Background & objectives: Cardiac diseases like arrhythmogenic cardiomyopathy are linked to structural abnormalities like fibrosis and fat replacement. Assessments by pathologists are somewhat subjective and using state-of-the-art stereology is time-consuming. We aimed to develop a standardized semi-automated method for cardiac phenotyping using QuPath.


Methods: Whole-slide samples of Picro Sirius Red-stained myocardial tissue were used to develop a pipeline in QuPath. Five cases (25 regions) were used for training. The process included training a pixel classifier to annotate the tissue sample, semi-automatically dividing the tissue into regions, training a different pixel classifier to differentiate between fibrosis and myocardium, and using Cellpose to detect single adipocytes.

Results: The accuracy of the pixel classifiers was estimated to be over 96% on the separate set of training images, and the preliminary tests look promising. We will annotate ground-truth regions in five randomly selected samples from different cases and compare them blinded with automated classification. Similarity will be calculated using the Jaccard coefficient, and performance will be measured using the F1-score.


The method will be further tested by applying it to five arrhythmogenic cardiomyopathy cases and matched controls in a blinded manner, and the results will be compared with the established diagnostic criteria of arrhythmogenic cardiomyopathy. The results of the study are pending and will be presented at the conference.


Conclusion: The expected outcome is an automated method for estimating fibrosis, fat, and residual myocytes in Picro Sirius Red-stained myocardial tissue. We expect that this method will contribute to a standardized and reproducible tool that can be used to establish a cardiac phenotype in cardiac pathological research projects and, hopefully, in future daily diagnostics.