H&E-like staining of OCT images of human skin via generative adversarial network

Abstract

Noninvasive and high-speed optical coherence tomography (OCT) systems have been widely deployed for daily clinical uses. High-resolution OCTs are advancing rapidly; however, grey-level OCT images are not easy to read for pathologists due to the lack of diagnosis specificity compared with hematoxylin and eosin (H&E) stained images. This work presents an OCT to H&E image translation model to convert the OCT images to H&E-like stained images using unpaired OCT and H&E datasets. “H&E like” means the stratum corneum (SC) boundary and the dermal-epidermal junction (DEJ) of the OCT and the translated images are consistent. Pre-trained segmentation models for the DEJ and the SC are exploited to enhance the performance of anatomical image translation and reduce the DEJ and SC lower boundary errors to ±2.3 and ±1.7 μm, respectively. A pre-trained VGG16 network extracts the features of the nuclei. Pearson's correlation coefficient of the nuclei location and size consistency is 84% ± 1%. As a result, in vivo medical image translation accuracy with cellular resolution was achieved.