Semi-Automated Computational Identification of Fibrosis for Enhanced Histopathological Decision Support

Area of Science:

• Cardiovascular Pathology
• Medical Image Analysis
• Computational Pathology

Background:

• Myocardial fibrosis is a key indicator of cardiac disease progression.
• Accurate assessment of myocardial fibrosis in tissue samples is challenging for pathologists.
• Existing manual methods lack the pixel-level accuracy required for precise quantification.

Purpose of the Study:

• To develop and validate a semi-automated system for accurate myocardial fibrosis assessment.
• To improve the efficiency and interpretability of fibrosis quantification in cardiac histopathology.
• To provide pathologists with a reliable tool for quantitative analysis of cardiac tissue.

Main Methods:

• The system integrates Gabor filters with CIELAB color space analysis.
• It performs semi-automated analysis on histopathological myocardial samples.
• The approach allows for pixel-level differentiation between fibrous, healthy, and variant tissues.

Main Results:

• The system achieved 87.5% accuracy in identifying images with fibrosis.
• An overall accuracy of 80% was obtained across all 45 tested images.
• The method demonstrated superior pixel-level accuracy compared to manual estimations.

Conclusions:

• The developed semi-automated system offers a reliable and accurate tool for myocardial fibrosis quantification.
• This approach provides a solid foundation for automated diagnosis in cardiovascular pathology.
• The system enhances the capabilities of pathologists in analyzing cardiac tissue samples.