tonghui(Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《计算机应用》
CSCD
北大核心
2019年第8期2420-2425,共6页
journal of Computer Applications
关键词
器官分割
Vnet-S
深度学习
分割网络
级联网络结构
organ segmentation
Vnet-S
deep learning
segmentation network
cascaded network structure
