The Quantification of Myocardial Fibrosis on Human Histopathology Images by a Semi-Automatic Algorithm

• Published in: Applied sciences Vol. 14; no. 17; p. 7696
• Main Authors: Gonciar, Diana, Berciu, Alexandru-George, Danku, Alex Ede, Lorenzovici, Noemi, Dulf, Eva-Henrietta, Mocan, Teodora, Nicula, Sorina-Melinda, Agoston-Coldea, Lucia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Algorithms; Cardiomyocytes; Collagen; Fibrosis; Histochemistry; Histopathology; Learning strategies; Localization; Machine learning; Medical imaging equipment; Methods; myocardial fibrosis; Python; semi-automatic quantification; Software; Iowa; Romania; United States > US
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14177696

(1) Background: Considering the increasing workload of pathologists, computer-assisted methods have the potential to come to their aid. Considering the prognostic role of myocardial fibrosis, its precise quantification is essential. Currently, the evaluation is performed semi-quantitatively by the pathologist, a method exposed to the issues of subjectivity. The present research proposes validating a semi-automatic algorithm that aims to quantify myocardial fibrosis on microscopic images. (2) Methods: Forty digital images were selected from the slide collection of The Iowa Virtual Slidebox, from which the collagen volume fraction (CVF) was calculated using two semi-automatic methods: CIELAB-MATLAB® and CIELAB-Python. These involve the use of color difference analysis, using Delta E, in a rectangular region for CIELAB-Python and a region with a random geometric shape, determined by the user’s cursor movement, for CIELAB-MATLAB®. The comparison was made between the stereological evaluation and ImageJ. (3) Results: A total of 36 images were included in the study (n = 36), demonstrating a high, statistically significant correlation between stereology and ImageJ on the one hand, and the proposed methods on the other (p < 0.001). The mean CVF determined by the two methods shows a mean bias of 1.5% compared with stereology and 0.9% compared with ImageJ. Conclusions: The combined algorithm has a superior performance compared to the proposed methods, considered individually. Despite the relatively small mean bias, the limits of agreement are quite wide, reflecting the variability of the images included in the study.