Artificial Staining of Raw Microscopy Cells using CycleGANs

Doulgeris, Georgios (2021) Artificial Staining of Raw Microscopy Cells using CycleGANs. Master's Thesis / Essay, Artificial Intelligence.

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Abstract

Raw images from a microscope usually need processing in order to be inspected. Researchers use chemical agents to stain the samples and highlight the relevant structures that need to be examined, however this is often a laborious, costly and risky procedure. In this thesis, a CycleGAN is used with the aim to automate the chemical staining of raw microscopy cell images. CycleGAN's unsupervised learning approach is ideal, addressing the lack of available data sets in the process, so that grayscale cell images can be colorized digitally, avoiding the difficulties of chemical staining. We experiment with different techniques and examine whether they can lead to the best possible results. We find that the addition of spectral normalization stabilizes the training of the CycleGAN without adding computational cost. Moreover, we observe that by utilizing two different learning rates for the generators and discriminators as well as using asynchronous updates for the GAN components, we can reach a better convergence point and generate the best staining results, with our best experiment engulfing a structural similarity index measure (SSIM) of 0.881 and a color difference score give by the ΔΕ*94$ metric of 12.73. The cycle consistent nature of CycleGAN can be used to produce paired samples for use in other endeavors. Overall, the tuned-up version of CycleGAN was capable of generating realistic samples, however there were problems with the localization of the peroxisome stain.

Item Type:	Thesis (Master's Thesis / Essay)
Supervisor name:	Schomaker, L.R.B. and Klei, I.J. van der
Degree programme:	Artificial Intelligence
Thesis type:	Master's Thesis / Essay
Language:	English
Date Deposited:	06 May 2021 09:04
Last Modified:	06 May 2021 09:04
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/24349

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