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A novel automated image analysis method for accurate adipocyte quantification.

Osman OS, Selway JL, Kępczyńska MA, Stocker CJ, O'Dowd JF, Cawthorne MA, Arch JR, Jassim S, Langlands K - Adipocyte (2013)

Bottom Line: Photomicrographs of H&E-stained paraffin sections of murine gonadal adipose were transformed using standard image processing/analysis algorithms to reduce background and enhance edge-detection.This allowed the isolation of individual adipocytes from which their area could be calculated.Performance was compared with manual measurements made from the same images, in which adipocyte area was calculated from estimates of the major and minor axes of individual adipocytes.

View Article: PubMed Central - PubMed

Affiliation: The Clore Laboratory; University of Buckingham; Buckingham, UK ; Department of Applied Computing; University of Buckingham; Buckingham, UK.

ABSTRACT
Increased adipocyte size and number are associated with many of the adverse effects observed in metabolic disease states. While methods to quantify such changes in the adipocyte are of scientific and clinical interest, manual methods to determine adipocyte size are both laborious and intractable to large scale investigations. Moreover, existing computational methods are not fully automated. We, therefore, developed a novel automatic method to provide accurate measurements of the cross-sectional area of adipocytes in histological sections, allowing rapid high-throughput quantification of fat cell size and number. Photomicrographs of H&E-stained paraffin sections of murine gonadal adipose were transformed using standard image processing/analysis algorithms to reduce background and enhance edge-detection. This allowed the isolation of individual adipocytes from which their area could be calculated. Performance was compared with manual measurements made from the same images, in which adipocyte area was calculated from estimates of the major and minor axes of individual adipocytes. Both methods identified an increase in mean adipocyte size in a murine model of obesity, with good concordance, although the calculation used to identify cell area from manual measurements was found to consistently over-estimate cell size. Here we report an accurate method to determine adipocyte area in histological sections that provides a considerable time saving over manual methods.

No MeSH data available.


Related in: MedlinePlus

Figure 3. Comparison of mean adipocyte size derived from manual and automatic methods. Mean adipocyte size ± SD was calculated using at least 16 fields per whole-slide image, from at least 4 animals per group, chow (CH) of high-fat (HF) fed, using both manual ellipse measurements (A), and automatic pixel counting (B).
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Figure 3: Figure 3. Comparison of mean adipocyte size derived from manual and automatic methods. Mean adipocyte size ± SD was calculated using at least 16 fields per whole-slide image, from at least 4 animals per group, chow (CH) of high-fat (HF) fed, using both manual ellipse measurements (A), and automatic pixel counting (B).

Mentions: Both methods identified a significant (P < 0.01) increase in mean adipocyte size in tissue isolated from offspring of high-fat fed dams as compared with the offspring of chow-fed controls (Fig. 3). However, we found that the automated method consistently returned smaller values for cell size, the difference being exacerbated in larger cells. Specifically, the manual method returned an average fat cell size of 1004 µm2 in lean animals and 1733 µm2 in the high-fat group, compared with 901 µm2 and 1350 µm2 respectively by automated counting. This may have been as a consequence of over-estimates of area achieved with manual methods that rely on the calculation of an ellipse to approximate cell area. Specifically, ellipses are calculated from the longest axis values (which are themselves based in human judgment), as demonstrated in Figure 1C, and thus the area value obtained will consistently be a marginal over-estimate for a cell of irregular shape. In turn, this may explain the slight decrease in significance observed when diet-associated changes in fat size were assessed by the automated method compared with the manual approach. Similar changes in size distribution were observed in the offspring of high-fat fed groups with both methods (Fig. 4A and B). When the graphs of the two methods are superimposed, a slight leftward shift in the automated value is seen (Fig. 4C and D), consistent with more accurate size measurement as described above. The only point where a significant difference (P < 0.0001) was observed between automated and manual quantification (using a two-way ANOVA with a Bonferroni post-hoc test) was in the smallest size category (<500 µm2) in the offspring of the high-fat fed animals (Fig. 4D).


A novel automated image analysis method for accurate adipocyte quantification.

Osman OS, Selway JL, Kępczyńska MA, Stocker CJ, O'Dowd JF, Cawthorne MA, Arch JR, Jassim S, Langlands K - Adipocyte (2013)

Figure 3. Comparison of mean adipocyte size derived from manual and automatic methods. Mean adipocyte size ± SD was calculated using at least 16 fields per whole-slide image, from at least 4 animals per group, chow (CH) of high-fat (HF) fed, using both manual ellipse measurements (A), and automatic pixel counting (B).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3756104&req=5

Figure 3: Figure 3. Comparison of mean adipocyte size derived from manual and automatic methods. Mean adipocyte size ± SD was calculated using at least 16 fields per whole-slide image, from at least 4 animals per group, chow (CH) of high-fat (HF) fed, using both manual ellipse measurements (A), and automatic pixel counting (B).
Mentions: Both methods identified a significant (P < 0.01) increase in mean adipocyte size in tissue isolated from offspring of high-fat fed dams as compared with the offspring of chow-fed controls (Fig. 3). However, we found that the automated method consistently returned smaller values for cell size, the difference being exacerbated in larger cells. Specifically, the manual method returned an average fat cell size of 1004 µm2 in lean animals and 1733 µm2 in the high-fat group, compared with 901 µm2 and 1350 µm2 respectively by automated counting. This may have been as a consequence of over-estimates of area achieved with manual methods that rely on the calculation of an ellipse to approximate cell area. Specifically, ellipses are calculated from the longest axis values (which are themselves based in human judgment), as demonstrated in Figure 1C, and thus the area value obtained will consistently be a marginal over-estimate for a cell of irregular shape. In turn, this may explain the slight decrease in significance observed when diet-associated changes in fat size were assessed by the automated method compared with the manual approach. Similar changes in size distribution were observed in the offspring of high-fat fed groups with both methods (Fig. 4A and B). When the graphs of the two methods are superimposed, a slight leftward shift in the automated value is seen (Fig. 4C and D), consistent with more accurate size measurement as described above. The only point where a significant difference (P < 0.0001) was observed between automated and manual quantification (using a two-way ANOVA with a Bonferroni post-hoc test) was in the smallest size category (<500 µm2) in the offspring of the high-fat fed animals (Fig. 4D).

Bottom Line: Photomicrographs of H&E-stained paraffin sections of murine gonadal adipose were transformed using standard image processing/analysis algorithms to reduce background and enhance edge-detection.This allowed the isolation of individual adipocytes from which their area could be calculated.Performance was compared with manual measurements made from the same images, in which adipocyte area was calculated from estimates of the major and minor axes of individual adipocytes.

View Article: PubMed Central - PubMed

Affiliation: The Clore Laboratory; University of Buckingham; Buckingham, UK ; Department of Applied Computing; University of Buckingham; Buckingham, UK.

ABSTRACT
Increased adipocyte size and number are associated with many of the adverse effects observed in metabolic disease states. While methods to quantify such changes in the adipocyte are of scientific and clinical interest, manual methods to determine adipocyte size are both laborious and intractable to large scale investigations. Moreover, existing computational methods are not fully automated. We, therefore, developed a novel automatic method to provide accurate measurements of the cross-sectional area of adipocytes in histological sections, allowing rapid high-throughput quantification of fat cell size and number. Photomicrographs of H&E-stained paraffin sections of murine gonadal adipose were transformed using standard image processing/analysis algorithms to reduce background and enhance edge-detection. This allowed the isolation of individual adipocytes from which their area could be calculated. Performance was compared with manual measurements made from the same images, in which adipocyte area was calculated from estimates of the major and minor axes of individual adipocytes. Both methods identified an increase in mean adipocyte size in a murine model of obesity, with good concordance, although the calculation used to identify cell area from manual measurements was found to consistently over-estimate cell size. Here we report an accurate method to determine adipocyte area in histological sections that provides a considerable time saving over manual methods.

No MeSH data available.


Related in: MedlinePlus