Automated Quantification of Pancreatic Steatosis in Biopsy Images using a Classification Based System

Non-Alcoholic Fatty Pancreas Disease (NAFPD) is the most common pancreatic condition in adults and is usually associated with obesity and insulin resistance. It is a new medical term that indicates the development of pancreatic steatosis, which at an advanced stage leads to the irreversible replacement of acinar cells with fat droplets. Although increasing prevalence rates are recorded worldwide for this condition, it has been studied to a small extent due to the diagnostic limitations of noninvasive medical imaging methods. In recent years and with the development of modern computer vision systems, digital pathology through biopsy imaging systems has become the gold standard in modern clinical trials. The current work presents an automated diagnostic tool for measuring the fat ratio in pancreatic biopsy specimens. The automated analysis is performed on a set of 20 histological images using supervised machine learning algorithms. Its diagnostic performance presents a minimum fat quantification error of 0.23% compared to that obtained from human semi-quantitative estimates.