Chemical and computational approaches for optimizing histological images

Abstract

As a key component in a histological examination, histological images play a pivotal role in both tissue investigation and disease identification. To meet the gradually expanding demands of histological images utilization, technology and approaches from other disciplines have been widely applied to optimize the histological technique, especially in the aspects of microscope examination and staining. In this thesis, two projects on this topic are presented. The first project investigated and determined a tissue optical clearing method named CUBIC as the optimized approach to enhancing the performance of a novel imaging technique, ultraviolet photoacoustic microscopy. The outstanding clearing capacity provided by CUBIC was able to improve the imaging modality in terms of image contrast and image depth. Apart from the investigation on microscope examination, the second part of this manuscript focus on optimizing the staining protocol. Considering the current protocol involves a series of labour-intensive operations when a single slice is required to present in multiple stain colours, the second project introduced a novel unsupervised deep learning model for multiplexed virtual staining realization. Coined as StarcleGAN, the approach can synthesize high-quality staining images of single tissue slices under different staining methods.

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