Generation and Simulation of Yeast Microscopy Imagery with Deep Learning

TL;DR

This paper explores deep learning methods to generate and simulate yeast microscopy images, aiming to enable easier replication of experiments through synthetic data modeling.

Contribution

It introduces a novel GAN for image generation and an advanced future frame prediction model for microscopy simulation, applied to yeast cell imaging.

Findings

Deep learning effectively models TLFM experiments

Proposed models generate realistic synthetic microscopy images

Further research needed for real-world experiment accuracy

Abstract

Time-lapse fluorescence microscopy (TLFM) is an important and powerful tool in synthetic biological research. Modeling TLFM experiments based on real data may enable researchers to repeat certain experiments with minor effort. This thesis is a study towards deep learning-based modeling of TLFM experiments on the image level. The modeling of TLFM experiments, by way of the example of trapped yeast cells, is split into two tasks. The first task is to generate synthetic image data based on real image data. To approach this problem, a novel generative adversarial network, for conditionalized and unconditionalized image generation, is proposed. The second task is the simulation of brightfield microscopy images over multiple discrete time-steps. To tackle this simulation task an advanced future frame prediction model is introduced. The proposed models are trained and tested on a novel dataset that is presented in this thesis. The obtained results showed that the modeling of TLFM experiments, with deep learning, is a proper approach, but requires future research to effectively model real-world experiments.