Purpose: Identification of cardiomyocyte nuclei is of great importance when assessing heart tissue for cardiovascular disease. However, manual identification of cardiomyocyte nuclei in hematoxylin & eosin (H&E)-stained heart tissue is particularly complex due to extensive heterogeneity in morphology. While expensive and time-consuming staining processes can be utilized to identify cardiomyocyte nuclei, this study proposes to test the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to automate the identification of cardiomyocyte nuclei in H&E-stained heart tissue without the need for additional staining.

Materials and Methods: 100 H&E-stained heart tissue sample regions from cancer patients were manually labeled with the input from an expert pathologist. Sample regions were processed into 3,584 labeled tiles and 3,584 tiles without labels, and then sorted, augmented, and run through a CycleGAN to facilitate the identification of cardiomyocyte nuclei. Performance was assessed with Fréchet Inception Distance (FID), Fréchet ResNet-50 Distance (FRD), sensitivity, specificity, and accuracy.

Results: Sensitivity and specificity were 61.6% sensitivity, 96.3% specificity with an accuracy of 85.0% for 30 randomly selected samples. FID and FRD reached as low as 64.8 and 2.46, respectively, with downward trend for FID and high volatility with a slight downward trend for FRD. Notably, the best results were achieved when FRD was at its lowest value.