Dennis Kristiansson, Adrian Lundell, Fredrik Meisingseth and David Tonderski defended their Bachelor Thesis at Chalmers University of Technology on 27 May 2020. Congrats!
Title: Deep learning for particle tracking
Abstract: The use of machine learning for classication has in recent years spread into a wide range of disciplines, amongst them the detection of particles for particle tracking on microscopy data. We modified the Python package DeepTrack, which makes use of deep learning to detect particles, creating a package called U-Track. By using a new network architecture based on a U-Net, better performance and higher computational efficiency than DeepTrack was achieved on images with multiple particles. Furthermore, functionality to track detected particles over series of frames was developed. The application of U-Track on experimental data from two-dimensional flow nanometry produced tracks consistent with theory, as well as tracking larger quantities of particles over longer periods of time compared to a digital filter based benchmark algorithm.
Supervisors: Daniel Midtvedt, Department of Physics, University of Gothenburg
Examiner: Lena Falk, Department of Physics, University of Gothenburg
Opponents: Patrik Wallin, Isak Pettersson, Alexei Orekhov, Anna Wisakanto
Place: Online Meeting
Time: 27 May, 2020, 9:00