virtual histology staining of skin tissue using ex vivo confocal microscopy and deep learning

Background: 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.objective: this study aimed to modify an unsupervised deep-learning model to generate h&e-like images from cm images.material and methods: 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.results: 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. conclusion: the integration of cm with deep learning-based virtual staining provides advantages for diagnostic applications by streamlining laboratory staining procedures.