https://www.journal-of-hepatology.eu/article/S0168-8278(05)00569-6/pdf

A pilot approach for quantitative assessment of liver fibrosis using ultrasound: preliminary results in 79 cases

Coming up with a quantitative diagnosis method for liver fibrosis using ultrasound would be highly significant. To permit tissue characterization using the characteristics of the echo signal such as power spectrum, texture parameters, local attenuation, and statistical characteristics, the relation between complicated scatterer structures and the echo signal must be understood. In this study, we analyzed the property of the echo amplitude envelope using computer-simulated scatterer models. These models mimicked various liver conditions to evaluate our quantitative parametric imaging methods. Statistical echo characteristics changed with the density of the heterogeneous scatterer buried in a speckle. The new analysis method for a medium in which some tissues are embedded was proposed in consideration of analysis results from computer simulations. In the new method, it is possible to eliminate the influence of a cyst or veins and to detect the existence of fibers more clearly than in previous methods.

Proposal of a parametric imaging method for quantitative diagnosis of liver fibrosis

Ultrasound as a noninvasive imaging technique is widely used to diagnose liver diseases. Texture analysis and classification of ultrasound liver images have become an important research topic across the world. In this study, GLGCM (Gray Level Gradient Co-Occurrence Matrix) was implemented for texture analysis of ultrasound liver images first, followed by the use of GLCM (Gray Level Co-occurrence Matrix) at the second stage. Twenty two features were obtained using the two methods, and seven most powerful features were selected for classification using BP (Back Propagation) neural network. Fibrosis was divided into five stages (S0-S4) in this study. The classification accuracies of S0-S4 were 100%, 90%, 70%, 90% and 100%, respectively.

Texture analysis and classification of ultrasound liver images.

OBJECTIVE We propose a new sonographic technique for detecting parenchymal stiffness of the liver. This technique measures the physiologic change in the diameter of the hepatic inferior vena cava (IVC). The hepatic portion of the IVC is extensively attached to the hepatic parenchyma so that changes in diameter of the venous lumen could not occur without changes in the shape of the surrounding parenchyma. Therefore, increased parenchymal stiffness due to cirrhosis or fibrosis may result in an impaired change in venous diameter. METHODS Thirty patients with histologically proven cirrhosis and 30 patients with normal livers were examined. A commercial ultrasound system was used in conjunction with a convex probe at a center frequency of 4.0 MHz. The major axis of the IVC was measured during normal breathing. The patients were then requested to take a deep breath to produce negative intrathoracic pressure, and the same measurement as that during normal breathing was repeated immediately. RESULTS In the normal liver group, the maximal diameter of the vena cava was 2.35 +/- 0.34 cm (mean +/- SD), and this was reduced by 1.30 +/- 0.67 cm (range, 0.4-2.85 cm) during deep inspiration. In the cirrhotic patient group, the maximal diameter was 1.74 +/- 0.35 cm, and this was reduced by 0.03 +/- 0.09 cm (range, 0.0-0.4 cm) (P < .0001). CONCLUSIONS The physiologic change in the diameter of the hepatic portion of the IVC enhanced by deep respiration may reflect the stiffness of hepatic parenchyma.

Impairment of change in diameter of the hepatic portion of the inferior vena cava: a sonographic sign of liver fibrosis or cirrhosis.

Background: Semiquantitative staging of liver fibrosis is a highly subjective procedure and may lead to an uncertainty in judgment regarding the degree of severity and hence the progression of the disease.



Aim: In this work, we present an automated quantification system (AQS) for evaluating the degree of severity of fibrosis in liver biopsies based on Ishak et al.'s classification. Accordingly, liver fibrosis is classified into six classes depending on its severity and progression. The described system is of special value in accurately assessing the prognosis of chronic liver disease.



Methods: In our method, we tried to approximate the architecture of the fibrosis in the subject sample using texture features and shape representation of the fibrosis structural expansion with an overall accuracy of about 98%.



Results and conclusion: The presented AQS is considered to be a novel approach in the domain of automatic liver fibrosis quantification. It is a true quantification and intelligent approach that attempts to utilize the current semiquantitative methods of liver fibrosis assessment to turn them into real quantitative ones with significant reduction in variability and subjectivity. We propose that our method can be adopted by a panel of expert liver pathologists and software to be developed and used on a wide scale.

Quantitative assessment of liver fibrosis: a novel automated image analysis method.

To objectively evaluate the parenchymal echo pattern of cirrhotic liver and chronic hepatitis, the authors applied an image analyzing system (IAS) using a neural network. Autopsy specimens in a water tank (n=13) were used to examine the relationship between the diameter of the regenerative nodule and the coarse score (CS) calculated by IAS. CS was significantly correlated with the diameter of the regenerative nodule (p<0.0001, r=0.966). CS is considered to be useful for evaluating the coarseness of the parenchymal echo pattern.

A. Kobayashi

Papers

An image analyzing system using an artificial neural network for evaluating the parenchymal echo pattern of cirrhotic liver and chronic hepatitis