Deep learning enables virtual hematoxylin and eosin stained histological imaging for 2D and 3D histopathology

Abstract

Histological image with hematoxylin and eosin (H&E) staining is essential for histopathological diagnosis and has been the gold standard of tumor diagnosis. However, the acquiring of H&E stained images takes around one week in the hospital due to the involved lengthy and laborious steps such as tissue preprocessing, sectioning, and staining. In this paper, I presented two novel deep learning assisted imaging methods that can shorten the acquisition time of H&E stained images from one week to several minutes, greatly increasing the efficacy of histopathological diagnosis. The first imaging method combines deep learning with ultraviolet photoacoustic microscopy (UV-PAM), which can rapidly get the H&E digital staining histological image of unprocessed fresh mouse brain within 15 minutes. The second method integrates deep learning with computational autofluorescence microscopy (CHAMP), which further reduces the time to 1 minute. In addition to the 2D histological imaging, by combing deep learning with a vibratome-assisted 3D ultraviolet imaging system, I further developed a pipeline for the rapid and fully automatic acquiring of H&E stained histopathological images of a whole organ. With the designed method, we rapidly get the 3D H&E histological images of versatile organs, such as brain, liver, kidney, heart, lung, heart, spleen with little human involvement. The proposed method might promote the routine use of 3D histology in research or clinic, as it greatly shortens the acquisition time of 3D H&E histological images from one month to ~3 days. Lastly, I summarize the advances that our three deep learning assisted histological imaging methods have brought to conventional 2D and 3D histology. Some improvements and new perspectives are also discussed.

Date of Award	2022
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Tsz Wai WONG (Supervisor)