Study of lipid proton difference evaluation via 9.4T MRI analysis of fatty liver induced by exposure to methionine and choline-deficient (MCD) diet and high-fat diet (HFD) in an animal model

Liver fat analysis using optimized support vector machine with support vector regression

BACKGROUND

Fatty liver disease is a common condition caused by excess fat in the liver. It consists of two types: Alcoholic Fatty Liver Disease, also called alcoholic steatohepatitis, and Non-Alcoholic Fatty Liver Disease (NAFLD). As per epidemiological studies, fatty liver encompasses 9% to 32% of the general population in India and affects overweight people.

OBJECTIVE

An Optimized Support Vector Machine with Support Vector Regression model is proposed to evaluate the volume of liver fat by image analysis (LFA-OSVM-SVR).

METHOD

The input computed tomography (CT) liver images are collected from the Chennai liver foundation and Liver Segmentation (LiTS) datasets. Here, input datasets are pre-processed using Gaussian smoothing filter and bypass filter to reduce noise and improve image intensity. The proposed U-Net method is used to perform the liver segmentation. The Optimized Support Vector Machine is used to classify the liver images as fatty liver image and normal images. The support vector regression (SVR) is utilized for analyzing the fat in percentage.

RESULTS

The LFA-OSVM-SVR model effectively analyzed the liver fat from CT scan images. The proposed approach is activated in python and its efficiency is analyzed under certain performance metrics.

CONCLUSION

The proposed LFA-OSVM-SVR method attains 33.4%, 28.3%, 25.7% improved accuracy with 55%, 47.7%, 32.6% lower error rate for fatty image classification and 30%, 21%, 19.5% improved accuracy with 57.9%, 46.5%, 31.76% lower error rate for normal image classificationthan compared to existing methods such as Convolutional Neural Network (CNN) with Fractional Differential Enhancement (FDE) (CNN-FDE), Fully Convolutional Networks (FCN) and Non-negative Matrix Factorization (NMF) (FCN-NMF), and Deep Learning with Fully Convolutional Networks (FCN) (DL-FCN).

Shear-Wave Elastography Using Commercially Available Ultrasound in a Mouse Model of Chronic Liver Disease

Elastography is currently used clinically to diagnose the degree of liver stiffness. We sought to develop a shear-wave elastography (SWE) measurement method using ultrasound in mice and to compare its results with those of other noninvasive tests for liver fibrosis. We divided male mice into three groups (normal (G1), liver fibrosis (G2), and fatty liver (G3)). We measured mouse liver SWE values and compared them with T1rho and T2 values from magnetic resonance imaging results. We also compared the SWE values with the expression levels of a serum liver fibrosis biomarker (Mac-2-binding protein (M2BP)) and hepatic genes. SWE values significantly increased over time in G2 but did not change in G3. T1rho values in G2 and G3 were significantly increased compared with those in G1. T2 values in G2 did not increase compared with those in group 1. T2 values in G3 significantly increased compared with those in groups 1 and 2. In G2, SWE values significantly and positively correlated with T1rho values. SWE values significantly correlated with serum M2BP levels in G2 but did not correlate with inflammatory gene expression. We could measure SWE values to assess the degree of liver fibrosis in mouse models of liver disease.