Detection and Spatial Analysis of Hepatic Steatosis in Histopathology Images using Sparse Linear Models

International audienceHepatic steatosis is a defining feature of nonalco-holic fatty liver disease, emerging with the increasing incidence of obesity and metabolic syndrome. The research in image-based analysis of hepatic steatosis mostly focuses on the quantification of fat in biopsy images. This work furthers the image-based analysis of hepatic steatosis by exploring the spatial characteristics of fat globules in whole slide biopsy images after performing fat detection. An algorithm based on morphological filtering and sparse linear models is presented for fat detection. Then the spatial properties of detected fat globules in relation to the hepatic anatomical structures of central veins and portal tracts are explored. The test dataset consists of 38 high resolution images from 21 patients. The experimental results provide an insight into the size distributions of fat globules and their location with respect to the anatomical structures

Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Non-Alcoholic Fatty Liver Disease (NAFLD) is a common syndrome that mainly leads to fat accumulation in liver and steatohepatitis. It is targeted as a severe medical condition ranging from 20% to 40% in adult populations of the Western World. Its effect is identified through insulin resistance, which places patients at high mortality rates. An increased fat aggregation rate, can dramatically increase the development of liver steatosis, which in later stages may advance into fibrosis and cirrhosis. During recent years, new studies have focused on building new methodologies capable of detecting fat cells, based on the histology method with digital image processing techniques. The current study, expands previous work on the detection of fatty liver, by identifying once more a number of diverse histological findings. It is a combined study of both image analysis and supervised learning of fat droplet features, with a specific goal to exclude other findings from fat ratio calculation. The method is evaluated in a total set of 40 liver biopsy images with different magnification capabilities, performing satisfyingly (1.95% absolute error)

Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Non-Alcoholic Fatty Liver Disease (NAFLD) is a common syndrome that mainly leads to fat accumulation in liver and steatohepatitis. It is targeted as a severe medical condition ranging from 20% to 40% in adult populations of the Western World. Its effect is identified through insulin resistance, which places patients at high mortality rates. An increased fat aggregation rate, can dramatically increase the development of liver steatosis, which in later stages may advance into fibrosis and cirrhosis. During recent years, new studies have focused on building new methodologies capable of detecting fat cells, based on the histology method with digital image processing techniques. The current study, expands previous work on the detection of fatty liver, by identifying once more a number of diverse histological findings. It is a combined study of both image analysis and supervised learning of fat droplet features, with a specific goal to exclude other findings from fat ratio calculation. The method is evaluated in a total set of 40 liver biopsy images with different magnification capabilities, performing satisfyingly (1.95% absolute error)