One-side Virtual Histological Staining Model for Complex Human Samples

Abstract

Virtual histological staining technique with a label-free auto-fluorescence image as an input is a challenging scientific pursuit to visualize complicated biological structures with distinct features. Recently, most of the related methods follow the two-side image translation architecture to get rid of paired data restriction, which is necessary for unprocessed and thick tissue virtual histological staining style transformation. However, the associated cycle consistency loss will inevitably lead to huge calculation consumption and cannot address the problem of incorrect translation among intracellular and extracellular components, which we termed as incorrect staining. In this paper, we propose a novel and computational-efficient one-side image translation framework to transfer unstained auto-fluorescence images into virtual hematoxylin- and eosin-stained counterparts for both thin and thick human samples. To address the incorrect nuclear staining issue, we design a region-classification loss to incorporate partial supervision information. Experimental data on both thin and thick human lung samples are used to demonstrate that our method is computationally efficient while achieving a comparable transformation performance over the two-side framework.