Abstract
In histopathology, acquiring subcellular-level three-dimensional (3D) tissue structures efficiently and without damaging the tissues during serial sectioning and staining remains a formidable challenge. We address this by integrating holotomography with deep learning and creating 3D virtual hematoxylin and eosin (H&E) images from label-free thick cancer tissues. This method involves measuring the tissues' 3D refractive index (RI) distribution using holotomography, followed by processing with a deep learning-based image translation framework to produce virtual H&E staining in 3D. Applied to colon cancer tissues up to 50 µm thick-far surpassing conventional slide thickness-this technique provides direct methodological validation through chemical H&E staining. It reveals quantitative 3D microanatomical structures of colon cancer with subcellular resolution. Further validation of our method's repeatability and scalability is demonstrated on gastric cancer samples across different institutional settings. This innovative 3D virtual H&E staining method enhances histopathological efficiency and reliability, marking a significant advancement in extending histopathology to the 3D realm and offering substantial potential for cancer research and diagnostics.