Sethunath, Deepak2018-04-272018-04-272018-04-26https://hdl.handle.net/1805/15955http://dx.doi.org/10.7912/C2/2353Indiana University-Purdue University Indianapolis (IUPUI)This thesis explores a minimally invasive approach of diagnosing Non-Alcoholic Fatty Liver disease (NAFLD) on mice and humans which can be useful for pathologists while performing their diagnosis. NAFLD is a spectrum of diseases going from least severe to most severe – steatosis, steatohepatitis, fibrosis and finally cirrhosis. This disease primarily results from fat deposition in the liver which is unrelated to alcohol or viral causes. In general, it affects individuals having a combination of at least three of the five metabolic syndromes namely, obesity, hypertension, diabetes, hypertriglyceridemia, and hyperlipidemia. Given how common these metabolic syndromes have become, the rate of NAFLD has increased dramatically over the years affecting about three-quarters of all obese individuals including many children, making it one of the most common diseases in United States. Our study focuses on building various computational models which help identify different histological features in a liver biopsy image, thereby analyzing if a person is affected by NAFLD or not. Here, we develop and validate the performance of automated classifiers built using image processing and machine learning methods for detection of macro- and microsteatosis, lobular and portal inflammation and also categorize different types fibrosis in murine and human fatty liver disease and study the correlation of automated quantification of macrosteatosis, lobular and portal inflammation, and fibrosis (amount of collagen) with expert pathologist’s semi-quantitative grades. Our research for macrosteatosis and microsteatosis prediction shows the model’s precision and sensitivity as 94.2%, 95% for macrosteatosis and 79.2%, 77% for microsteatosis. Our models detect lobular and portal inflammation(s) with a precision, sensitivity of 79.6%, 77.1% for lobular inflammation and 86%, 90.4% for portal inflammation. We also present the first study on identification of the six different types of fibrosis having a precision of 85.6% for normal fibrosis and >70% for portal fibrosis, periportal fibrosis, pericellular fibrosis, bridging fibrosis and cirrhosis. We have also quantified the amount of collagen in a liver biopsy and compared it to the pathologist semi-quantitative fibrosis grade.Non Alcoholic Fatty Liver DiseaseAutomatic detectionComputer VisionMachine learningImage ProcessingPattern RecognitionThesis10.7912/C2V36H