Adiposoft: automated software for the analysis of white adipose tissue cellularity in histological sections

Abstract

The accurate estimation of the number and size of cells provides relevant information on the kinetics of growth and the physiological status of a given tissue or organ. Here, we present Adiposoft, a fully automated open-source software for the analysis of white adipose tissue cellularity in histological sections. First, we describe the sequence of image analysis routines implemented by the program. Then, we evaluate our software by comparing it with other adipose tissue quantification methods, namely, with the manual analysis of cells in histological sections (used as gold standard) and with the automated analysis of cells in suspension, the most commonly used method. Our results show significant concordance between Adiposoft and the other two methods. We also demonstrate the ability of the proposed method to distinguish the cellular composition of three different rat fat depots. Moreover, we found high correlation and low disagreement between Adiposoft and the manual delineation of cells. We conclude that Adiposoft provides accurate results while considerably reducing the amount of time and effort required for the analysis.

Fig. 1.

Sequence of image analysis operations performed with Adiposoft. A: Initial image. B: 8-bits red channel. C: Binarized version of the red channel. D: Result of the morphological Opening of (C). E: Distance transform of (D). F: Watershed of (D)using as seed the maxima of the distance transform (E). G: AND operation between (C) and (F). H: Deletion of objects touching the edges of the image. I: Deletion of small objects. J: Final labeled image.

Fig. 2.

(Top) Original image cells in suspension. (Bottom) Segmentation result.

Fig. 3.

Plot of Tukey posthoc test results for two-way ANOVA tests for the mean diameter.

Fig. 4.

(Top) Plot showing the agreement between the average diameters calculated using Adiposoft or the manual method. The plot is based on the analysis of 250 images. The line shows the linear regression with the 95% confidence interval. (Bottom) Bland Altman plot of diameter measures.

Fig. 5.

Sample H&E-stained tissue section acquired at low magnification (1.25×). The squares indicate the positions and the size of some random high-resolution (20×) acquisitions.