[1]王志明,周鸿宇,瞿少成.基于医学影像的脂肪组织分割与定量方法研究[J].医学信息,2021,34(11):28-30,39.[doi:10.3969/j.issn.1006-1959.2021.11.009]
 WANG Zhi-ming,ZHOU Hong-yu,QU Shao-cheng.Research on Adipose Tissue Segmentation and Quantification Method Based on Medical Imaging[J].Medical Information,2021,34(11):28-30,39.[doi:10.3969/j.issn.1006-1959.2021.11.009]
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基于医学影像的脂肪组织分割与定量方法研究()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
34卷
期数:
2021年11期
页码:
28-30,39
栏目:
综述
出版日期:
2021-06-01

文章信息/Info

Title:
Research on Adipose Tissue Segmentation and Quantification Method Based on Medical Imaging
文章编号:
1006-1959(2021)11-0028-04
作者:
王志明周鸿宇瞿少成
(1.华中师范大学物理科学与技术学院电子信息系,湖北 武汉 430000; 2.中国科学院深圳先进技术研究院生物医学与健康工程研究所,广东 深圳 518000)
Author(s):
WANG Zhi-mingZHOU Hong-yuQU Shao-cheng
(1.Department of Electronic Information,College of Physical Science and Technology,Central China Normal University, Wuhan 430000,Hubei,China; 2.Institute of Biomedicine and Health Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518000,Guangdong,China)
关键词:
脂肪组织计算机断层扫描磁共振成像图像分割
Keywords:
AdiposetissueComputedtomographyMagnetic resonance imagingImage segmentation
分类号:
TP391
DOI:
10.3969/j.issn.1006-1959.2021.11.009
文献标志码:
A
摘要:
随着社会发展与生活水平的逐步提高,超重和肥胖已经成为全球性的问题,由肥胖所引起的代谢类疾病以及癌症已经严重威胁全球人类健康。肥胖是由于人体摄入的过量能量以甘油三酯的形式存储在脂肪细胞中引起的。大量医学证明,人体不同部位的脂肪组织具有不同的生理特点,因此对体内脂肪组织进行分割与定量具有重要的临床意义。医学影像技术的发展可以对人体总脂肪组织(TAT)无创成像,并且通过图像分割技术能实现总脂肪组织的分割。本文主要综述近年来国内外脂肪组织分割与定量的相关研究,旨在为从事肥胖或脂肪组织图像分割的研究人员提供新的思路与借鉴。
Abstract:
With the development of society and the gradual improvement of living standards, overweight and obesity have become global problems. Metabolic diseases and cancers caused by obesity have seriously threatened global human health.Obesity is caused by the body’s excessive intake of energy stored in fat cells in the form of triglycerides.A large number of medical proofs have shown that adipose tissue in different parts of the human body has different physiological characteristics, so segmentation and quantification of adipose tissue in the body has important clinical significance.The development of medical imaging technology can non-invasively image human body total adipose tissue (TAT), and segmentation of total adipose tissue can be achieved through image segmentation technology.This article mainly reviews the domestic and foreign research on adipose tissue segmentation and quantification in recent years, and aims to provide new ideas and references for researchers engaged in obesity or adipose tissue image segmentation.

参考文献/References:

[1]Upadhyay J,Farr O,Perakakis N,et al.Obesity as a disease[J].Medical Clinics,2018,102(1):13-33. [2]Messiah SE,Vidot DC,SomarribaG,et al.Obesity and cardiometabolic disease risk factors among US adolescents with disabilities[J].World Journal of Diabetes,2015,6(1):200-207. [3]Chadid S,Singer MR,Kreger BE,et al.Midlife weight gain is a risk factor for obesity-related cancer[J].British Journal of Cancer,2018,118(12):1665-1671. [4]Bizino MB,Sala ML,de Heer P,et al.MR of multi-organ involvement in the metabolic syndrome[J].Magnetic Resonance Imaging Clinics,2015,23(1):41-58. [5]刘慧,白冰.血管周围脂肪组织与心血管疾病的关系[J].心血管病学进展,2018,39(4):640-643. [6]潘晓航.医学图像分割方法[J].电子技术与软件工程,2018(11):84-85. [7]Graffy PM,Pickhardt PJ.Quantification of hepatic and visceral fat by CT and MR imaging:relevance to the obesity epidemic,metabolic syndrome and NAFLD[J].The British Journal of Radiology,2016,89(1062):20151024. [8]庄俐,张惠莉,张瑞霞.棕色脂肪形成的内分泌及环境影响因素研究[J].医学信息,2019,32(4):36-38. [9]Pham TT,Ivaska KK,Hannukainen JC,et al.Human Bone Marrow Adipose Tissue is a Metabolically Active and Insulin-Sensitive Distinct Fat Depot[J].Journal of Clinical Endocrinology&Metabolism,2020,105(7):1-11. [10]Borga M,West J,Bell JD,et al.Advanced body composition assessment:from body mass index to body composition profiling[J].BMJ Open Access,2018,66(5):887-895. [11]张守华,丁兰洲,常琼,等.能谱CT成像技术指标与脂肪密度模型相关性[J].中华实用诊断与治疗杂志,2017,31(2):153-155. [12]王萍,唐光才,舒健,等.非酒精性脂肪肝肝/脾CT值与血脂的关系研究[J].西南医科大学学报,2018,41(1):67-70. [13]Hu HH,Kan HE.Quantitative proton MR techniques for measuring fat[J].Nmr in Bio-medicine,2013,26(12):1609-1629. [14]Fritz S.Whole-body MRI at high field:technical limits and clinical potential[J].European Radiology,2005,15(5):946-959. [15]骆睿,胡小情,陈潇,等.磁共振多通道射频接收线圈性能评估[J].集成技术,2016,5(3):79-83. [16]祝乐群,李冠武,施丹,等.多回波化学位移编码水/脂MRI评估骨髓脂肪的可行性研究[J].实用放射学杂志,2018,34(2):283-286. [17]Cheng C,Zou C,Liang C,et al.Fat-water separation using a region‐growing algorithm with self‐feeding phasor estimation[J].Magnetic Resonance in Medicine,2017,77(6):2390-2401. [18]曹鸿吉,盛斌,吴雯,等.基于改进K-Means的腹内脂肪自动定量检测算法[J].计算机辅助设计与图形学学报,2017,29(4):575-583. [19]Valentinitsch AC,Karampinos D,Alizai H,et al.Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle[J].Journal of Magnetic Resonance Imaging,2013,37(4):917-927. [20]Wald D,Teucher B,Dinkel J,et al.Automatic quantification of subcutaneous and visceral adipose tissue from whole-body magnetic resonance images suitable for large cohort studies[J].Journal of Magnetic Resonance Imaging,2012,36(6):1421-1434. [21]Sadananthan SA,Prakash B,Leow KS,et al.Automated segmentation of visceral and subcutaneous(deep and superficial)adipose tissues in normal and overweight men[J].Journal of Magnetic Resonance Imaging,2015,41(4):924-934. [22]陈春林.基于Micro-CT的脂肪测量软件设计开发[D].西安电子科技大学,2014. [23]刘淑霞,王小红,朱春,等.腹部脂肪面积定量 CT 测定及其与肥胖并发症关系的临床研究[J].影像研究与医学应用,2018(1):176-177. [24]晏乘曦,王玲,姚丁华,等.CT定量测量髋部骨折患者髋部肌肉、脂肪面积及CT值的可重复性、可信度分析[J].山东医药,2018,58(16):58-60. [25]Nemoto M,Yeernuer T,Masutani Y,et al.Development of automatic visceral fat volume calculation software for CT volume data[J].Journal of Obesity,2014(2014):495084. [26]Joshi AA,Hu HH,Richard M,et al.Automatic intra-subject registration-based segmentation of abdominal fat from water–fat MRI[J].Journal of Magnetic Resonance Imaging,2013,37(2):423-430. [27]Krishna ST,Kalluri HK.Deep learning and transfer learning approaches for image classification[J].International Journal of Recent Technology and Engineering(IJRTE),2019,7(5S4):427-432. [28]Shen N,Li X,Zheng S,et al.Automated and accurate quantification of subcutaneous and visceral adipose tissue from magnetic resonance imaging based on machine learning[J].Magnetic Resonance Imaging,2019(64):28-36. [29]Langner T,Hedstrom A,Morwald K,et al.Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water-fat MRI[J].Magnetic Resonance in Medicine,2019,81(4):2736-2745. [30]张嘉祺,赵晓丽,董晓亚,张翔.面向图像语义分割的生成对抗网络模型[J].传感器与微系统,2019,38(8):50-53.

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更新日期/Last Update: 1900-01-01