Lukas Niese defended his Master thesis on 17 January 2022. Congrats!

Lukas Niese defended his Master thesis in Physics at the Technische Universität Dresden on 17 January 2022. Congrats!

(Image from Lukas Niese’s Master Thesis)
Title: Application of Deep Learning for Investigation of Chemotactic Behaviour in Marine Microorganisms

Deep learning has recently become a powerful instrument, enhancing research in many fields and profiting from abundant availability of manifold data sets. In active matter research, medicine and biology there is huge demand of robust and accurate methods to track and analyse micro scale particles and cells in microscopy images. The Pyhton based software Deeptrack 2.0 offers a basic toolkit to build customized deep learning methods for particle localization, classification and tracking. In this project Deeptrack 2.0 was used to track marine microorganisms and investigate their motion in response to chemical stimulants, known as chemotaxis. In addition, the accuracy of particle localization and classification was measured by three different benchmark tests, which imitated shapes and movement of real microorganisms. The results were compared with the performance ofthe algorithmic standard method Trackmate by Fiji ImageJ. Deeptrack 2.0 has shown a significantly better performance for particles with complex shapes and with time varying appearance were to be tacked. However Trackmate is slightly more accurate in locating small particles appearing in Gaussian intensity distribution. In the experimental part two test assays have been developed and proven a facile and robust way to study chemoattraction in the autotrophic green alga Dunaliella tertiolecta. Deeptrack was successfully applied create and analyze the cell trajectories according to velocity and spatial distribution in individuals. Based on the developed combination of experiment and computational analysis, further investigations can be carried out to elucidate the chemical and ecological nature of chemotaxis in Dunaliella tertiolecta.

​Adviser: Prof. Giovanni Volpe
Examiner: Prof. Alexander Eychmüller (TU Dresden)
Date: 17 January 2022
Time: 17:00
Place: TU Dresden and Online via Zoom

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