157 Chemical profiling of testicular parenchyma in rams using an exploration of echointensity bands and a novel computer algorithm (r-Algo) increasing precision and accuracy of echotextural analyses: a sequel

The biochemical composition of testicular tissue carries a detailed “molecular history” of the conditions or events that produced it, including steroidogenic function as well as cellular and fluid residence times (e.g. sperm production and storage/transport rates). The aim of this study was to employ ultrasound image segmentation to increase the consistency and accuracy of determining chemical composition of rams’ testicular parenchyma from its first order echotextural characteristics (i.e. mean numerical pixel intensity [MPI], standard deviation of numerical pixel values or mean pixel heterogeneity [MPH], and pixel frequency distribution or mean pixel concentration [MPC]). Ten testes obtained after slaughter from different sexually mature Karakul rams were scanned ex situ with an 8-MHz linear-array transducer, in the longitudinal and transverse planes. The Kjeldahl method was used to determine the amount of crude protein; an oven-drying method, to determine the moisture content; and a Soxhlet extraction of dried samples, to determine the fat content of testicular tissue samples. Digital ultrasonograms of testicular parenchyma were normalized and converted to bitmap files using ImageProPlus® analytical software. Two different approaches, namely echo intensity (EI) banding and algorithmic segmentation of resultant bitmaps using proprietary software r-Algo, were compared for their usefulness in detecting quantitative correlations between first-order echotextural characteristics and proximate chemical composition of testicular tissue. Correlational analyses between echotextural characteristics within EI bands and chemical constituents were completed using Pearson Product Moment tests (SigmaPlot11.0; Systat Software Inc.). All bitmap files were uploaded to a Python algorithm r-Algo to calculate mean MPI, MPH, and MPC for each possible combination of individual pixel intensity values. The regression equations for identified pixel intensity ranges were then used to compute the estimated values of chemical constituents from MPI, MPH, and MPC. Using 25- or 50-pixel intensity bands for testicular ultrasonograms, we only identified two EI bands for images obtained in a longitudinal plane and five EI bands for images in the transverse plane that had mean MPI, MPH, or MPC significantly correlated with testicular tissue constituents. The mean accuracy of predicting testicular composition using r-Algo–identified pixel intensity ranged from 72.51 ± 5.13% (MPH-fat) to 99.69 ± 0.12% (MPC-moisture) for images in the longitudinal plane, and from 78.31 ± 5.24% (MPI-fat) to 99.69 ± 0.06 (MPC-moisture) for testicular images in the transverse plane. Algorithmic detection of specific pixel intensity ranges is an optimal method of pixel segmentation for determining correlations between first-order echotextural characteristics and chemical composition of the testes. Our present results highlight the importance of incorporating this method of computer-assisted image analysis for ultrasonographic monitoring of changes in the chemical composition of reproductive tissues.