Self-Attention Based Virtual Staining for Bright-field Images of Label-free Human Carotid Atherosclerotic Plaque Tissue Section.

Abstract

Histological analysis of carotid atherosclerotic plaque tissue specimens is a widely used method for studying the diagnosis of ischemic heart disease and stroke. Understanding the physiological and pathological mechanisms of carotid atherosclerotic plaque is of great significance for the effective prevention and treatment of plaque formation and rupture. In this work, we adapted a self-attention generative adversarial model to virtually stain label-free human carotid atherosclerotic plaque tissue sections into corresponding H&E stained sections. The self-attention mechanism and multi-layer structure are introduced into the residual steps of the generator and in the discriminator. Our method achieved the best performance (SSIM, PSNR, and LPIPS of 0.53, 20.29, and 0.30, respectively) in comparison with other state-of-the-art methods.Clinical Relevance - The proposed approach allows for the virtual staining of unlabeled human carotid plaque tissue images. It identifies the histopathological features of atherosclerotic plaques in the same tissue sample which could facilitate the development of personalized prevention and other interventional treatments for carotid atherosclerosis.