Skip to main content

Advertisement

SpringerLink
Log in

Segmentation and quantification of adipose tissue by magnetic resonance imaging

  • Review Article
  • Published:
Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

In this brief review, introductory concepts in animal and human adipose tissue segmentation using proton magnetic resonance imaging (MRI) and computed tomography are summarized in the context of obesity research. Adipose tissue segmentation and quantification using spin relaxation-based (e.g., T1-weighted, T2-weighted), relaxometry-based (e.g., T1-, T2-, T2*-mapping), chemical-shift selective, and chemical-shift encoded water–fat MRI pulse sequences are briefly discussed. The continuing interest to classify subcutaneous and visceral adipose tissue depots into smaller sub-depot compartments is mentioned. The use of a single slice, a stack of slices across a limited anatomical region, or a whole body protocol is considered. Common image post-processing steps and emerging atlas-based automated segmentation techniques are noted. Finally, the article identifies some directions of future research, including a discussion on the growing topic of brown adipose tissue and related segmentation considerations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Wang Y, Lobstein T (2006) Worldwide trends in childhood overweight and obesity. Int J Ped Obes 1:11–25

    Article  Google Scholar 

  2. Flegal KM, Carroll MD, Ogden CL, Curtin LR (2010) Prevalence and trends in obesity among US adults, 1999–2008. JAMA 303:235–241

    Article  CAS  PubMed  Google Scholar 

  3. Després JP, Lemieux I, Bergeron J et al (2008) Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol 28:1039–1049

    Article  PubMed  CAS  Google Scholar 

  4. Matsuzawa Y, Funahashi T, Nakamura T (2011) The concept of metabolic syndrome: contribution of visceral fat accumulation and its molecular mechanism. J Atheroscler Thromb 18:629–639

    Article  CAS  PubMed  Google Scholar 

  5. Bizino MB, Sala ML, de Heer P et al (2015) MR of multi-organ involvement in the metabolic syndrome. Magn Reson Imaging Clin N Am 23:41–58

    Article  PubMed  Google Scholar 

  6. Silver HJ, Welch EB, Avison MJ, Niswender KD (2010) Imaging body composition in obesity and weight loss: challenges and opportunities. Diabetes Metab Syndr Obes 28:337–347

    Article  Google Scholar 

  7. Machann J, Horstmann A, Born M, Hesse S, Hirsch FW (2013) Diagnostic imaging in obesity. Best Pract Res Clin Endocrinol Metab 27:261–277

    Article  PubMed  Google Scholar 

  8. MacDonald AJ, Greig CA, Baracos V (2011) The advantages and limitations of cross-sectional body composition analysis. Curr Opin Support Palliat Care 5:342–349

    Article  PubMed  Google Scholar 

  9. Lemieux S, Prud’homme D, Bouchard C, Tremblay A, Després JP (1993) Sex differences in the relation of visceral adipose tissue accumulation to total body fatness. Am J Clin Nutr 58:463–467

    CAS  PubMed  Google Scholar 

  10. Conway JM, Yanovski SZ, Avila NA, Hubbard VS (1995) Visceral adipose tissue differences in black and white women. Am J Clin Nutr 61:765–771

    CAS  PubMed  Google Scholar 

  11. Kadowaki T, Sekikawa A, Murata K et al (2006) Japanese men have larger areas of visceral adipose tissue than Caucasian men in the same levels of waist circumference in a population-based study. Int J Obes (Lond) 30:1163–1165

    Article  CAS  Google Scholar 

  12. Koska J, Stefan N, Votruba SB, Smith SR, Krakoff J, Bunt JC (2008) Distribution of subcutaneous fat predicts insulin action in obesity in sex-specific manner. Obesity (Silver Spring) 16:2003–2009

    Article  CAS  Google Scholar 

  13. Engelson ES, Kotler DP, Tan Y et al (1999) Fat distribution in HIV-infected patients reporting trances enlargement quantified by whole-body magnetic resonance imaging. Am J Clin Nutr 69:1162–1169

    CAS  PubMed  Google Scholar 

  14. Klein S, Fontana L, Young VL et al (2004) Absence of an effect of liposuction on insulin action and risk factors for coronary heart disease. N Engl J Med 350:2549–2557

    Article  CAS  PubMed  Google Scholar 

  15. Mayer LE, Klein DA, Black E et al (2009) Adipose tissue distribution after weight restoration and weight maintenance in women with anorexia nervosa. Am J Clin Nutr 90:1132–1137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cordes C, Dieckmeyer M, Ott B et al (2015) MR-detected changes in liver fat, abdominal fat, and vertebral bone marrow fat after a four-week calorie restriction in obese women. J Magn Reson Imaging. doi:10.1002/jmri.24908

    PubMed  Google Scholar 

  17. Machann J, Thamer C, Stefan N et al (2010) Follow-up whole-body assessment of adipose tissue compartments during a lifestyle intervention in a large cohort at increased risk for type 2 diabetes. Radiology 257:353–363

    Article  PubMed  Google Scholar 

  18. Anblagan D, Deshpande R, Jones NW et al (2013) Measurement of fetal fat in utero in normal and diabetic pregnancies using magnetic resonance imaging. Ultrasound Obstet Gynecol 42:335–340

    Article  CAS  PubMed  Google Scholar 

  19. Kabir N, Forsum E (1993) Estimation of total body fat and subcutaneous adipose tissue in full-term infants less than 3 months old. Pediatr Res 34:448–454

    Article  CAS  PubMed  Google Scholar 

  20. Harrington TA, Thomas EL, Frost G, Modi N, Bell JD (2004) Distribution of adipose tissue in the newborn. Pediatr Res 55:437–441

    Article  PubMed  Google Scholar 

  21. Brambilla P, Bedogni G, Moreno LA et al (2006) Crossvalidation of anthropometry against magnetic resonance imaging for the assessment of visceral and subcutaneous adipose tissue in children. Int J Obes (Lond) 30:23–30

    Article  CAS  Google Scholar 

  22. Fields DA, Teague AM, Short KR, Chernausek SD (2015) Evaluation of DXA vs. MRI for body composition measures in 1-month olds. Pediatr Obes. doi:10.1111/ijpo.12021

    Google Scholar 

  23. Lange T, Beuchert M, Baumstark MW et al (2015) Value of MRI and MRS fat measurements to complement conventional screening methods for childhood obesity. J Magn Reson Imaging. doi:10.1002/jmri.24919

    Google Scholar 

  24. Siegel MJ, Hildebolt CF, Bae KT, Hong C, White NH (2007) Total and intraabdomianl fat distribution in preadolescents and adolescents: measurement with MR imaging. Radiology 242:846–856

    Article  PubMed  Google Scholar 

  25. Khoo CM, Leow MK, Sadananthan SA et al (2014) Body fat partitioning does not explain the interethnic variation in insulin sensitivity among Asian ethnicity: the Singapore adult metabolism study. Diabetes 63:1093–1102

    Article  CAS  PubMed  Google Scholar 

  26. Stefan N, Kantartzis K, Machann J et al (2008) Identification and characterization of metabolically benign obesity in humans. Arch Intern Med 168:1609–1616

    Article  PubMed  Google Scholar 

  27. Thomas EL, Parkinson JR, Frost GS et al (2012) The missing risk: MRI and MRS phenotyping of abdominal adiposity and ectopic fat. Obesity (Silver Spring) 20:76–87

    Article  CAS  Google Scholar 

  28. St-Onge MP, Janssen I, Heymsfield SB (2004) Metabolic syndrome in normal-weight Americans: new definition of the metabolically obese, normal-weight individual. Diabetes Care 27:2222–2228

    Article  PubMed  Google Scholar 

  29. Kelishadi R, Cook SR, Motlagh ME et al (2008) Metabolically obese normal weight and phenotypically obese metabolically normal youths: the CASPIAN study. J Am Diet Assoc 108:82–90

    Article  CAS  PubMed  Google Scholar 

  30. Kullberg J, Johansson L, Lind L, Ahlström H, Strand R (2015) Imiomics: bringing -omics to whole body imaging: examples in cross-sectional interaction between whole-body MRI and non-imaging data. In: Proceedings of the 23rd scientific meeting, International Society for Magnetic Resonance in Medicine, Toronto, p 3757

  31. Shen W, Wang Z, Punyanitya M et al (2003) Adipose tissue quantification by imaging methods: a proposed classification. Obes Res 11:5–16

    Article  PubMed  PubMed Central  Google Scholar 

  32. Thomas LW (1962) The chemical composition of adipose tissue of man and mice. Q J Exp Physiol Cogn Med Sci 47:179–188

    CAS  PubMed  Google Scholar 

  33. Lee YS, Gallagher D (2008) Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care 11:566–572

    Article  PubMed  PubMed Central  Google Scholar 

  34. Thomas EL, Fitzpatrick JA, Malik SJ, Taylor-Robinson SD, Bell JD (2013) Whole body fat: content and distribution. Prog Nucl Magn Reson Spectrosc 73:56–80

    Article  CAS  PubMed  Google Scholar 

  35. Kaul S, Rothney MP, Peters DM et al (2012) Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring) 20:1313–1318

    Article  Google Scholar 

  36. Mourtzakis M, Prado CM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE (2008) A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab 33:997–1006

    Article  PubMed  Google Scholar 

  37. Kullberg J, Brandberg J, Angelhed JE et al (2009) Whole-body adipose tissue analysis: comparison of MRI, CT, and dual energy X-ray absorptiometry. Br J Radiol 82:123–130

    Article  CAS  PubMed  Google Scholar 

  38. Bredella MA, Ghomi RH, Thomas BJ et al (2010) Comparison of DXA and CT in the assessment of body composition in premenopausal women with obesity and anorexia nervosa. Obesity (Silver Spring) 18:2227–2233

    Article  Google Scholar 

  39. Metzinger MN, Miramontes B, Zhou P et al (2014) Correlation of X-ray computed tomography with quantitative nuclear magnetic resonance methods for pre-clinical measurement of adipose and lean tissues in living mice. Sensors (Basel) 14:18526–18542

    Article  PubMed Central  Google Scholar 

  40. Varady KA, Santosa S, Jones PJ (2007) Validation of hand-held bioelectrical impedance analysis with magnetic resonance imaging for the assessment of body composition in overweight women. Am J Hum Biol 19:429–433

    Article  PubMed  Google Scholar 

  41. Ludescher B, Machann J, Eschweiler GW et al (2009) Correlation of fat distribution in whole body MRI with generally used anthropometric data. Invest Radiol 44:712–719

    Article  PubMed  Google Scholar 

  42. Browning LM, Mugridge O, Dixon AK, Aitken SW, Prentice AM, Jebb SA (2011) Measuring abdominal adipose tissue: comparison of simpler methods with MRI. Obes Facts 4:9–15

    Article  PubMed  Google Scholar 

  43. Ludwig UA, Klausmann F, Baumann S et al (2014) Whole-body MRI-based fat quantification: a comparison to air displacement plethysmography. J Magn Reson Imaging 40:1437–1444

    Article  PubMed  Google Scholar 

  44. Karlsson AK, Kullberg J, Stokland E, Allvin K, Gronowitz E, Svensson PA, Dahlgren J (2013) Measurements of total and regional body composition in preschool children: a comparison of MRI, DXA, and anthropometric data. Obesity (Silver Spring) 21:1018–1024

    Article  Google Scholar 

  45. Silver HJ, Niswender KD, Kullberg J et al (2013) Comparison of gross body fat–water magnetic resonance imaging at 3 Tesla to dual-energy X-ray absorptiometry in obese women. Obesity (Silver Spring) 21:765–774

    Article  Google Scholar 

  46. Clarke LP, Velthuizen RP, Camacho MA et al (1995) MRI segmentation: methods and applications. Magn Reson Imaging 13:343–368

    Article  CAS  PubMed  Google Scholar 

  47. Baba S, Jacene HA, Engles JM, Honda H, Wahl RL (2010) CT Hounsfield units of brown adipose tissue increase with activation: preclinical and clinical studies. J Nucl Med 51:246–250

    Article  PubMed  Google Scholar 

  48. Ahmadi N, Hajsadeghi F, Conneely M et al (2013) Active brown and white adipose tissues with computed tomography. Acad Radiol 20:1443–1447

    Article  PubMed  Google Scholar 

  49. Kvist H, Chowdhury B, Grangård U, Tylén U, Sjöström L (1988) Total and visceral adipose tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. Am J Clin Nutr 48:1351–1361

    CAS  PubMed  Google Scholar 

  50. Yoshizumi T, Nakamura T, Yamane M et al (1999) Abdominal fat: standardized technique for measurement at CT. Radiology 211:283–286

    Article  CAS  PubMed  Google Scholar 

  51. Maurovich-Horvat P, Massaro J, Fox CS, Moselewski F, O’Donnell CJ, Hoffmann U (2006) Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomopgrahy. Int J Obes (Lond) 31:500–506

    Article  Google Scholar 

  52. El-Serag H, Hashmi A, Garcia J et al (2014) Visceral abdominal obesity measured by CT scan is associated with an increased risk of Barrett’s oesophagus: a case–control study. Gut 63:220–229

    Article  PubMed  PubMed Central  Google Scholar 

  53. Seidell JC, Bakker CJ, van der Kooy K (1990) Imaging techniques for measuring adipose tissue distribution—a comparison between computed tomography and 1.5T magnetic resonance. Am J Clin Nutr 51:953–957

    CAS  PubMed  Google Scholar 

  54. Enzi G, Biondetti PR, Fiore D, Semisa M, Zurlo F (1986) Subcutaneous and visceral fat distribution according to sex, age, and overweight, evaluated by computed tomopgrahy. Am J Clin Nutr 44:739–746

    CAS  PubMed  Google Scholar 

  55. Sobol W, Rossner S, Hinson B et al (1991) Evaluation of a new magnetic resonance imaging method for quntitating adipose tissue areas. Int J Obes (Lond) 15:589–599

    CAS  Google Scholar 

  56. Fowler PA, Fuller MF, Glasbey CA et al (1991) Total and subcutaneous adipose tissue in women: the measurement of distribution and accurate prediction of quantity by using magnetic resonance imaging. Am J Clin Nutr 54:18–25

    CAS  PubMed  Google Scholar 

  57. Shen W, Liu H, Punyanitya M, Chen J, Heymsfield SB (2005) Pediatric obesity phenotyping by magnetic resonance imaging. Curr Opin Clin Nutr Metab Care 8:595–601

    Article  PubMed  PubMed Central  Google Scholar 

  58. Fowler PA, Fuller MF, Glasbey CA, Cameron CG, Foster MA (1992) Validation of in vivo measurement of adipose tissue by magnetic resonance imaging of lean and obese pig. Am J Clin Nutr 56:7–13

    CAS  PubMed  Google Scholar 

  59. Mitchell AD, Scholz AM, Wange PC, Song H (2001) Body composition analysis of the pig by magnetic resonance imaging. J Anim Sci 79:1800–1813

    CAS  PubMed  Google Scholar 

  60. Ranefall P, Bidar AW, Hockings PD (2009) Automatic segmentation of intra-abdominal and subcutaneous adipose tissue in 3D whole mouse MRI. J Magn Reson Imaging 30:554–560

    Article  PubMed  Google Scholar 

  61. Luu YK, Lublinsky S, Ozcivici E, Capilla E, Pessin JE, Rubin CT, Judex S (2009) In vivo quantification of subcutaneous and visceral adiposity by micro-computed tomography in a small animal model. Med Eng Phys 31:34–41

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Johnson DH, Flask CA, Ernsberger PR, Wong WC, Wilson DL (2008) Reproducible MRI measurement of adipose tissue volumes in genetic and dietary rodent obesity models. J Magn Reson Imaging 28:915–927

    Article  PubMed  PubMed Central  Google Scholar 

  63. Johnson DH, Narayan S, Wilson DL, Flask CA (2012) Body composition analysis of obesity and hepatic steatosis in mice by relaxation compensated fat fraction (RCFF) MRI. J Magn Reson Imaging 35:837–843

    Article  PubMed  PubMed Central  Google Scholar 

  64. Tang Y, Sharma P, Nelson MD, Simerly R, Moats RA (2011) Automatic abdominal fat assessment in obese mice using a segmented shape model. J Magn Reson Imaging 34:866–873

    Article  PubMed  Google Scholar 

  65. Sasser TA, Chapman SE, Li S et al (2012) Segmentation and measurement of fat volumes in murine obesity models using X-ray computed tomography. J Vis Exp. doi:10.3791/3680

    PubMed  PubMed Central  Google Scholar 

  66. Garteiser P, Doblas S, Towner RA, Griffin TM (2013) Calibration of a semi-automated segmentation method for quantification of adipose tissue compartments from magnetic resonance images of mice. Metabolism 62:1686–1695

    Article  CAS  PubMed  Google Scholar 

  67. Gifford A, Kullberg J, Berglund J et al (2014) Canine body composition quantification using 3 Tesla fat–water MRI. J Magn Reson Imaging 39:485–491

    Article  PubMed  PubMed Central  Google Scholar 

  68. Monziols M, Collewet G, Mariette F, Kouba M, Davenel A (2005) Muscle and fat quantification in MRI gradient echo images using a partial volume detection method. Application to the characterization of pig belly tissue. Magn Reson Imaging 23:745–755

    Article  CAS  PubMed  Google Scholar 

  69. Kremer PV, Förster Scholz AM (2013) Use of magnetic resonance imaging to predict the body composition of pigs in vivo. Animal 7:879–884

    Article  CAS  PubMed  Google Scholar 

  70. Hu HH, Kan HE (2013) Quantitative proton MR techniques for measuring fat. NMR Biomed 26:1609–1629

    Article  CAS  PubMed  Google Scholar 

  71. Gronemeyer SA, Steen RG, Kauffman WM, Reddick WE, Glass JO (2000) Fast adipose tissue (FAT) assessment by MRI. Magn Reson Imaging 18:815–818

    Article  CAS  PubMed  Google Scholar 

  72. Schick F (2005) Whole-body MRI at high field: technical limits and clinical potential. Eur Radiol 15:946–959

    Article  PubMed  Google Scholar 

  73. Katscher U, Börnert P (2006) Parallel RF transmission in MRI. NMR Biomed 19:393–400

    Article  PubMed  Google Scholar 

  74. Axel L, Constantini J, Listerud J (1987) Intensity correction in surface-coil MR imaging. AJR 148:418–420

    Article  CAS  PubMed  Google Scholar 

  75. Wang D, Doddrell DM (2005) Method for a detailed measurement of image intensity nonuniformity in magnetic resonance imaging. Med Phys 32:952–960

    Article  PubMed  Google Scholar 

  76. Chen W (2004) A fuzzy c-means (FCM) based algorithm for intensity inhomogeneity correction and segmentation of MR images. In: IEEE international symposium on biomedical imaging: nano to macro, pp 1307–1310

  77. Lin M, Chan S, Chen JH et al (2011) A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI. Med Phys 38:5–14

    Article  PubMed  PubMed Central  Google Scholar 

  78. Hou Z (2006) A review of MR image intensity inhomogeneity correction. Int J Biomed Imaging. doi:10.1155/IJBI/2006/49151

    PubMed  PubMed Central  Google Scholar 

  79. Peterson P, Romu T, Brorson H, Leinhard OD, Månsson S (2015) Fat quantification in skeletal muscle using multigradient-echo imaging: comparison of fat and water references. J Magn Reson Imaging. doi:10.1002/jmri.24972

    PubMed Central  Google Scholar 

  80. Collewet G, Davenel A, Toussaint C, Akoka S (2002) Correction of intensity nonuniformity in spin-echo T1-weighted images. Magn Reson Imaging 20:365–373

    Article  CAS  PubMed  Google Scholar 

  81. Yang GZ, Myerson S, Chabat F, Pennell DJ, Firmin DN (2002) Automatic MRI adipose tissue mapping using overlapping mosaics. MAGMA 14:39–44

    Article  CAS  PubMed  Google Scholar 

  82. Kullberg J, Ahlström H, Johansson L, Frimmel H (2007) Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI. Int J Obes (Lond) 31:1806–1817

    Article  CAS  Google Scholar 

  83. Positano V, Cusi K, Santarelli MF et al (2008) Automatic correction of intensity inhomogeneities improves unsupervised assessment of abdominal fat by MRI. J Magn Reson Imaging 28:403–410

    Article  PubMed  Google Scholar 

  84. Leinhard OD, Johansson A, Rydell J, Smedby O, Nystrom F, Lundberg P, Borga M (2008) Quantitative abdominal fat estimation using MRI. In: IEEE international conference on pattern recognition, vol 19, pp 1–4. doi:10.1109/ICPR.2008.4761764

  85. Romu T, Borga M, Leinhard OD (2011) MANA – Multi scale adaptive normalized averaging. In: IEEE international symposium on biomedical imaging: nano to macro, pp 361–364. doi:10.1109/ISBI.2011.5872424

  86. Andersson T, Romu T, Karlsson A et al (2015) Consistent intensity inhomogeneity correction in water–fat MRI. J Magn Reson Imaging 42:468–476

    Article  PubMed  Google Scholar 

  87. Sussman DL, Yao J, Summers RM (2010) Automated fat measurement and segmentation with intensity inhomogeneity correction. In: Proceedings of SPIE 7623, medical imaging 2010: image processing, 76233X

  88. Azzabou N, de Sousa PL, Carlier PG (2010) Non-uniformity correction using cosine functions basis and total variation constraint. In: IEEE international symposium on biomedical imaging: nano to macro, pp 748–751. doi:10.1109/ISBI.2010.5490068

  89. Mosbech TH, Pilgaard K, Vaag A, Larsen R (2011) Automatic segmentation of abdominal adipose tissue in MRI. In: Image analysis: 17th Scandinavian conference, SCIA, lecture notes in computer science, pp 501–511. ISBN:978-3-642-21226-0

  90. Würslin C, Springer F, Yang B, Schick F (2011) Compensation of RF field and receiver coil induced inhomogeneity effects in abdominal MR images by a priori knowledge of the human adipose tissue distribution. J Magn Reson Imaging 34:716–726

    Article  PubMed  Google Scholar 

  91. Reeder SB, Hu HH, Sirlin CB (2012) Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging 36:1011–1014

    Article  PubMed  PubMed Central  Google Scholar 

  92. Addeman BT, Kutty S, Perkins TG et al (2014) Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method. J Magn Reson Imaging 41:233–241

    Article  PubMed  Google Scholar 

  93. Poonawalla AH, Sjoberg BP, Rehm JL et al (2013) Adipose tissue MRI for quantitative measurement of central obesity. J Magn Reson Imaging 37:707–716

    Article  PubMed  PubMed Central  Google Scholar 

  94. Hernando D, Levin YS, Sirlin CB, Reeder SB (2014) Quantification of liver iron with MRI: state of the art and remaining challenges. J Magn Reson Imaging 40:1003–1021

    Article  PubMed  PubMed Central  Google Scholar 

  95. Li Z, Graff C, Gmitro AF, Squire SW, Bilgin A, Outwater EK, Altbach MI (2009) Rapid water and lipid imaging with T2 mapping using a radial IDEAL-GRASE technique. Magn Reson Med 61:1415–1424

    Article  PubMed  PubMed Central  Google Scholar 

  96. Pandey A, Bilgin A, Cumar S, Kalb B, Martin DR, Altbach MI (2013) Automated segmentation of liver parenchyma and blood vessel with in vivo radial Gradient and Spin-Echo (GRASE) datasets for characterization of diffuse liver disease. In: Proceedings of the 21st scientific meeting, International Society for Magnetic Resonance in Medicine, Salt Lake City, p 1528

  97. Kan HE, Scheenen TW, Wohlgemuth M, Klomp DW, van Loosbroek-Wagenmans I, Padberg GW, Heerschap A (2009) Quantitative MR imaging of individual muscle involvement in facioscapulohumeral muscular dystrophy. Neuromuscul Disord 9:357–362

    Article  Google Scholar 

  98. Alabousi A, Al-Attar S, Joy TR, Hegele RA, McKenzie CA (2011) Evaluation of adipose tissue volume quantification with IDEAL fat–water separation. J Magn Reson Imaging 34:474–479

    Article  PubMed  Google Scholar 

  99. Walker GE, Verti B, Marzullo P et al (2007) Deep subcutaneous adipose tissue: a distinct abdominal adipose depot. Obesity (Silver Spring) 15:1933–1943

    Article  CAS  Google Scholar 

  100. Lundbom J, Hakkarainen A, Lundbom N, Taskinen MR (2013) Deep subcutaneous adipose tissue is more saturated than superficial subcutaneous adipose tissue. Int J Obes (Lond) 37:620–622

    Article  CAS  Google Scholar 

  101. Kelley DE, Thaete FL, Troost F, Huwe T, Goodpaster BH (2000) Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance. Am J Physiol Endocrinol Metab 278:E941–E948

    CAS  PubMed  Google Scholar 

  102. Misra A, Garg A, Abate N, Peshock RM, Stray-Gundersen J, Grundy SM (1997) Relationship of anterior and posterior subcutaneous abdominal fat to insulin sensitivity in nondiabetic men. Obes Res 5:93–99

    Article  CAS  PubMed  Google Scholar 

  103. Ross R, Aru J, Freeman J, Hudson R, Janssen I (2002) Abdominal adiposity and insulin resistance in obese men. Am J Physiol Endocrinol Metab 282:E657–E663

    Article  CAS  PubMed  Google Scholar 

  104. Nichols JH, Samy B, Nasir K et al (2008) Volumetric measurement of pericardial adipose tissue from contrast-enhanced coronary computed tomography angiography: a reproducibility study. J Cardiovasc Comput Tomogr 2:288–295

    Article  PubMed  PubMed Central  Google Scholar 

  105. Shahzad R, Bos D, Metz C et al (2013) Automatic quantification of epicardial fat volume on non-enhanced cardiac CT scans using a multi-atlas segmentation approach. Med Phys 40:09190

    Article  Google Scholar 

  106. Elming MB, Lønborg J, Rasmussen T et al (2013) Measurement of pericardial adipose tissue using contrast enhanced cardiac multidetector computed tomography—comparison with cardiac magnetic resonance imaging. Int J Cardiovasc Imaging 29:1401–1407

    Article  PubMed  Google Scholar 

  107. Mihl C, Loeffen D, Versteylen MO et al (2014) Automated quantification of epicardial adipose tissue (EAT) in coronary CT angiopgraphy: comparison with manual assessment and correlation with coronary artery disease. J Cardiovasc Comput Tomogr 8:215–221

    Article  PubMed  Google Scholar 

  108. Spearman JV, Meinel FG, Schoepf UJ et al (2014) Automated quantification of epicardial adipose tissue using CT angiography: evaluation of a prototype software. Eur Radiol 24:519–526

    Article  PubMed  Google Scholar 

  109. Kortelainen ML, Särkioja T (2001) Visceral fat and coronary pathology in male adolescents. Int J Obes Relat Metab Disord 25:228–232

    Article  CAS  PubMed  Google Scholar 

  110. Liu KH, Chan YL, Chan WB, Chan JC, Chu CW (2006) Mesenteric fat thickness is an independent determinant of metabolic syndrome and identifies subjects with increased carotid intima-media thickness. Diabetes Care 29:379–384

    Article  PubMed  Google Scholar 

  111. Bergman RN, Kim SP, Hsu I et al (2007) Abdominal obesity: role in the pathophysiology of metabolic disease and cardiovascular risk. Am J Med 120(2 Suppl 1):S3–S8

    Article  CAS  PubMed  Google Scholar 

  112. Fabbrini E, Tamboli RA, Magkos F et al (2010) Surgical removal of omental fat does not improve insulin sensitivity and cardiovascular risk factors in obese adults. Gastroenterology 139:448–455

    Article  PubMed  PubMed Central  Google Scholar 

  113. Albu JB, Kovera AJ, Allen L et al (2005) Independent association of insulin resistance with larger amounts of intermuscular adipose tissue and a greater actue insulin response to glucose in African American than in white nondiabetic women. Am J Clin Nutr 82:1210–1217

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Yim JE, Heshka S, Albu J et al (2007) Intermuscular adipose tissue rivals visceral adipose tissue in independent associations with cardiovascular risk. Int J Obes (Lond) 31:1400–1405

    Article  CAS  Google Scholar 

  115. Boettcher M, Machann J, Stefan N et al (2009) Intermuscular adipose tissue: association with other adipose tissue compartments and insulin sensitivity. J Magn Reson Imaging 29:1340–1345

    Article  PubMed  Google Scholar 

  116. Al-Attar SA, Pollex RL, Robinson JF et al (2006) Semi-automated segmentation and quantification of adipose tissue in calf and thigh by MRI: a preliminary study in patients with monogenic metabolic syndrome. BMC Med Imaging 31:6–11

    Google Scholar 

  117. Karampinos DC, Baum T, Nardo L et al (2012) Characterization of the regional distribution of skeletal muscle adipose tissue in type 2 diabetes using chemical shift-based water/fat separation. J Magn Reson Imaging 35:899–907

    Article  PubMed  PubMed Central  Google Scholar 

  118. Mattei JP, Fur YL, Cuge N, Guis S, Cozzone PJ, Bendahan D (2006) Segmentation of fascias, fat and muscle from magnetic resonance images in humans: the DISPIMAG software. MAGMA 19:275–279

    Article  CAS  PubMed  Google Scholar 

  119. Orgiu S, Lafortuna CL, Rastelli F, Cadioli M, Falini A, Rizzo G (2015) Automatic muscle and fat segmentation in the thigh from T1-weighted MRI. J Magn Reson Imaging. doi:10.1002/jmri.25031

    PubMed  Google Scholar 

  120. Casazza K, Hanks LJ, Hildalgo B, Hu HH, Affuso O (2012) Short-term physical activity intervention decreases femoral bone marrow adipose tissue in young children: a pilot study. Bone 50:23–27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Bathija A, Davis S, Trubowitz S (1979) Bone marrow adipose tissue: response to acute starvation. Am J Hematol 6:191–198

    Article  CAS  PubMed  Google Scholar 

  122. Schwartz AV, Sigurdsson S, Hue TF et al (2013) Vertebral bone marrow fat associated with lower trabecular BMD and prevalent vertebral fracture in older adults. J Clin Endocrinol Metab 98:2294–2300

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Karampinos DC, Melkus G, Baum T, Bauer JS, Rummeny EJ, Krug R (2014) Bone marrow fat quantification in the presence of trabecular bone: initial comparison between water–fat imaging and single-voxel MRS. Magn Reson Med 71:1158–1165

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Demerath EW, Shen W, Lee M et al (2007) Approximation of total visceral adipose tissue with a single magnetic resonance image. Am J Clin Nutr 85:362–368

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Schaudinn A, Linder N, Garnov N et al (2015) Predictive accuracy of single- and multi-slice MRI for the estimation of total visceral adipose tissue in overweight to severely obese patients. NMR Biomed 28:583–590

    Article  PubMed  Google Scholar 

  126. Machann J, Thamer C, Schnoedt B et al (2005) Standardized assessment of whole body adipose tissue topography by MRI. J Magn Reson Imaging 21:455–462

    Article  PubMed  Google Scholar 

  127. Börnert P, Keupp J, Eggers H, Aldefeld B (2007) Whole-body 3D water/fat resolved continuously moving table imaging. J Magn Reson Imaging 25:660–665

    Article  PubMed  Google Scholar 

  128. Shen W, Chen J, Gantz M, Velasquez G, Punyanitya M, Heymsfield SB (2012) A single MRI slice does not accurately predict visceral and subcutaneous adipose tissue changes during weight loss. Obesity (Silver Spring) 20:2458–2463

    Article  Google Scholar 

  129. Shen W, Punyanitya M, Wang Z et al (2004) Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol 97:2333–2338

    Article  PubMed  Google Scholar 

  130. Shen W, Punyanitya M, Wang Z et al (2004) Visceral adipose tissue: relations between single-slice areas and total volume. Am J Clin Nutr 80:271–278

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Shen W, Punyanitya M, Chen J et al (2007) Visceral adipose tissue: relationships between single slice areas at different locations and obesity-related health. Int J Obes (Lond) 31:763–769

    CAS  Google Scholar 

  132. Irlbeck T, Jm Massaro, Bamberg F, O’Donnel CJ, Hoffman U, Fox CS (2010) Association between single-slice measurements of visceral and abdominal subcutaneous adipose tissue with volumetric measurements: the Framingham Heart Study. Int J Obes (Lond) 34:781–787

    Article  CAS  Google Scholar 

  133. Kuk JL, Church TS, Blair SN, Ross R (2006) Does measurement site for visceral and abdominal subcutaneous adipose tissue alter associations with the metabolic syndrome? Diabetes Care 29:679–684

    Article  PubMed  Google Scholar 

  134. Kuk JL, Church TS, Blair SN, Ross R (2010) Measurement site and the association between visceral and abdominal subcutaneous adipose tissue with metabolic risk in women. Obesity (Silver Spring) 18:1336–1340

    Article  CAS  Google Scholar 

  135. Thomas EL, Bell JD (2003) Influence of undersampling on magnetic resonance imaging measurements of intra-abdominal adipose tissue. Int J Obes Relat Metab Disord 27:211–218

    Article  CAS  PubMed  Google Scholar 

  136. Springer F, Ehehalt S, Sommer J et al (2012) Predicting volumes of metabolically important whole-body adipose tissue compartments in overweight and obese adolescents by different MRI approaches and anthropometry. Eur J Radiol 81:1488–1494

    Article  PubMed  Google Scholar 

  137. Thomas EL, Saeed N, Hajnal JV et al (1998) Magnetic resonance imaging of total body fat. J Appl Physiol 85:1778–1785

    CAS  PubMed  Google Scholar 

  138. Shen W, Chen J, Kwak S, Punyanitya M, Heymsfield SB (2011) Between-slice intervals in quantification of adipose tissue and muscle in children. Int J Pediatr Obesity 6:149–156

    Article  Google Scholar 

  139. Schwenzer NF, Machann J, Schraml C et al (2010) Quantitative analysis of adipose tissue in single transverse slices for estimation of volumes of relevant fat tissue compartments. Invest Radiol 45:788–794

    Article  PubMed  Google Scholar 

  140. Maislin G, Ahmed MM, Gooneratne N et al (2012) Single slice vs. volumetric MR assessment of visceral adipose tissue, reliability and validity among the overweight and obese. Obesity (Silver Spring) 20:2124–2132

    Article  Google Scholar 

  141. Jin Y, Imielinska CZ, Laine AF, Udupa J, Shen W, Heymsfield SB (2003) Segmentation and evaluation of adipose tissue from whole body MRI scans. In: Medical image computing and computer-assisted intervention—MICCAI 2003 lecture notes in computer science, vol 2878, pp 635–642

  142. Zhao B, Colville J, Kalaigian J, Curran S, Jiang L, Kijewski P, Schwartz LH (2006) Automated quantification of body fat distribution on volumetric compute tomography. J Comput Assist Tomogr 30:777–783

    Article  PubMed  Google Scholar 

  143. Ohshima S, Yamamoto S, Yamaji T et al (2008) Development of an automated 3D segmentation program for volume quantification of body fat distribution using CT. Jpn J Radiol Tech 64:1177–1181

    Article  Google Scholar 

  144. Makrogiannis S, Caturegli G, Davatzikos C, Ferrucci L (2013) Computer-aided assessment of regional abdominal fat with food residue removal in CT. Acad Radiol 20:1413–1421

    Article  PubMed  PubMed Central  Google Scholar 

  145. Nemoto M, Yeernuer T, Masutani Y et al (2014) Development of automatic visceral fat volume calculation software for CT volume data. J Obes. doi:10.1155/2014/495084

    PubMed  PubMed Central  Google Scholar 

  146. Liou TH, Chan WP, Pan LC, Lin PW, Chou P (2006) Chen CH (2006) Fully automated large-scale assessment of visceral and subcutaneous abdominal adipose tissue by magnetic resonance imaging. Int J Obes (Lond) 30:844–852

    Article  Google Scholar 

  147. Armao D, Guyon JP, Firat Z, Brown MA, Semelka RC (2006) Accurate quantification of visceral adipose tissue (VAT) using water-saturation MRI and computer segmentation: preliminary results. J Magn Reson Imaging 23:736–741

    Article  PubMed  Google Scholar 

  148. Kullberg J, Johansson L, Ahlström H, Courivaud F, Koken P, Eggers H, Börnert P (2009) Automated assessment of whole-body adipose tissue depots from continuously moving bed MRI: a feasibility study. J Magn Reson Imaging 30:185–193

    Article  PubMed  Google Scholar 

  149. Kullberg J, Karlsson AK, Stokland E, Svensson PA, Dahlgren J (2010) Adipose tissue distribution in children: automated quantification using water and fat MRI. J Magn Reson Imaging 32:204–210

    Article  PubMed  Google Scholar 

  150. Nakai R, Azuma T, Kishimoto T, Hirata T, Takizawa O, Hyon SH, Tsutsumi S (2010) Development of a high-precision image-processing automatic measurement system for MRI visceral fat images acquired using a binomial RF-excitation pulse. Magn Reson Imaging 28:520–526

    Article  PubMed  Google Scholar 

  151. Würslin C, Machann J, Rempp H, Claussen C, Yang B, Schick F (2010) Topography mapping of whole body adipose tissue using a fully automated and standardized procedure. J Magn Reson Imaging 31:430–439

    Article  PubMed  Google Scholar 

  152. Zhou A, Murillo H, Peng Q (2011) Novel segmentation method for abdominal fat quantification by MRI. J Magn Reson Imaging 34:852–860

    Article  PubMed  Google Scholar 

  153. Wald D, Teucher B, Dinkel J et al (2012) Automatic quantification of subcutaneous and visceral adipose tissue from whole-body magnetic resonance images suitable for large cohort studies. J Magn Reson Imaging 36:1421–1434

    Article  PubMed  Google Scholar 

  154. Joshi AA, Hu HH, Leahy RM, Goran MI, Nayak KS (2013) Automatic intra-subject registration-based segmentation of abdominal fat from water–fat MRI. J Magn Reson Imaging 37:423–430

    Article  PubMed  PubMed Central  Google Scholar 

  155. Thörmer G, Bertram HH, Garnov N et al (2013) Software for automated MRI-based quantification of abdominal fat and preliminary evaluation in morbidly obese patients. J Magn Reson Imaging 37:1144–1150

    Article  PubMed  Google Scholar 

  156. Sadananthan SA, Prakash B, Leow MK et al (2015) Automated segmentation of visceral and subcutaneous (deep and superficial) adipose tissues in normal and overweight men. J Magn Reson Imaging 41:924–934

    Article  PubMed  Google Scholar 

  157. Senseney J, Hemler PF, McAuliffe MJ (2009) Automated segmentation of computed tomography images. In: Proceedings of the 26th IEEE international symposium on computer-based medical systems, pp 1–7. doi:10.1109/CBMS.2009.5255342

  158. Positano V, Christiansen T, Santarelli MF, Ringgaard S, Landini L, Gastaldelli A (2009) Accurate segmentation of subcutaneous and intermuscular adipose tissue from MR images of the thigh. J Magn Reson Imaging 29:677–684

    Article  PubMed  Google Scholar 

  159. Prescott JW, Priddy M, Best TM et al (2009) An automated method to detect interstitial adipose tissue in thigh muscles for patients with osteoarthritis. In: Conference Proceedings of IEEE Engineering in Medicine and Biology Society, pp 6360–6363

  160. Makrogiannis S, Serai S, Fishbein KW, Schreiber C, Ferrucci L, Spencer RG (2012) Automated quantification of muscle and fat in the thigh from water-, fat- and nonsuppressed MR images. J Magn Reson Imaging 35:1152–1161

    Article  PubMed  PubMed Central  Google Scholar 

  161. Valentinitsch A, Karmapinos DC, Alizai H, Subburaj K, Kumar D, Link TM, Majumdar S (2013) Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle. J Magn Reson Imaging 37:917–927

    Article  PubMed  PubMed Central  Google Scholar 

  162. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  163. Sezgin M, Bülent S (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146–168

    Article  Google Scholar 

  164. Bonekamp S, Ghosh P, Crawford S (2008) Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging. Int J Obes (Lond) 32:100–111

    Article  CAS  Google Scholar 

  165. Karlsson A, Rosander J, Romu T et al (2015) Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water–fat MRI. J Magn Reson Imaging 41:1558–1569

    Article  PubMed  Google Scholar 

  166. Heckemann RA, Keihaninejad S, Alijabar P, Rueckert D, Hajnal JV, Hammers A (2010) Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. NeuroImage 51:221–227

    Article  PubMed  Google Scholar 

  167. Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24:205–219

    Article  PubMed  Google Scholar 

  168. Devi CN, Chandrasekharan A, Sundararaman VK, Alex ZC (2015) Neonatal brain MRI segmentation: a review. Comput Biol Med 64:163–178

    Article  PubMed  Google Scholar 

  169. Kullberg J, Angelhed JE, Lönn L et al (2006) Whole-body T1 mapping improves the definition of adipose tissue: consequences for automated image analysis. J Magn Reson Imaging 24:394–401

    Article  PubMed  Google Scholar 

  170. Garnov N, Linder N, Schaudinn A et al (2014) Comparison of T1 relaxation times in adipose tissue of severly obese patients and healthy lean subjects measured by 1.5T MRI. NMR Biomed 27:1123–1128

    Article  PubMed  Google Scholar 

  171. Gensanne D, Josse G, Theunis J, Lagarde JM, Vincensini D (2009) Quantitative magnetic resonance imaging of subcutaneous adipose tissue. Skin Res Technol 15:45–50

    Article  CAS  PubMed  Google Scholar 

  172. Phinney SD, Stern JS, Burke KE, Tang AB, Miller G, Holman RT (1994) Human subcutaneous adipose tissue shows site-specific differences in fatty acid composition. Am J Clin Nutr 60:725–729

    CAS  PubMed  Google Scholar 

  173. Machann J, Stefan N, Schabel C et al (2013) Fraction of unsaturated fatty acids in visceral adipose tissue (VAT) is lower in subjects with high total VAT volume—a combined 1H MRS and volumetric MRI study in male subjects. NMR Biomed 26:232–236

    Article  CAS  PubMed  Google Scholar 

  174. Brunner G, Nambia V, Yang E et al (2011) Automatic quantification of muscle volumes in magnetic resonance imaging scans of the lower extremities. Magn Reson Imaging 29:1065–1075

    Article  PubMed  Google Scholar 

  175. Commean PK, Tuttle LJ, Hastings MK, Strube MJ, Mueller MJ (2011) Magnetic resonance imaging measurement reproducibility for calf muscle and adipose tissue volume. J Magn Reson Imaging 34:1285–1294

    Article  PubMed  PubMed Central  Google Scholar 

  176. Thomas MS, Newman D, Leinhard OD et al (2014) Test-retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system. Eur Radiol 24:2279–2291

    Article  PubMed  Google Scholar 

  177. Tong T, Wolz R, Wang Z et al (2015) Discriminative dictionary learning for abdominal multi-organ segmentation. Med Image Anal 23:92–104

    Article  PubMed  Google Scholar 

  178. Xu Z, Burke RP, Lee CP et al (2015) Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning. Med Image Anal 24:18–27

    Article  CAS  PubMed  Google Scholar 

  179. Borga M, Virtanen KA, Romu T et al (2014) Brown adipose tissue in humans: detection and functional analysis using PET, MRI, and DECT. Methods Enzymol 537:141–159

    Article  CAS  PubMed  Google Scholar 

  180. Cypess AM, Haft CR, Laughlin MR, Hu HH (2014) Brown fat in humans: consensus points and experimental guidelines. Cell Metab 20:408–415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Gifford A, Towse TF, Walker RC, Avison MJ, Welch EB (2015) Human brown adipose tissue depots automatically segmented by positron emission tomography/computed tomography and registered magnetic resonance images. J Vis Exp. doi:10.3791/52415

    PubMed  PubMed Central  Google Scholar 

  182. Lidell ME, Betz MJ, Leinhard OD et al (2013) Evidence for two types of brown adipose tissue in humans. Nat Med 19:631–634

    Article  CAS  PubMed  Google Scholar 

  183. Betz MJ, Enerbäck S (2015) Human brown adipose tissue: what we have learned so far. Diabetes 64:2352–2360

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

H. Hu acknowledges research support from Philips Healthcare and literature reference assistance from librarian Kathy Zeblisky, MLS, of Phoenix Children’s Hospital.

Author information

Authors and Affiliations

  1. Department of Radiology, Phoenix Children’s Hospital, 1919 East Thomas Road, Phoenix, AZ, 85016, USA

    Houchun Harry Hu

  2. Obesity Research Center, Department of Medicine, Columbia University Medical Center, 1150 Saint Nicholas Avenue, New York, NY, 10032, USA

    Jun Chen

  3. Obesity Research Center, Department of Medicine and Institute of Human Nutrition, Columbia University Medical Center, 1150 Saint Nicholas Avenue, New York, NY, 10032, USA

    Wei Shen

Authors
  1. Houchun Harry Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Houchun Harry Hu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, H.H., Chen, J. & Shen, W. Segmentation and quantification of adipose tissue by magnetic resonance imaging. Magn Reson Mater Phy 29, 259–276 (2016). https://doi.org/10.1007/s10334-015-0498-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10334-015-0498-z

Keywords

Search

Navigation