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

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.