Abstract
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).
Highlights
Fatty liver develops in 90% of the population, with an alcohol consumption of over 60g on a daily basis
A fatty liver may be further classified as non-alcoholic (NAFL) [1], by the presence of hepatic steatosis without indicating any hepatocellular damage, but by swollen hepatocytes and circular fat cells
Non-Alcoholic Fatty Liver Disease (NAFLD) has been targeted as a massive disease, in Western country’s adults, due to the expanding predominance of obesity and insulin resistance
Summary
Fatty liver develops in 90% of the population, with an alcohol consumption of over 60g on a daily basis. A biopsy slide is scanned through microscopy and a digitized image is extracted by the software Through this technique, each digitized sample can reveal several liver structures, including a) fat cells, b) ballooned cells, as swollen fat wrapped hepatocytes c) central veins, and d) sinusoids, being responsible for mixing nutrientrich blood from the portal vein and with corresponding oxygenrich blood from the hepatic artery. “intra-observer” and “inter-observer” variability, can cause a critical degree of inaccuracy during the quantification of steatosis, among several clinicians Due to these limitations, researchers have undertaken the task of developing reliable automated software for the accurate assessment of steatosis prevalence, with widespread and cutting-edge methods, based on the digital biopsy image processing. The classification stage could be incorporated into already developed fat detection methodologies, as the last identification stage before fat ratio extraction
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