[1]崔奥宇,许幸芬,田苗,等.基于全卷积神经网络的冠状动脉中心线提取方法[J].中国医学物理学杂志,2023,40(4):429-435.[doi:DOI:10.3969/j.issn.1005-202X.2023.04.006]
　CUI Aoyu,XU Xingfen,TIAN Miao,et al.Coronary artery centerline extraction method based on fully convolutional network[J].Chinese Journal of Medical Physics,2023,40(4):429-435.[doi:DOI:10.3969/j.issn.1005-202X.2023.04.006]
点击复制

基于全卷积神经网络的冠状动脉中心线提取方法()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第4期
页码:
429-435
栏目:
医学影像物理
出版日期:
2023-04-25

文章信息/Info

Title:
Coronary artery centerline extraction method based on fully convolutional network
文章编号:
1005-202X（2023）04-0429-07
作者:
崔奥宇; 许幸芬; 田苗; 张磊
兰州交通大学数理学院， 甘肃 兰州 730070
Author(s):
CUI Aoyu;  XU Xingfen;  TIAN Miao;  ZHANG Lei
School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China
关键词:
图像处理; 中心线提取; 全卷积神经网络; 冠状动脉造影; 深度学习
Keywords:
Keywords: image processing centerline extraction fully convolutional network coronary angiography deep learning
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2023.04.006
文献标志码:
A
摘要:
精确提取冠状动脉血管造影（CAG）中心线对血管疾病诊断具有重要意义，本文提出了一种基于全卷积神经网络（FCN）的CAG中心线提取方法。首先利用基于Hessian矩阵的Frangi滤波器去除大部分伪血管噪声，突出CAG的血管树，明显区分血管和背景；利用Steger算法获得血管截面中心亚像素点，将初步提取的中心线作为数据集，标注473张经处理的CAG图像，其中378张为训练集，95张为测试集。以像素准确率（PACC）、平均准确率（MACC）和平均重叠率（MIoU）作为测试结果的评价标准。采用FCN模型分割数据集，将低层特征信息融合高层信息，对融合后的特征图进行反卷积操作，PACC达到0.85，MACC达到0.92，MIoU达到0.82。结果表明本方法具有较高的精度，可有效提取CAG中心线，为冠心病的诊断提供一种有效的辅助手段。
Abstract:
Abstract: The accurate extraction of coronary artery centerline is of great significance for the diagnosis of vascular diseases. Herein a coronary artery centerline extraction method based on fully convolutional network is proposed. The Frangi filter based on Hessian matrix is used to remove most of the pseudo-vessel noise, highlight the blood vessel tree of coronary artery, and clearly distinguish the blood vessels from the background and the Steger algorithm is used to obtain the sub-pixel points in the center of the blood vessel cross-section. The initially extracted centerlines are taken as a data set, and a total of 473 processed images are labeled, with 378 in training set and 95 in test set. The test results are evaluated in terms of pixel accuracy (PACC), mean pixel accuracy (MACC), and mean intersection over union (MIoU). The FCN model is used to segment the data set, fuse the low-level feature information with high-level information, and perform deconvolution on the fused feature map. The highest PACC, MACC and MIoU reach 0.85, 0.92 and 0.82, respectively. The proposed method which has high accuracy and can effectively extract the coronary artery centerline can serve as an auxiliary method for the diagnosis of coronary heart diseases.

相似文献/References:

[1]田 龙,李明辉,胡逸民,等.利用EPID图像金球位置自动跟踪算法研究[J].中国医学物理学杂志,2014,31(04):5038.[doi:10.3969/j.issn.1005-202X.2014.04.014]
[2]刘玉红,王志芳,杨佳仪,等.彩色图像二值化算法及应用[J].中国医学物理学杂志,2013,30(01):3873.[doi:10.3969/j.issn.1005-202X.2013.01.009]
[3]杜永兴,桑路路,秦岭,等.一种基于图像匹配的微波热疗方法[J].中国医学物理学杂志,2016,33(10):1026.[doi:10.3969/j.issn.1005-202X.2016.10.011]
　[J].Chinese Journal of Medical Physics,2016,33(4):1026.[doi:10.3969/j.issn.1005-202X.2016.10.011]
[4]朱翔宇,葛中芹,张冰清,等.基于图像处理的医学影像处理平台系统设计[J].中国医学物理学杂志,2017,34(4):388.[doi:DOI:10.3969/j.issn.1005-202X.2017.04.014]
[5]金京,孟雅丹,徐琦,等.二维分形维数在皮肤癌光学相干断层成像技术诊断中的应用[J].中国医学物理学杂志,2017,34(7):698.[doi:10.3969/j.issn.1005-202X.2017.07.010]
　[J].Chinese Journal of Medical Physics,2017,34(4):698.[doi:10.3969/j.issn.1005-202X.2017.07.010]
[6]王耿媛,赖官铨,段铮昱,等. 基于裂隙灯的眼微血管形态学参数分析在眼科疾病诊断的应用[J].中国医学物理学杂志,2018,35(3):323.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.014]
　WANG Gengyuan,LAI Guanquan,DUAN Zhengyu,et al. Application of morphological parameters analysis of ophthalmic microvasculature based on slit lamp in the diagnosis of ophthalmic diseases[J].Chinese Journal of Medical Physics,2018,35(4):323.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.014]
[7]王力,孟庆民,郭永新,等. 基于Hessian矩阵的DSA图像冠状动脉直径的测量[J].中国医学物理学杂志,2019,36(10):1182.[doi:DOI:10.3969/j.issn.1005-202X.2019.10.013]
　WANG Li,MENG Qingmin,GUO Yongxin,et al. Measurement of coronary artery diameter on DSA image based on Hessian matrix[J].Chinese Journal of Medical Physics,2019,36(4):1182.[doi:DOI:10.3969/j.issn.1005-202X.2019.10.013]
[8]孙凯,姚旭峰,马风玲,等.基于机器学习的血细胞分类研究进展[J].中国医学物理学杂志,2020,37(1):127.[doi:DOI:10.3969/j.issn.1005-202X.2020.01.023]
　SUN Kai,YAO Xufeng,et al.Blood cell classification based on machine learning[J].Chinese Journal of Medical Physics,2020,37(4):127.[doi:DOI:10.3969/j.issn.1005-202X.2020.01.023]
[9]惠钊,黄慧明.基于比率距离的自适应超声图像去噪方法[J].中国医学物理学杂志,2020,37(2):174.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.008]
　HUI Zhao,HUANG Huiming.Adaptive ultrasound image denoising method based on ratio distance[J].Chinese Journal of Medical Physics,2020,37(4):174.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.008]
[10]王尧,黄钢.深度学习在细胞图像分析中的应用进展[J].中国医学物理学杂志,2022,39(8):963.[doi:DOI:10.3969/j.issn.1005-202X.2022.08.008]
　WANG Yao,HUANG Gang,Advances in application of deep learning in cell image analysis[J].Chinese Journal of Medical Physics,2022,39(4):963.[doi:DOI:10.3969/j.issn.1005-202X.2022.08.008]

备注/Memo

备注/Memo:
【收稿日期】2022-10-03 【基金项目】甘肃省自然科学基金（20JR5RA420）；兰州交通大学校青年基金（1200060820） 【作者简介】崔奥宇，硕士研究生，主要从事图像处理、三维光学信息处理研究，E-mail: 582698897@qq.com 【通信作者】许幸芬，博士，硕士生导师，主要从事三维光学传感、图像处理等研究，E-mail: xuxingfen1982@163.com
更新日期/Last Update: 2023-04-25