Skip to main content

Advertisement

Log in

Assisted quantification of abdominal adipose tissue based on magnetic resonance images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

An assisted method to segment Visceral Adipose Tissue (VAT) and Subcutaneous Adipose Tissue (SAT) from Magnetic Resonance Imaging (MRI) slices is presented. The segmentation process, called shape-based segmentation, consists in three main steps: 1) to draw a series of closed curves at different slices that separates the abdominal structures of interest, 2) to generate a 3D model from the closed curves for each abdominal structure by using shape-based interpolation and 3) to apply a segmentation algorithm to define the adipose tissue. The 3D models considerably simplify the problem since the abdominal structures are separated, and in turn, this reduces the possibility of large segmentation errors. In addition, a fully automatic segmentation procedure was also implemented. Twenty slices of MRI at the abdominal region for each of twelve subjects were analysed. The results of the shape-based and automatic segmentation were compared with the expert segmentation carried out in the slice located at the umbilicus level. Correlation Coefficient (CC) and volume error (VE) were used as performance measures. The comparison between the expert and shape-based segmentation for SAT yielded results of CC= 0.974 and VE=-0.01 ± 5.8 cm3, while for VAT the performance indexes were CC= 0.993 and VE= 0.9 ± 1.8 cm3. The results suggest that the shape-based segmentation provides an accurate and simple assessment of the abdominal adiposity with minimal human intervention and it could be used as a simple tool in clinics.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Azpiroz-Leehan J, Cadena M, Ramos-Ibañez N (2013) Comparative statistical analysis between two methods for the measurement of visceral fat in humans. In: 2013 Pan American health care exchanges (PAHCE). IEEE, pp 1–4

  2. Baglioni S, Cantini G, Poli G, Francalanci M, Squecco R, Di Franco A, Borgogni E, Frontera S, Nesi G, Liotta F et al (2012) Functional differences in visceral and subcutaneous fat pads originate from differences in the adipose stem cell. PLoS one 7(5):e36,569

    Article  Google Scholar 

  3. Bonekamp S, Ghosh P, Crawford S, Solga S, Horska A, Brancati F, Diehl A, Smith S, Clark J (2008) Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging. Int J Obes 32(1):100

    Article  Google Scholar 

  4. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  Google Scholar 

  5. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 5:603–619

    Article  Google Scholar 

  6. Fields DA, Goran MI, McCrory MA (2002) Body-composition assessment via air-displacement plethysmography in adults and children: a review. Am J Clin Nutr 75 (3):453–467

    Article  Google Scholar 

  7. Flegal KM, Kit BK, Orpana H, Graubard BI (2013) Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. Jama 309(1):71–82

    Article  Google Scholar 

  8. Florin C, Paragios N, Funka-Lea G, Williams J (2007) Liver segmentation using sparse 3d prior models with optimal data support. In: Biennial international conference on information processing in medical imaging. Springer, pp 38–49

  9. Frederiksen L, Nielsen T, Wraae K, Hagen C, Frystyk J, Flyvbjerg A, Brixen K, Andersen M (2009) Subcutaneous rather than visceral adipose tissue is associated with adiponectin levels and insulin resistance in young men. J Clin Endocrinol Metabol 94(10):4010–4015

    Article  Google Scholar 

  10. Grainger AT, Tustison NJ, Qing K, Roy R, Berr SS, Shi W (2018) Deep learning-based quantification of abdominal fat on magnetic resonance images. PloS one 13(9):e0204,071

    Article  Google Scholar 

  11. Gronemeyer SA, Steen RG, Kauffman WM, Reddick WE, Glass JO (2000) Fast adipose tissue (fat) assessment by mri. Magn Reson Imaging 18(7):815–818

    Article  Google Scholar 

  12. Heckel F, Konrad O, Peitgen HO (2010) Fast and smooth interactive segmentation of medical images using variational interpolation. In: Proceedings of the 2nd Eurographics conference on visual computing for biology and medicine. Eurographics Association, pp 9–16

  13. Herman GT, Zheng J, Bucholtz CA (1992) Shape-based interpolation. IEEE Comput Graph Appl 3: 69–79

    Article  Google Scholar 

  14. Huang LK, Wang MJJ (1995) Image thresholding by minimizing the measures of fuzziness. Pattern Recogn 28(1):41–51

    Article  Google Scholar 

  15. Ibrahim MM (2010) Subcutaneous and visceral adipose tissue: structural and functional differences. Obes Rev 11(1):11–18

    Article  Google Scholar 

  16. Irving BA, Weltman JY, Brock DW, Davis CK, Gaesser GA, Weltman A (2007) Nih imagej and slice-o-matic computed tomography imaging software to quantify soft tissue. Obesity 15(2):370–376

    Article  Google Scholar 

  17. 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: International conference on medical image computing and computer-assisted intervention. Springer, pp 635–642

  18. Klein S (2004) The case of visceral fat: argument for the defense. J Clin Invest 113(11):1530–1532

    Article  Google Scholar 

  19. Kuczmarski RJ (1996) Bioelectrical impedance analysis measurements as part of a national nutrition survey. Am J Clin Nutrit 64(3):453S–458S

    Article  Google Scholar 

  20. 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 31(12):1806

    Article  Google Scholar 

  21. Langner T, Hedström A, Mörwald K, Weghuber D, Forslund A, Bergsten P, Ahlström H, Kullberg J (2019) Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water–fat MRI. Magn Reson Med 81(4):2736–2745

    Article  Google Scholar 

  22. Lankton S, Tannenbaum A (2008) Localizing region-based active contours. IEEE Trans Image Process 17(11):2029–2039

    Article  MathSciNet  Google Scholar 

  23. Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2016

    Article  MathSciNet  Google Scholar 

  24. Machann J, Thamer C, Schnoedt B, Haap M, Haring HU, Claussen CD, Stumvoll M, Fritsche A, Schick F (2005) Standardized assessment of whole body adipose tissue topography by MRI. J Magn Reson Imaging: Official J Int Soc Magn Reson Med 21(4):455–462

    Article  Google Scholar 

  25. Ogden CL, Carroll MD, Kit BK, Flegal KM (2014) Prevalence of childhood and adult obesity in the united states, 2011-2012. Jama 311(8):806–814

    Article  Google Scholar 

  26. Ogden CL, Carroll MD, Lawman HG, Fryar CD, Kruszon-Moran D, Kit BK, Flegal KM (2016) Trends in obesity prevalence among children and adolescents in the united states, 1988-1994 through 2013-2014. Jama 315(21):2292–2299

    Article  Google Scholar 

  27. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  28. Pan Y, Jeong WK, Whitaker R (2011) Markov surfaces: a probabilistic framework for user-assisted three-dimensional image segmentation. Comput Vis Image Underst 115(10):1375–1383

    Article  Google Scholar 

  29. Poon M, Hamarneh G, Abugharbieh R (2008) Efficient interactive 3d livewire segmentation of complex objects with arbitrary topology. Comput Med Imaging Graph 32(8):639–650

    Article  Google Scholar 

  30. Positano V, Gastaldelli A, Sironi A.m., Santarelli M.F., Lombardi M., Landini L. (2004) An accurate and robust method for unsupervised assessment of abdominal fat by MRI. J Magn Reson Imaging: Official J In Soc Magn Reson Med 20 (4):684–689

    Article  Google Scholar 

  31. Positano V, Cusi K, Santarelli MF, Sironi A, Petz R, DeFronzo R, Landini L, Gastaldelli A (2008) Automatic correction of intensity inhomogeneities improves unsupervised assessment of abdominal fat by MRI. J Magn Reson Imaging: Official J Int Soc Magn Reson Med 28(2):403–410

    Article  Google Scholar 

  32. Preis SR, Massaro JM, Robins SJ, Hoffmann U, Vasan RS, Irlbeck T, Meigs JB, Sutherland P, D’Agostino Sr RB, O’donnell CJ et al (2010) Abdominal subcutaneous and visceral adipose tissue and insulin resistance in the framingham heart study. Obesity 18(11):2191–2198

    Article  Google Scholar 

  33. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

  34. Schenk A, Prause G, Peitgen HO (2000) Efficient semiautomatic segmentation of 3d objects in medical images. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 186–195

  35. Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

    Article  Google Scholar 

  36. Sun J, Xu B, Freeland-Graves J (2016) Automated quantification of abdominal adiposity by magnetic resonance imaging. Am J Hum Biol 28(6):757–766

    Article  Google Scholar 

  37. Thomas EL, Saeed N, Hajnal JV, Brynes A, Goldstone AP, Frost G, Bell JD (1998) Magnetic resonance imaging of total body fat. J Appl Physiol 85 (5):1778–1785

    Article  Google Scholar 

  38. Thörmer G, Bertram HH, Garnov N, Peter V, Schütz T, Shang E, Blüher M, Kahn T, Busse H (2013) Software for automated MRI-based quantification of abdominal fat and preliminary evaluation in morbidly obese patients. J Magn Reson Imaging 37(5):1144–1150

    Article  Google Scholar 

  39. Tokunaga K, Matsuzawa Y, Ishikawa K, Tarui S (1983) A novel technique for the determination of body fat by. Int J Obes 7:445

    Google Scholar 

  40. Wang J, Heymsfield SB, Aulet M, Thornton J, Pierson Jr R (1989) Body fat from body density: underwater weighing vs. dual-photon absorptiometry. Am J Physiol-Endocrinol Metabolism 256(6):E829–E834

    Article  Google Scholar 

  41. Wang D, Shi L, Chu WC, Hu M, Tomlinson B, Huang WH, Wang T, Heng PA, Yeung DK, Ahuja AT (2015) Fully automatic and nonparametric quantification of adipose tissue in fat–water separation mr imaging. Med Biol Eng Comput 53(11):1247–1254

    Article  Google Scholar 

  42. Wang Y, Qiu Y, Thai T, Moore K, Liu H, Zheng B (2017) A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on ct images. Comput Methods Programs Biomed 144:97–104

    Article  Google Scholar 

  43. Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, Sugimoto M, Takahashi N, Erickson BJ (2018) Automated abdominal segmentation of ct scans for body composition analysis using deep learning. Radiology 290(3):669–679

    Article  Google Scholar 

  44. Zhou A, Murillo H, Peng Q (2011) Novel segmentation method for abdominal fat quantification by MRI. J Magn Reson Imaging 34(4):852–860

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Martin O. Mendez.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mendez, M.O., Azpiroz-Leehan, J., Sacristan-Rock, E. et al. Assisted quantification of abdominal adipose tissue based on magnetic resonance images. Multimed Tools Appl 79, 1519–1534 (2020). https://doi.org/10.1007/s11042-019-08360-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08360-z

Keywords

Navigation