THESIS
2022
1 online resource (x, 64 pages) : illustrations (chiefly color)
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 an...[
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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.
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