Label-free, three-dimensional virtual staining of histopathological slides exploiting deep learning and refractive index tomography

Hematoxylin and eosin (H&E) staining has been a gold standard for diagnosing cancer in histopathology. Nonetheless, H&E staining heavily requires time, resources and is limited in two-dimensional analyses. Here, we propose three-dimensional (3D) virtual staining of hematoxylin and eosin (H&E) from label-free refractive index (RI) images of histopathological slides. To achieve this, we integrated RI tomography, which provides 3D RI distribution without staining, with deep learning. By predicting brightfield (BF) images from RI images of thick colon slides, we generate virtually stained 3D H&E images. The accuracy of our approach is validated against conventional staining methods.