Article | Published:

Body composition, energy expenditure and physical activity

Reliability and validity of the new VikingSlice software for computed tomography body composition analysis

European Journal of Clinical Nutritionvolume 73pages5461 (2019) | Download Citation



Body composition assessment by computed tomography (CT) is increasingly used for diagnostic and prognostic purposes in various patient groups. This study aimed to compare the reliability and validity of a newly in-house developed segmentation software VikingSlice against a commercial software (SliceOMatic) for quantification of adipose tissue and skeletal muscle cross-sectional areas (CSA).


Fifty abdominal CT sets from chronic pancreatitis patients were analyzed (mean age 49, range 27–84 years; 38 males). Soft tissue CSAs at level of 4th lumbar vertebra were assessed by measuring standard Hounsfield unit threshold definitions with both softwares. Analysis with VikingSlice included automatic segmentation of interested region with subsequent manual corrections. Analysis with SliceOMatic included manual segmentation of each area. Same investigator measured CSAs using both programs. Inter-observer reliability of CSAs measurements with VikingSlice were assessed by comparing results from two independent investigators. Measurements were compared using the intra-class correlation coefficient (ICC), coefficient of variation (CV), Jaccard index and Bland–Altman analyses.


The inter-observer reliability of VikingSlice was excellent (CV 3.4–15.4%, ICC 0.979–0.999, Jaccard index 0.68–0.98). Validity was high (CV 1.6–10.2%, ICC 0.950–0.997) for measurements by SliceOmatic and VikingSlice. The findings were supported in the Bland–Altman plots. The reliability study had small average differences with means of soft tissue compartments in range −2.29 cm2 to 1.56 cm2; average differences between both softwares were −1.28 cm2 to 0.31 cm2.


The in-house developed software VikingSlice was fast and showed good reliability that is comparable with commercial software in its utility to estimate adipose tissue and skeletal muscle CSAs.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Koster A, Ding J, Stenholm S, Caserotti P, Houston DK, Nicklas BJ, et al. Does the amount of fat mass predict age-related loss of lean mass, muscle strength, and muscle quality in older adults? J Gerontol–Ser A Biol Sci Med Sci. 2011;66:888–95.

  2. 2.

    Addison O, LaStayo PC, Dibble LE, Marcus RL. Inflammation, aging, and adiposity: implications for physical therapists. J Geriatr Phys Ther. 2012;35:86–94.

  3. 3.

    Prior SJ, Joseph LJ, Brandauer J, Katzel LI, Hagberg JM, Ryan AS. Reduction in midthigh low-density muscle with aerobic exercise training and weight loss impacts glucose tolerance in older men. J Clin Endocrinol Metab. 2007;92:880–6.

  4. 4.

    Dubé M-C, Lemieux S, Piché M-E, Corneau L, Bergeron J, Riou M-E, et al. The contribution of visceral adiposity and mid-thigh fat-rich muscle to the metabolic profile in postmenopausal women. Obesity. 2011;19:953–9.

  5. 5.

    Durheim M, Slentz C. Relationships between exercise-induced reductions in thigh intermuscular adipose tissue, changes in lipoprotein particle size, and visceral adiposity. Am J Physiol. 2008;295:407–12.

  6. 6.

    Tuttle LJ, Sinacore DR, Cade WT, Mueller MJ. Lower physical activity is associated with higher intermuscular adipose tissue in people with type 2 diabetes and peripheral neuropathy. Phys Ther. 2011;91:923–30.

  7. 7.

    Tuttle LJ, Sinacore DR, Mueller MJ. Intermuscular adipose tissue is muscle specific and associated with poor functional performance. J Aging Res. 2012;1–7.

  8. 8.

    Martin L, Birdsell L, MacDonald N, Reiman T, Clandinin MT, McCargar LJ, et al. Cancer cachexia in the age of obesity: Skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol. 2013;31:1539–47.

  9. 9.

    Miller BS, Ignatoski KM, Daignault S, Lindland C, Doherty M, Gauger PG, et al. Worsening central sarcopenia and increasing intra-abdominal fat correlate with decreased survival in patients with adrenocortical carcinoma. World J Surg. 2012;36:1509–16.

  10. 10.

    Yip C, Dinkel C, Mahajan A, Siddique M, Cook GJR, Goh V. Imaging body composition in cancer patients: visceral obesity, sarcopenia and sarcopenic obesity may impact on clinical outcome. Insights Imaging. 2015;6:489–97.

  11. 11.

    Gibson DJ, Burden ST, Strauss BJ, Todd C, Lal S. The role of computed tomography in evaluating body composition and the influence of reduced muscle mass on clinical outcome in abdominal malignancy: a systematic review. Eur J Clin Nutr. 2015;69:1–8.

  12. 12.

    Mourtzakis M, Prado CMM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE. 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. 2008;33:997–1006.

  13. 13.

    Ross R, Leger L, Guardo R, De Guise J, Pike BG. Adipose tissue volume measured by magnetic resonance imaging and computerized tomography in rats. J Appl Physiol. 1991;70:2164–72.

  14. 14.

    Ross R, Léger L, Morris D, de Guise J, Guardo R. Quantification of adipose tissue by MRI: relationship with anthropometric variables. J Appl Physiol. 1992;72:787–95.

  15. 15.

    Park YW, Allison DB, Heymsfield SB, Gallagher D. Larger amounts of visceral adipose tissue in Asian Americans. Obes Res. 2001;9:381–7.

  16. 16.

    Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in lder persons is associated with functional impairment and physical disability. J Am Geriatr Soc. 2002;50:889.

  17. 17.

    Slentz CA, Aiken LB, Houmard JA, Bales CW, Johnson JL, Tanner CJ, et al. Role of exercise in reducing the risk of diabetes and obesity inactivity, exercise, and visceral fat. STRRIDE: a randomized, controlled study of exercise intensity and amount. J Appl Physiol. 2005;99:1613–8.

  18. 18.

    Shen W, Punyanitya M, Chen J, Gallagher D, Albu J, Pi-Sunyer X, et al. Visceral adipose tissue: relationships between single slice areas at different locations and obesity-related health risks. Int J Obes. 2007;31:763–9.

  19. 19.

    Nazare JA, Smith JD, Borel AL, Haffner SM, Balkau B, Ross R, et al. Ethnic influences on the relations between abdominal sub- cutaneous and visceral adiposity, liver fat, and cardiometabolic risk profile: the International Study of Prediction of Intra-Abdominal Adiposity and Its Relationship With Cardiometabolic Risk/Int. Am J Clin Nutr. 2012;96:714–26.

  20. 20.

    Davidson LE, Kelley DE, Heshka S, Thornton J, Pi-Sunyer FX, Boxt L, et al. Skeletal muscle and organ masses differ in overweight adults with type 2 diabetes. J Appl Physiol. 2014.

  21. 21.

    Demerath EW, Ritter KJ, Couch WA, Rogers NL, Moreno GM, Choh A, et al. Validity of a new automated software program for visceral adipose tissue estimation. Int J Obes. 2007;31:285–91.

  22. 22.

    Potretzke AM, Schmitz KH, Jensen MD. Preventing overestimation of pixels in computed tomography assessment of visceral fat. Obes Res. 2004;12:1698–701.

  23. 23.

    Kim J, Wang Z, Heymsfield SB, Baumgartner RN, Gallagher D. Total-body skeletal muscle mass: estimation by a new dual-energy. Am J Clin Nutr. 2002;76:378–83.

  24. 24.

    Takahashi N, Sugimoto M, Psutka SP, Chen B, Moynagh MR, Carter RE. Validation study of a new semi-automated software program for CT body composition analysis. Abdom Radiol. 2017.

  25. 25.

    Poulsen JL, Nilsson M, Brock C, Sandberg TH, Krogh K, Drewes AM. The impact of opioid treatment on regional gastrointestinal transit. J Neurogastroenterol Motil. 2016;22:282–91.

  26. 26.

    Madzak A, Engjom T, Wathle GK, Olesen SS, Tjora E, Lærum BN. et al. Secretin-stimulated MRI assessment of exocrine pancreatic function in cystic fibrosis and healthy volunteers. Pancreatology. 2016;16:S111.

  27. 27.

    Madzak A, Olesen SS, Haldorsen IS, Drewes AM, Frøkjær JB. Secretin-stimulated MRI characterization of pancreatic morphology and function in patients with chronic pancreatitis. Pancreatology. 2017;17:228–36.

  28. 28.

    Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86:420–8.

  29. 29.

    Bland MJ, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–10.

  30. 30.

    Irving Ba, Weltman JY, Brock DW, Davis CK, Gaesser Ga, Weltman A. NIH ImageJ and Slice-O-Matic computed tomography imaging software to quantify soft tissue. Obesity. 2007;15:370–6.

  31. 31.

    Richards CH, Roxburgh CSD, MacMillan MT, Isswiasi S, Robertson EG, Guthrie GK, et al. The relationships between body composition and the systemic inflammatory response in patients with primary operable colorectal cancer. PLoS ONE. 2012;7(8):e41883.

  32. 32.

    van Vugt JLA, Levolger S, Gharbharan A, Koek M, Niessen WJ, Burger JWA, et al. A comparative study of software programmes for cross-sectional skeletal muscle and adipose tissue measurements on abdominal computed tomography scans of rectal cancer patients. J Cachexia Sarcopenia Muscle. 2016.

  33. 33.

    Addison O, Marcus RL, Lastayo PC, Ryan AS. Intermuscular fat: A review of the consequences and causes. Int J Endocrinol. 2014;2014:34–6.

  34. 34.

    Hausman GJ, Basu U, Du M, Fernyhough-Culver M, Dodson MV. Intermuscular and intramuscular adipose tissues: Bad vs. good adipose tissues. Adipocyte. 2014;3:242–55.

  35. 35.

    Decazes P, Rouguette A, Chetrit A, Vera P, Gardin I. Automatic measurement of the total visceral adipose tissue from computed tomography images by using a multi-atlas segmentation method. J Comput Assist Tomogr. 2018;42(1):139–145.

  36. 36.

    Zhao B, Colville J, Curran S, Jiang L, Kijewski P, Schwartz L. Automated quantification of body fat distribution on volumetric computed tomography. J Comput Assist Tomogr. 2006;30:777–83.

  37. 37.

    Kullberg J, Hedström A, Brandberg J, Strand R, Johansson L. Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci Rep. 2017;7(1):10425.

  38. 38.

    Kamiya N, Zhou X, Chen H, Hara T, Hoshi H, Yokoyama R, et al. Automated recognition of the psoas major muscles on X-ray CT images. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:3557–60.

  39. 39.

    Popuri K, Cobzas D, Esfandiari N, Baracos V, Jägersand M. Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging. 2016;35:512–20.

Download references

Author information


  1. Gastroenterology, Hepatology and Nutrition Centre, Pauls Stradins Clinical University Hospital, Riga, Latvia

    • Imanta Ozola-Zālīte
    •  & Aldis Pukitis
  2. Mech-Sense & Centre for Pancreatic Diseases, Department of Gastroenterology and Hepatology, Clinical Institute, Aalborg University Hospital, Aalborg, Denmark

    • Esben Bolvig Mark
    • , Søren Schou Olesen
    •  & Asbjørn Mohr Drewes
  3. Department of Radiology, Clinical Institute, Aalborg University Hospital, Aalborg, Denmark

    • Esben Bolvig Mark
    • , Tomas Gudauskas
    •  & Jens Brøndum Frokjær
  4. Department of Personalized Oncology, Clinical Hospital N1 “MEDSI”, Moscow, Russia

    • Vladimir Lyadov


  1. Search for Imanta Ozola-Zālīte in:

  2. Search for Esben Bolvig Mark in:

  3. Search for Tomas Gudauskas in:

  4. Search for Vladimir Lyadov in:

  5. Search for Søren Schou Olesen in:

  6. Search for Asbjørn Mohr Drewes in:

  7. Search for Aldis Pukitis in:

  8. Search for Jens Brøndum Frokjær in:

Conflict of interest

The authors declare that they have no conflict of interest.

Corresponding author

Correspondence to Aldis Pukitis.

Electronic supplementary material

About this article

Publication history






Further reading