Hillevi Wachmeister will defend her Master Thesis in Physics at Chalmers University of Technology on 11 June 2020
Title: Tracking marine micro organisms using deep learning
The goal of this project is to develop a software that can be used to study swimming patterns of marine micro organisms. The software is based on a neural network, which is trained to recognize different types of plankton. The predictions from the network are then used to find the positions of the plankton, and then track their movements.
The project is divided into two parts. First, videos containing only one type of plankton, Lingulodinium polyedra and Alexandrium tamarense respectively, are analyzed. A type of neural network, called U-net, is trained to segment the input images into background and plankton sections. From the segmented images, positions can be obtained and then connected to form a trajectory for each plankton. The drift of the plankton movements is calculated and subtracted from the trajectories, and finally the speed and net displacement is calculated. The results from the single plankton experiments are compared to a previous analysis that was made using the algorithmic method TrackMate.
Secondly, videos containing two types of plankton are analyzed. Two experiments are conducted using Strombidium arenicola and Rhodomonas baltica in the first experiment, and Alexandrium tamarense and Rhodomonas baltica in the second. The segmented images, obtained from the U-net, consists of an additional plankton section for the second type of plankton present in the experiment.
The analysis of the single plankton experiments yields longer and fewer trajectories using the U-net method, compared to the previous TrackMate results. This shows that the TrackMate method is losing plankton at more positions, compared to the U-net method. The U-net method is therefore able to track each plankton for a longer time. The multi-plankton experiments proves the network’s ability to distinguish and track multiple plankton at the same time.
Master programme: MPPHYS – Physics
Supervisor: Daniel Midtvedt
Examiner: Giovanni Volpe
Opponent: Frida Eriksson
Date: 11 June 2020, 9:00
Place: Nexus + Online via Zoom