[1]王戚玲,汪 兵,利玉林,等.肝细胞肝癌CT图像纹理分析分割技术的初步研究[J].医学信息,2018,31(11):89-92.[doi:10.3969/j.issn.1006-1959.2018.11.027]
 WANG Qi-ling,WANG Bing,LI Yu-lin,et al.Preliminary Study on Segmentation Technique of Texture Analysis of Hepatocellular Carcinoma CT Image[J].Journal of Medical Information,2018,31(11):89-92.[doi:10.3969/j.issn.1006-1959.2018.11.027]
点击复制

肝细胞肝癌CT图像纹理分析分割技术的初步研究()
分享到:

医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
31卷
期数:
2018年11期
页码:
89-92
栏目:
论著
出版日期:
2018-06-01

文章信息/Info

Title:
Preliminary Study on Segmentation Technique of Texture Analysis of Hepatocellular Carcinoma CT Image
文章编号:
1006-1959(2018)11-0089-04
作者:
王戚玲1汪 兵1利玉林1成官迅1向子云2
1.北京大学深圳医院医学影像科,广东 深圳 518036; 2.深圳市龙岗区人民医院影像科,广东 深圳 518172
Author(s):
WANG Qi-ling1WANG Bing1LI Yu-lin1CHENG Guan-xun1XIANG Zi-yun2
1.Department of Medical Imaging,Shenzhen Hospital,Peking University,Shenzhen 518036,Guangdong,China; 2.Department of Imaging,Shenzhen Longgang District People's Hospital,Shenzhen 518172,Guangdong,China
关键词:
影像组学影像特征分割
Keywords:
Key words:RadiomicsImage featuresSegmentation
分类号:
R735.7
DOI:
10.3969/j.issn.1006-1959.2018.11.027
文献标志码:
A
摘要:
目的 探讨机器自动分割肝癌CT图像纹理特征的可行性。方法 用强度、纹理、形状和边缘的图像特征来描述分割的情况,计算从操作者分割提取的特征与机器分割的相关性,测量不同操作者分割CT图像与机器分割CT图像的一致性。结果 操作者在选择不同层面时并不一致。操作者的分割结果也并非重叠。每个机器分割与其操作者手动分割的平均重叠程度与两个操作者之间的重叠程度相当(74% vs 69%)。机器分割与操作者手动分割组内相关性(ICC)结果表示纹理和强度特征是最显著的,边缘和形状特征最小。结论 本研究通过在每个操作者分割的最大圆来确定机器自动分割,从而有助于临床中可以更快、更准确的对CT图像进行分割。
Abstract:
Abstract:Objective To explore the feasibility of automatic segmentation of liver cancer CT image texture features.Methods Intensity,texture,shape,and edge image features were used to describe the segmentation conditions.The correlation between the segmentation feature extracted from the operator and the machine segmentation was calculated,and the consistency between different operator segmented CT images and machine segmentation CT images was measured.Results Operators are inconsistent when choosing different levels.The operator's segmentation results are also not overlapping.The average degree of overlap between each machine segment and its operator's manual segmentation is comparable to the overlap between the two operators(74%vs69%).Machine segmentation and operator manual segmentation of intra-group correlation(ICC)results indicate that texture and intensity features are the most prominent,with minimal edge and shape features.Conclusion In this study,the automatic segmentation of the machine is determined by the maximum circle segmented by each operator,which helps to quickly and accurately segment the CT image in the clinic.

参考文献/References:

[1]Doi K.Current status and future potential of computer-aided diagnosis in medical imaging[J].Br J Radiol,2014,78(1):3-19. [2]Kamble R,Kokare M,Deshmukh G,et al.Localization of optic disc and fovea in retinal images using intensity based line scanning analysis[J].Comput Biol Med,2017,87:382-396. [3]Lambin P,Riosvelazquez E,Leijenaar R,et al.Radiomics:extracting more information from medical images using advanced feature analysis[J].European Journal of Cancer,2012,48(4):441-446. [4]Chen W,Giger ML,Bick U.A fuzzy c-means(FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images[J].Academic Radiology,2006,13(1):63-72. [5]Cai W,He B,Fan Y,et al.Comparison of liver volumetry on contrast-enhanced CT images:one semiautomatic and two automatic approaches[J].Journal of Applied Clinical Medical Physics,2016,17(6):118. [6]Tae WS.Regional gray matter volume reduction associated with major depressive disorder:a voxel-based morphometry[J].iMRI,2015,19(1):10-18. [7]Caceres A,Hall DL,Zelaya FO,et al.Measuring fMRI reliability with the intra-class correlation coefficient[J].Neuroimage,2009,45(3):758-768. [8]Rubin DL,Willrett D,O'Connor MJ,et al.Automated tracking of quantitative assessments of tumor burden in clinical trials[J].Translational Oncology,2014,7(1):23-35. [9]Balagurunathan Y,Gu Y,Wang H,et al.Reproducibility and Prognosis of Quantitative Features Extracted from CT Images 1,2[J].Translational Oncology,2014,7(1):72-87. [10]Dettori L,Semler L.A comparison of wavelet,ridgelet,and curvelet-based texture classification algorithms in computed tomography[J].Computers in Biology&Medicine,2007,37(4):486-498. [11]Gevaert O,Xu J,Hoang CD,et al.Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data methods and preliminary results[J].Radiology,2012,264(2):387-396. [12]Napel SA,Beaulieu CF,Rodriguez C,et al.Automated retrieval of CT images of liver lesions on the basis of image similarity:method and preliminary results[J].Radiology,2010,256(1):243-252. [13]Gevaert O,Mitchell LA,Achrol AS,et al.Glioblastoma multiforme:exploratory radiogenomic analysis by using quantitative image features[J].Radiology,2014,273(1):168-174. [14]Gevaert O,Mitchell LA,Achrol AS,et al.Glioblastoma multiforme:exploratory radiogenomic analysis by using quantitative image features[J].Radiology,2015,276(1):313. [15]Ionan AC,Polley MYC,Mcshane LM,et al.Comparison of confidence interval methods for an intra-class correlation coefficient (ICC)[J].Bmc Medical Research Methodology,2014,14(1):121.

相似文献/References:

[1]黄梦倩,张瑞恒,刘楚浩,等.影像组学在神经系统疾病中的应用研究[J].医学信息,2019,32(16):47.[doi:10.3969/j.issn.1006-1959.2019.16.015]
 HUANG Meng-qian,ZHANG Rui-heng,LIU Chu-hao,et al.Application of Radiomics in Nervous System Diseases[J].Journal of Medical Information,2019,32(11):47.[doi:10.3969/j.issn.1006-1959.2019.16.015]
[2]李文华,许传斌,崔景景,等.基于治疗前MR-T2WI影像组学特征预测肝细胞癌射频消融术后早期复发的价值[J].医学信息,2022,35(18):39.[doi:10.3969/j.issn.1006-1959.2022.18.009]
 LI Wen-hua,XU Chuan-bin,CUI Jing-jing,et al.The Value of Predicting Early Recurrence of Hepatocellular Carcinoma After Radiofrequency Ablation Based on Pre-treatment MR-T2WI Radiomics Features[J].Journal of Medical Information,2022,35(11):39.[doi:10.3969/j.issn.1006-1959.2022.18.009]
[3]蔡 凯,黄贵华.基于生命/影像组学的精准组学放射治疗研究进展[J].医学信息,2020,33(14):27.[doi:10.3969/j.issn.1006-1959.2020.14.010]
 CAI Kai,HUANG Gui-hua.Research Progress of Precise Omics Radiotherapy Based on Life/Imaging Omics[J].Journal of Medical Information,2020,33(11):27.[doi:10.3969/j.issn.1006-1959.2020.14.010]
[4]吴文杰,裴天龙,汪永康,等.基于CT影像组学特征预测肾透明细胞癌分化程度的研究[J].医学信息,2021,34(11):96.[doi:10.3969/j.issn.1006-1959.2021.11.026]
 WU Wen-jie,PEI Tian-long,WANG Yong-kang,et al.Study on Predicting Differentiation Degree of Renal Clear Cell Carcinoma Based on CT Imaging Features[J].Journal of Medical Information,2021,34(11):96.[doi:10.3969/j.issn.1006-1959.2021.11.026]

更新日期/Last Update: 2018-06-01