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Abstract
Microstructures in the liver are primarily composed of hepatocytes, hepatic blood, and biliary vessels. Because each hepatocyte comes in contact with both vessels, these vessels form three-dimensional (3D) periodic network patterns. Confocal microscope images are useful for observing 3D structures; however, it is necessary to explicitly describe the vessel structures using 3D images of sinusoidal endothelial cells. For this purpose, we propose a new approach for image segmentation based on the Turing reaction-diffusion model, in which temporal and spatial patterns are self-organized. Turing conditions provided reliable tools for describing the 3D structures. Moreover, using the proposed method, the sinusoidal patterns of rats fed a high-fat/high-cholesterol diet were examined; these rats exhibited pathological features similar to those of human patients with nonalcoholic steatohepatitis related to metabolic syndrome. The findings showed that the parameter in diffusion terms differed significantly among the experimental groups. This observation provided a heuristic argument for parameter selection leading to pattern recognition problems in diseased rats.
Highlights
Because each hepatocyte comes in contact with both vessels, these vessels form three-dimensional (3D) periodic network patterns
Confocal microscope images are useful for observing 3D structures; it is necessary to explicitly describe the vessel structures using 3D images of sinusoidal endothelial cells
We propose a new approach for image segmentation based on the Turing reaction-diffusion model, in which temporal and spatial patterns are self-organized
Summary
The liver is one of the most important organs and is capable of regenerating it-. H. Shoji self [1]. Many researchers have become interested in development of methods for reconstruction of the liver based on tissue engineering techniques [2]. These types of periodic networks cannot be fully understood in two dimensions [3] [4]. To date, imaging analyses in hepatology have largely relied on two-dimensional (2D) images because appropriate methods for three-dimensional (3D) reconstruction and quantification have not been developed
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