How to use image analysis for islet counting - Girman, Peter; Berkova, Zuzana; Dobolilova, Eva; Saudek, Frantisek (The review of diabetic studies, 2008)
AIM: Assessment of islet mass before islet transplantation requires a reliable technique to enable exact analysis of islet volume. This study aimed to test the applicability of digital image analysis (DIA) for evaluation of samples of purified and non-purified islets. METHODS: Pancreatic islets were isolated from 10 Lewis rats. Samples of purified (n = 10) and non-purified islets (n = 30) were counted conventionally and by using a computerized method. The equipment for the computerized counting consisted of a digital camera installed on a stereomicroscope and connected to a personal computer. Images of 2272x1704 pixels were processed using a previously described non-commercial program originally developed for this purpose. Islets were converted to equivalents using globe and ellipsoid models. The insulin content of purified islets was assessed using radioimmunoassay and was correlated to the absolute and standardized islet number. RESULTS: Mean absolute numbers of purified islets +/- SD were 908 +/- 130 and 1049 +/- 230 (manually and DIA respectively). Mean insulin content +/- SD obtained from purified islets was 161 +/- 45 mU. The mean equivalents of purified islets (1589 +/- 555 for globe and 1219 +/- 452 for ellipsoid) significantly correlated with insulin content. However, this correlation was not significant when absolute islet numbers were used, counted using either method. There was no significant difference in absolute non-purified islet numbers assessed by manual and computerized methods (average +/- SD in 50 microl samples; 12.6 +/- 4.1 and 13.3 +/- 5.3 respectively; p = 0.22). The manual method showed a significantly higher yield of islet equivalents (IE; p < 0.001 for both globe and ellipsoid). CONCLUSION: The computer-based system for islet counting correlated better to insulin content than conventional islet estimation and prevented overestimation. Reproducibility and ease of assessment make it potentially applicable to clinical islet transplantation.