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In vivo measurements of muscle volume by automatic image analysis of spiral computed tomography scans

Published online by Cambridge University Press:  09 March 2007

E. A. Navajas ,
C. A. Glasbey ,
K. A. McLean ,
A. V. Fisher ,
A. J. L. Charteris ,
N. R. Lambe ,
L. Bünger  and
G. Simm
E. A. Navajas*
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King's Buildings, Edinburgh EH9 3JG, UK
C. A. Glasbey
Affiliation:
Biomathematics and Statistics Scotland, King's Buildings, Edinburgh EH9 3JZ, UK
K. A. McLean
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King's Buildings, Edinburgh EH9 3JG, UK
A. V. Fisher
Affiliation:
University of Bristol, Division of Farm Animal Science, Langford, Bristol BS40 5DU, UK
A. J. L. Charteris
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King's Buildings, Edinburgh EH9 3JG, UK
N. R. Lambe
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King's Buildings, Edinburgh EH9 3JG, UK
L. Bünger
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King's Buildings, Edinburgh EH9 3JG, UK
G. Simm
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King's Buildings, Edinburgh EH9 3JG, UK
*
†E-mail: [email protected]
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Abstract

This study investigates the accuracy of an automatic image analysis method that was developed for spiral computed tomography scans (SCTS), with the objective of calculating the volume of muscle in the hind leg (HLMVCT) and lumbar region (LRMVCT) in lambs. The first step in the image analysis method was the isolation (segmentation) of the muscle regions in each image of the SCTS, using a new program that was implemented in the Sheep Tomogram Analysis Routines software (STAR). Due to the differences of muscle shape in the regions investigated, the new segmentation program applies different segmentation paths in specific subregions. These were automatically identified by the program based on skeletal landmarks. After the segmentation was completed, the muscles areas were automatically measured by counting the pixels representing muscle in each image; the volumes were calculated by adding the muscle areas of each image multiplied by the depth of the image (inter-slice distance). The accuracy of these measures of muscle volume was evaluated, using regression analysis, by comparing HLMVCT and LRMVCT to the hind leg and lumbar region muscle weights measured after dissection (HLMWD, no. =240, and LRMWD, no. =50, respectively) of Texel (TEX) and Scottish Blackface (SBF) female and male lambs slaughtered in 2003-04. The effects of breed, sex and year on the association (SCTS v. dissection) were evaluated. There was a strong association between HLMVCT and HLMWD ( R2=97·4%), which only increased slightly ( R2=97·7%) when breed was included in the model. This indicates that HLMWD can be estimated directly from HLMVCT with a high degree of accuracy. For the lumbar region, the association was high ( R2=83·0% to 88·8% depending on the model) but lower than in the hind leg, probably because the automatic segmentation isolates only the areas of the longissimus lumborum and multifidi muscles. Breed had a significant effect on the prediction of LRMWD from LRMVCT, as well as sex in the case of the TEX lambs. The results indicated that the predictions of LRMWD from LRMVCT require different equations for very divergent breeds such as TEX and SBF.

Keywords

computed tomographyspiral scanmusclemuscle volumesheep
Type
Research Article
Information
Animal Science , Volume 82 , Issue 4 , August 2006 , pp. 545 - 553
Copyright
Copyright © British Society of Animal Science 2006

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References

Brenoe, U. T. and Kolstad, K. 2000. Body composition and development measured repeatedly by computer tomography during growth in two types of turkeys. Poultry Science 79: 546–552.Google Scholar
Falconer, D. S. and Mackay, T. F. C. 1996. Introduction to quantitative genetics. Longman, Harlow.Google Scholar
Genstat Committee. 2004. Genstat 7 release 7.2 (PC/Windows NT). Lawes Agricultural Trust, Rothamsted Experimental Station, Harpenden.Google Scholar
Glasbey, C. A. and Horgan, G. W. 1995. Image analysis for the biological sciences. Wiley, Chichester.Google Scholar
Glasbey, C. A. and Robinson, C. D. 2002. Estimators of tissue proportions from X-ray CT images. Biometrics 58: 928–936.Google Scholar
Glasbey, C. A. and Young, M. J. 2002. Maximum a posteriori estimation of image boundaries by dynamic programming. Applied Statistics 51: 209–221.Google Scholar
Glasbey, C. A., Navajas, E., McLean, K. A., Fisher, A. V., Lambe, N. R., Bünger, L. and Simm, G. 2004. Estimation of muscle volume by automated image analysis of spiral computed tomography scans in sheep. Proceedings of the British Society of Animal Science, 2004, 37.Google Scholar
Imaginis. 2005. Computed tomography imaging (CT scan, CAT scan): spiral CT and helical CT . http://imaginis.com//ct-scan/spiral.asp.Google Scholar
Kempster, A. J., Cuthbertson, A. and Harrington, C. 1982. Carcass evaluation in livestock breeding, production and marketing. Granada Publishing Ltd, London.Google Scholar
Kvame, T., McEwan, J. C., Amer, P. R. and Jopson, N. B. 2004. Economic benefits in selection for weight and composition of lambs cuts predicted by computer tomography. Livestock Production Science 90: 123–133.Google Scholar
Kvame, T. and Vangen, O. 2005. In-vivo composition of carcass regions in lambs of two genetic line, and selection of CT positions for estimation of each region Small Ruminant Research.CrossRefGoogle Scholar
Mann, A. D., Young, M. J., Glasbey, C. A. and McLean, K. A. 2005. STAR: Sheep Tomogram Analysis Routines (version 3.8). BioSS software documentation, University of Edinburgh.Google Scholar
Navajas, E., Glasbey, C. A., McLean, K. A., Bünger, L. and Simm, G. 2004. Comparison of manual and automatic segmentation of muscle regions in spiral computed tomography images of sheep. Proceedings of the British Society of Animal Science, 2004, p.35.Google Scholar
Nicoll, G. B., Jopson, N. B. and McEwan, J. C. 2002. Contribution of CT scanning to genetic improvement in a terminal sire sheep breeding programme. In Proceedings of the seventh world congress on genetics applied to livestock production, CD-ROM communication, no.11–30.Google Scholar
Nord, R. H. and Payne, R. K. 1995. A new equation set for converting body density to percent body fat. Asia Pacific Journal of Clinical Nutrition 4: 177–179.Google Scholar
Payne, R. C., Hutchinson, J. R., Robilliard, J. J., Smith, N. C. and Wilson, A. M. (2005) Functional specialisation of pelvic limb anatomy in horses (Equus caballus). Journal of Anatomy 206: 557–574.Google Scholar
Roberts, N., Cruz-Orive, L. M., Reid, N. M. K., Brodie, D. A., Bourne, M. and Edwards, R. H. T. 1993. Unbiased estimation of human body composition by the Cavalieri method using magnetic resonance imaging. Journal of Microscopy 171: 239–253.Google Scholar
Simm, G., Lewis, R. M., Collins, J. E. and Nieuwhof, G. J. 2001. Use of sire referencing schemes to select for improved carcass composition in sheep. Journal of Animal Science 79: E225–E259.Google Scholar
Szabo, C. S., Babinszky, L., Verstegen, M. W. A., Vangen, O., Jansman, A. J. M. and Kanis, E. 1999. The application of digital imaging techniques in the in vivo estimation of the body composition of pigs: a review. Livestock Production Science 60: 1–11.Google Scholar
Young, M.J., Lewis, R.M., Simm, G., Glasbey, C.A. and McLean, K.A. 2001a. Incorporating X-ray computer tomography into selection programmes to improve the quality of sheep meat. Scottish Agricultural College. Final report for the Department of Environment, Food and Rural Affairs (DEFRA).Google Scholar
Young, M. J., Nsoso, S. J., Logan, C. M. and Beatson, P. R. 1996. Prediction of carcass tissue weight in vivo using live weight, ultrasound or X-ray CT measurements. Proceedings of the New Zealand Society of Animal Production 56: 205–211.Google Scholar
Young, M. J., Simm, G. and Glasbey, C. A. 2001b. Computerised tomography for carcass analysis. Proceedings of the British Society of Animal Science, 2001. pp.250–254.Google Scholar