Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens

Area of Science:

• Digital pathology
• Computational imaging
• Histopathology analysis

Background:

• Quantitative pathology requires precise analysis of cellular and tissue morphology.
• Automated methods can improve the consistency and efficiency of histopathological assessments.
• Accurate quantification of hepatocyte features is crucial for diagnosing and grading liver conditions.

Purpose of the Study:

• To develop and validate a digital image analysis method for automated hepatocyte segmentation and morphological feature quantification.
• To support quantitative pathology by providing objective measurements of liver tissue structures.
• To assess the method's effectiveness in quantifying the Edmondson grade for liver disease.

Main Methods:

• Digital image analysis for automatic segmentation of hepatocyte structures.
• Extraction of sinusoids, fat droplets, and stromata to isolate trabeculae.
• Measurement of trabeculae morphology and division of images into cords.
• Calculation of nuclear-cytoplasmic ratio, nuclear density, and number of layers using local cord features.

Main Results:

• The proposed digital image analysis method successfully segmented hepatocyte structures.
• Morphological features of trabeculae and local cords were quantified.
• The method demonstrated effectiveness in the quantitative assessment of the Edmondson grade using surgical specimens.

Conclusions:

• The developed digital image analysis method provides a robust approach for quantitative pathology.
• Automated segmentation and feature quantification of hepatocytes can enhance diagnostic accuracy.
• This technique shows significant potential for objective liver disease grading.