Limits...
Computer tomographic investigation of subcutaneous adipose tissue as an indicator of body composition.

McEvoy FJ, Madsen MT, Nielsen MB, Svalastoga EL - Acta Vet. Scand. (2009)

Bottom Line: In some circumstances, computer assisted analysis of the resulting image data can identify and measure anatomical features.Image analysis correctly identified the limits of the relevant tissues and automated measurements were successfully generated.The approach to image analysis reported permits the creation of various maps showing adipose thickness or correlation of thickness with other variables by location on the surface of the pig.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Small Animal Clinical Sciences, Faculty of Life Sciences, University of Copenhagen, DK-1870 Frederiksberg C, Denmark. fme@life.ku.dk

ABSTRACT

Background: Modern computer tomography (CT) equipment can be used to acquire whole-body data from large animals such as pigs in minutes or less. In some circumstances, computer assisted analysis of the resulting image data can identify and measure anatomical features. The thickness of subcutaneous adipose tissue at a specific site measured by ultrasound, is used in the pig industry to assess adiposity and inform management decisions that have an impact on reproduction, food conversion performance and sow longevity. The measurement site, called "P2", is used throughout the industry. We propose that CT can be used to measure subcutaneous adipose tissue thickness and identify novel measurement sites that can be used as predictors of general adiposity.

Methods: Growing pigs (N = 12), were each CT scanned on three occasions. From these data the total volume of adipose tissue was determined and expressed as a proportion of total volume (fat-index). A computer algorithm was used to determined 10,201 subcutaneous adipose thickness measurements in each pig for each scan. From these data, sites were selected where correlation with fat-index was optimal.

Results: Image analysis correctly identified the limits of the relevant tissues and automated measurements were successfully generated. Two sites on the animal were identified where there was optimal correlation with fat-index. The first of these was located 4 intercostal spaces cranial to the caudal extremity of the last rib, the other, a further 5 intercostal spaces cranially.

Conclusion: The approach to image analysis reported permits the creation of various maps showing adipose thickness or correlation of thickness with other variables by location on the surface of the pig. The method identified novel adipose thickness measurement positions that are superior (as predictors of adiposity) to the site which is in current use. A similar approach could be used in other situations to quantify potential links between subcutaneous adiposity and disease or production traits.

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Computer tomography image. Transverse CT image showing the automatically determined boundaries. The outer limits of the skin are marked with white dots; the inner margin of subcutaneous adipose tissue is marked with black dots. Note in this particular image the algorithm misrepresents the desired boundaries in places because it was interrupted by discontinuities within the adipose tissue.
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Figure 1: Computer tomography image. Transverse CT image showing the automatically determined boundaries. The outer limits of the skin are marked with white dots; the inner margin of subcutaneous adipose tissue is marked with black dots. Note in this particular image the algorithm misrepresents the desired boundaries in places because it was interrupted by discontinuities within the adipose tissue.

Mentions: All pigs increased in body-mass between scans. The mean body-mass and its standard deviation (in parenthesis) were 94.9 (6.5) and 111.6 (13.1) Kg at the second and third scan sessions respectively. An image showing the points identified for measurement is shown in Figure 1. It can be seen that the algorithm accurately identifies the outer aspect of the skin and at most sites the innermost surface of the subcutaneous adipose tissue. The individual measurements thus made, correspond to skin thickness plus subcutaneous adipose tissue thickness. In places (for example ventrolaterally in Figure 1) the algorithm misrepresented the full thickness of the layer due to the presence of soft tissues (segments of the Cutaneus trunci muscle) within (as opposed to being deep to) the subcutaneous adipose tissue. On visual inspection, these images were identified, deleted and replaced by neighboring slices. These misrepresentations were thus not considered to have any significance on the overall results.


Computer tomographic investigation of subcutaneous adipose tissue as an indicator of body composition.

McEvoy FJ, Madsen MT, Nielsen MB, Svalastoga EL - Acta Vet. Scand. (2009)

Computer tomography image. Transverse CT image showing the automatically determined boundaries. The outer limits of the skin are marked with white dots; the inner margin of subcutaneous adipose tissue is marked with black dots. Note in this particular image the algorithm misrepresents the desired boundaries in places because it was interrupted by discontinuities within the adipose tissue.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2710334&req=5

Figure 1: Computer tomography image. Transverse CT image showing the automatically determined boundaries. The outer limits of the skin are marked with white dots; the inner margin of subcutaneous adipose tissue is marked with black dots. Note in this particular image the algorithm misrepresents the desired boundaries in places because it was interrupted by discontinuities within the adipose tissue.
Mentions: All pigs increased in body-mass between scans. The mean body-mass and its standard deviation (in parenthesis) were 94.9 (6.5) and 111.6 (13.1) Kg at the second and third scan sessions respectively. An image showing the points identified for measurement is shown in Figure 1. It can be seen that the algorithm accurately identifies the outer aspect of the skin and at most sites the innermost surface of the subcutaneous adipose tissue. The individual measurements thus made, correspond to skin thickness plus subcutaneous adipose tissue thickness. In places (for example ventrolaterally in Figure 1) the algorithm misrepresented the full thickness of the layer due to the presence of soft tissues (segments of the Cutaneus trunci muscle) within (as opposed to being deep to) the subcutaneous adipose tissue. On visual inspection, these images were identified, deleted and replaced by neighboring slices. These misrepresentations were thus not considered to have any significance on the overall results.

Bottom Line: In some circumstances, computer assisted analysis of the resulting image data can identify and measure anatomical features.Image analysis correctly identified the limits of the relevant tissues and automated measurements were successfully generated.The approach to image analysis reported permits the creation of various maps showing adipose thickness or correlation of thickness with other variables by location on the surface of the pig.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Small Animal Clinical Sciences, Faculty of Life Sciences, University of Copenhagen, DK-1870 Frederiksberg C, Denmark. fme@life.ku.dk

ABSTRACT

Background: Modern computer tomography (CT) equipment can be used to acquire whole-body data from large animals such as pigs in minutes or less. In some circumstances, computer assisted analysis of the resulting image data can identify and measure anatomical features. The thickness of subcutaneous adipose tissue at a specific site measured by ultrasound, is used in the pig industry to assess adiposity and inform management decisions that have an impact on reproduction, food conversion performance and sow longevity. The measurement site, called "P2", is used throughout the industry. We propose that CT can be used to measure subcutaneous adipose tissue thickness and identify novel measurement sites that can be used as predictors of general adiposity.

Methods: Growing pigs (N = 12), were each CT scanned on three occasions. From these data the total volume of adipose tissue was determined and expressed as a proportion of total volume (fat-index). A computer algorithm was used to determined 10,201 subcutaneous adipose thickness measurements in each pig for each scan. From these data, sites were selected where correlation with fat-index was optimal.

Results: Image analysis correctly identified the limits of the relevant tissues and automated measurements were successfully generated. Two sites on the animal were identified where there was optimal correlation with fat-index. The first of these was located 4 intercostal spaces cranial to the caudal extremity of the last rib, the other, a further 5 intercostal spaces cranially.

Conclusion: The approach to image analysis reported permits the creation of various maps showing adipose thickness or correlation of thickness with other variables by location on the surface of the pig. The method identified novel adipose thickness measurement positions that are superior (as predictors of adiposity) to the site which is in current use. A similar approach could be used in other situations to quantify potential links between subcutaneous adiposity and disease or production traits.

Show MeSH
Related in: MedlinePlus