Automated recognition and quantification of pancreatic islets in Zucker diabetic fatty rats treated with exendin-4

Type 2 diabetes is characterized by impaired insulin secretion from pancreatic beta-cells. Quantification of the islet area in addition to the insulin-positive area is important for detailed understanding of pancreatic islet histopathology. Here we show computerized automatic recognition of the islets of Langerhans as a novel high-throughput method to quantify islet histopathology. We utilized state-of-the-art tissue pattern recognition software to enable automatic recognition of islets, eliminating the need to laboriously trace islet borders by hand. After training by a histologist, the software successfully recognized even irregularly shaped islets with depleted insulin immunostaining, which were quite difficult to automatically recognize. The results from automated image analysis were highly correlated with those from manual image analysis. To establish whether this automated, rapid, and objective determination of islet area will facilitate studies of islet histopathology, we showed the beneficial effect of chronic exendin-4, a glucagon-like peptide-1 analog, treatment on islet histopathology in Zucker diabetic fatty (ZDF) rats. Automated image analysis provided qualitative and quantitative evidence that exendin-4 treatment ameliorated the loss of pancreatic insulin content and gave rise to islet hypertrophy. We also showed that glucagon-positive alpha-cell area was decreased significantly in ZDF rat islets with disorganized structure. This study is the first to demonstrate the utility of automatic quantification of digital images to study pancreatic islet histopathology. The proposed method will facilitate evaluations in preclinical drug efficacy studies as well as elucidation of the pathophysiology of diabetes.