Deep Learning Enables Rapid Whole-Organ Histological Imaging with Ultraviolet-Excited Sectioning Tomography

Abstract

Three-dimensional (3D) histopathology involves the microscopic examination of a specimen, which plays a vital role in studying tissue's 3D structures and the signs of diseases. However, acquiring high-quality histological images of a whole organ is extremely time-consuming (e.g., several weeks) and laborious, as the organ has to be sectioned into hundreds or thousands of slices for imaging. Besides, the acquired images are required to undergo a complicated image registration process for 3D reconstruction. Here, by incorporating a recently developed vibratome-assisted block-face imaging technique with deep learning, we developed a pipeline termed HistoTRUST that can rapidly and automatically generate subcellular whole organ's virtual hematoxylin and eosin (H&E) stained histological images, which can be reconstructed into 3D by simple image stacking (i.e., without registration). The performance and robustness of HistoTRUST have been successfully validated by imaging all six organs (e.g., brain, heart, liver, lung, kidney, and spleen). The imaging process for a whole organ takes hours to days, depending on the volume of imaged samples. The generated 3D dataset has the same color tune as the traditional H&E stained histological images. Therefore, the virtual H&E stained images can be directly analyzed by pathologists. HistoTRUST has a high potential to serve as a new standard in providing 3D histology for research or clinical applications.