Presentation Information
[POS-54]Quantitative Analysis of Liver Fibrosis Expansion Using 3D-Reconstructed Stained Images and Statistical Modeling
*Ryoma Ueda1 (1. Kwansei Gakuin Univ. (Japan))
Keywords:
Liver disease,Three-dimensional structure,Image analysis,Numerical analysis,Mathematical model,Reaction-diffusion equation
The liver is one of the most essential organ for metabolism. The liver is composed of stacked units called hepatic lobules, forming three-dimensional internal structures. When affected by disease, these structures undergo morphological changes due to abnormalities. Understanding the mechanisms behind these changes is essential for elucidating disease progression.
Almost researchers conducted liver analysis using two-dimensional images, while three-dimensional analysis remains limited because it is time-consuming and resource-intensive. However, given the inherently three-dimensional nature of the liver’s internal structure, two-dimensional analysis alone is insufficient for a comprehensive evaluation. This study aims to quantitatively assess fibrotic area expansion in liver disease using three-dimensional reconstructed stained images and analyze the underlying mechanisms through statistical modeling.In this study, we first conducted a three-dimensional quantitative analysis of disease progression. We used hepatic lobules from rats with liver disease (steatosis). Sectioned liver tissues were imaged and reconstructed into three-dimensional structures by stacking the images. Various morphological features associated with disease-induced changes were then measured, revealing intriguing findings regarding the volumetric changes in fibrotic regions.
Next, based on the extracted features, we attempted to reproduce the expansion of fibrotic regions using a mathematical modeling approach. We compared different models, including a diffusion model, the Fisher model, and the FitzHugh-Nagumo model, to investigate the underlying mechanisms governing disease progression.Particular attention was given to both two-dimensional and three-dimensional characteristics during the analysis. We confirmed that the dimensional context (2D vs. 3D) substantially affected the interpretation and depth of the analysis.
This study demonstrates the effectiveness of the proposed method in understanding the spatial two-dimensional and three-dimensional pattern formation mechanisms of diseased regions.
Almost researchers conducted liver analysis using two-dimensional images, while three-dimensional analysis remains limited because it is time-consuming and resource-intensive. However, given the inherently three-dimensional nature of the liver’s internal structure, two-dimensional analysis alone is insufficient for a comprehensive evaluation. This study aims to quantitatively assess fibrotic area expansion in liver disease using three-dimensional reconstructed stained images and analyze the underlying mechanisms through statistical modeling.In this study, we first conducted a three-dimensional quantitative analysis of disease progression. We used hepatic lobules from rats with liver disease (steatosis). Sectioned liver tissues were imaged and reconstructed into three-dimensional structures by stacking the images. Various morphological features associated with disease-induced changes were then measured, revealing intriguing findings regarding the volumetric changes in fibrotic regions.
Next, based on the extracted features, we attempted to reproduce the expansion of fibrotic regions using a mathematical modeling approach. We compared different models, including a diffusion model, the Fisher model, and the FitzHugh-Nagumo model, to investigate the underlying mechanisms governing disease progression.Particular attention was given to both two-dimensional and three-dimensional characteristics during the analysis. We confirmed that the dimensional context (2D vs. 3D) substantially affected the interpretation and depth of the analysis.
This study demonstrates the effectiveness of the proposed method in understanding the spatial two-dimensional and three-dimensional pattern formation mechanisms of diseased regions.