Histology-guided 3D virtual staining of microCT-imaged lung tissue via deep learning.

Abstract

Histologically stained tissue sections are considered the gold standard for studying microscopic anatomy and diagnosing disease in clinical practice. However, the processes of sectioning and staining are laborious, and the overall method relies on two-dimensional analysis. In contrast, X-ray-based virtual histology offers the advantage of virtual sectioning while retaining the full three-dimensional (3D) volumetric representation of the tissue. Nevertheless, its greyscale nature limits its specificity compared to conventional histological stains and creates an additional barrier for pathologists, whose training is primarily based on colour-stained histology. In this work, we present a histology-guided enhancement platform that can integrate the 3D information provided by synchrotron radiation phase-contrast microCT (PCµCT) with the rich visual features characteristic of histological stains. We introduce a multistage PCµCT-histology co-registration method combined with a virtual staining deep neural network and demonstrate successful virtual histological staining of PCµCT human and mouse lung tissue that closely resembles standard histology. We evaluate our strategy on multiple histological stains and apply it to identify 3D collagen-based remodelling of pulmonary arteries in patients with pulmonary hypertension. Overall, we expect our work to facilitate the integration of PCµCT as a clinical tool for 3D analysis of biological tissues and support non-destructive 3D pathology for disease biomarker exploration.