Virtual Histology Staining of Skin Tissue using Ex Vivo Confocal Microscopy and Deep Learning

The use of Hematoxylin-and-Eosin (H&E) staining is widely accepted as the most reliable method for diagnosing pathological tissues. However, the conventional H&E staining process for tissue sections is time-consuming and requires significant labor. In contrast, Confocal Microscopy (CM) enables quick and high-resolution imaging with minimal tissue preparation by fluorescence detection. However, it seems harder to interpret images from CM than H&E-stained images.

This study aimed to modify an unsupervised deep-learning model to generate H&E-like images from CM images.

This analytical study evaluated the efficacy of CM and virtual H&E staining for skin tumor sections related to Basal Cell Carcinoma (BCC). The acridine orange staining, combined with virtual staining techniques, was used to simulate H&E dyes; accordingly, an unsupervised CycleGAN framework, trained to virtually stain CM images was implemented. The training process incorporated adversarial and cycle consistency losses to ensure a precise mapping between CM and H&E images without compromising image content. The quality of the generated images was assessed by comparing them to the original images.

The CM images, specifically focusing on subtyping BCC and evaluating skin tissue characteristics, were qualitatively assessed. The H&E-like images generated from CM using the CycleGAN model exhibited both qualitative and quantitative similarities to real H&E images.

The integration of CM with deep learning-based virtual staining provides advantages for diagnostic applications by streamlining laboratory staining procedures.