Nils Jacobson defended his Master thesis on 16 February 2021. Congrats!

Nils Jacobson defended his Master thesis in MPCAS at the Chalmers University of Technology on 16 February 2021. Congrats!

Screenshot of Nils Jacobson’s Master Thesis defence.
Title: Vascular Bifurcation Detection in Cerebral CT Angiography Using CNN and Frangi Filters

Segmentation and feature extraction are important tools for analysing and visualizing information in medical image data, particularly in vascular image data which relates to widely spread vascular diseases. Vessel segmentation is extensively featured in research, recently adapting trends in deep learning image processing. This paper aims to develop a vessel bifurcation detection method to support a seed point based segmentation approach. The suggested approach is a combination of classification, with a convolutional neural network (DenseNet), local vessel segmentation, with Frangi filters, and 3D morphological skeletonization. A small data set is produced for network training and evaluation. Results indicate a high classification accuracy which filters problematic samples for the Frangi filter. Thus the combination is able to suggest quality branch seed points under most circumstances. Next step would be to expand the data set to enable further optimization and more rigid evaluation. In any case a combination of a high performance classifier followed by qualitative assessment of local samples show potential.​

​Name of the master programme: MPCAS – Complex Adaptive Systems
Supervisor: Jonna Hellström and Giovanni Volpe
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Eva Škvor

Place: Online via Zoom
Time: 16 February, 2021, 16:00

Link: Master thesis presentation Nils Jacobson

Falko Schmidt defended his PhD Thesis in Physics on 15 January 2021. Congrats!

Falko Schmidt defended his PhD Thesis in Physics on Friday, 15 January 2021. Congrats!

The disputation took place at 9 a.m., in PJ salen, Fysikgården.
Falko Schmidt’s opponent, Peer Fischer, gave an introductory presentation with title “Microswimmers and motile active matter”.


From Falko Schmidt’s PhD Thesis.
Title: Active Matter in a Critical State: From passive building blocks to active molecules, engines and droplets

The motion of microscopic objects is strongly affected by their surrounding environment. In quiescent liquids, motion is reduced to random fluctuations known as Brownian motion. Nevertheless, microorganisms have been able to develop mechanisms to generate active motion. This has inspired researchers to understand and artificially replicate active motion. Now, the field of active matter has developed into a multi-disciplinary field, with researchers developing artificial microswimmers, producing miniaturized versions of heat engines and showing that individual colloids self-assemble into larger microstructures. This thesis taps into the development of artificial microscopic and nanoscopic systems and demonstrates that passive building blocks such as colloids are transformed into active molecules, engines and active droplets that display a rich set of motions. This is achieved by combining optical manipulation with a phase-separating environment consisting of a critical binary mixture. I first show how simple absorbing particles are transformed into fast rotating microengines using optical tweezers, and how this principle can be scaled down to nanoscopic particles. Transitioning then from single particles to self-assembled modular swimmers, such colloidal molecules exhibit diverse behaviour such as propulsion, orbital rotation and spinning, and whose formation process I can control with periodic illumination. To characterize the molecules dynamics better, I introduce a machine-learning algorithm to determine the anomalous exponent of trajectories and to identify changes in a trajectory’s behaviour. Towards understanding the behaviour of larger microstructures, I then investigate the interaction of colloidal molecules with their phase-separating environment and observe a two-fold coupling between the induced liquid droplets and their immersed colloids. With the help of simulations I gain a better physical picture and can further analyse the molecules’ and droplets’ emergence and growth dynamics. At last, I show that fluctuation-induced forces can solve current limitations in microfabrication due to stiction, enabling a further development of the field towards smaller and more stable nanostructures required for nowadays adaptive functional materials. The insights gained from this research mark the path towards a new generation of design principles, e.g., for the construction of flexible micromotors, tunable micromembranes and drug delivery in health care applications.

Benjamin Midtvedt defended his Master Thesis on June 15, 2020. Congrats!

Benjamin Midtvedt defended his Master Thesis in Engineering Mathematics and Computer Science at Chalmers University of Technology on 15 June 2020. Congrats!

Screenshot of Benjamin Midtvedt’s Master Thesis defence.
Title: DeepTrack: A comprehensive deep learning framework for digital microscopy

Despite the rapid advancement of deep-learning methods for image analysis, they remain underutilized for the analysis of microscopy images. State of the art methods require expertise in deep-learning to implement, disconnecting the development of new methods from end-users. The packages that are available are typically highly specialized, challenging to reappropriate, and almost impossible to interface with other methods. Finally, training deep-learning models often requires large datasets of manually annotated images, making it prohibitively difficult to procure training data that accurately represents the problem.

DeepTrack is a deep-learning framework targeting optical microscopy, designed to account for each of these issues. Firstly, it is packaged with an easy-to-use graphical user interface, solving standard microscopy problems with no required programming experience. Secondly, it bypasses the need for manually annotated experimental data by providing a comprehensive programming API for creating representative synthetic data, designed to exactly suit the problem. DeepTrack creates physical simulations of samples described by refractive index or fluorophore distributions, using fully customizable optical systems. To accurately represent the data to be analyzed, DeepTrack supports arbitrary optical aberration and experimental noise. Thirdly, many standard deep-learning methods are packaged with DeepTrack, including architectures such as U-NET, and regularization techniques such as augmentations, decreasing the barrier to entry. Finally, the framework is fully modular and easily extendable to implement new methods, providing both longevity and a centralized foundation to deploy new deep-learning solutions.

We demonstrate the versatility of DeepTrack by training networks to solve a broad range of common microscopy problems, including particle tracking, cell-counting in dense biological samples, multi-particle 3-dimensional tracking, and cell segmentation and classification.

Master Programme: Engineering Mathematics and Computer Science
Supervisor: Giovanni Volpe
Examiner: Giovanni Volpe
Opponents: Aykut Argun and Saga Helgadóttir

Time: 15 June 2020, 16:00
Place: Online via Zoom

Tobias Sandström and Lars Jansson defended their Master Thesis on 15 June, 2020. Congrats!

Tobias Sandström and Lars Jansson defended their Master Thesis in Complex Adaptive Systems at Chalmers University of Technology on 15 June 2020. Congrats!

Screenshot of Tobias Sandström and Lars Jansson’s Master Thesis defence.
Title: Graph Convolutional Neural Networks for Brain Connectivity Analysis​​

We explore the strengths and limitations of Graph Convolutional Neural Networks (GCNs) for classification of graph structured data. GCNs differs from regular Artificial Neural Networks (ANNs) in that they operate directly on graph structures by defining convolutional operators in a non-euclidean space. We show that GCNs perform well on graph structured data, where regular ANNs typically fail due to the arbitrary ordering of nodes. Different GCN architectures are examined and compared to simplistic ANNs. Tests are initially performed on simulated data sets with implicit class-dissimilarities in regards to graph structures. We demonstrate that GCNs is vital in accurately classifying the simulated data. Network performance is later evaluated on structured MRI-data, displaying cortical thicknesses for 68 regions in the brain of patients with Alzheimer’s disease and a healthy control group. On the structured MRI-data, both GCNs and regular ANNs are shown to be able classifiers. However, it is crucial for the performance of ANNs that an order of nodes can be imposed on the MRI-data from labeled brain regions.

Supervisors: Jonas Andersson & Alice Deimante Neimantaite, Syntronic AB
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Jonathan Bergqvist

Time: 15 June, 2020, 14:00
Place: Online via Zoom

Hillevi Wachtmeister defended her Master Thesis on June 11, 2020. Congrats!

Hillevi Wachmeister defend her Master Thesis in Physics at Chalmers University of Technology on 11 June 2020. Congrats!

Screenshot of Hillevi Wachmeister’s Master Thesis defence.
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

Sofia Lundborg defended her Master Thesis on June 4, 2020. Congrats!

Sofia Lundborg defended her Master Thesis in Complex Adaptive Systems at Chalmers University of Technology on 4 June 2020. Congrats!

Screenshot of Sofia Lundborg’s Master Thesis defence.
Title: Training Binary Deep Neural Networks Using Knowledge Distillation

Binary networks can be used to speed up inference time and make image analysis possible on less powerful devices. When binarizing a network the accuracy drops.
The thesis aimed to investigate how the accuracy of a binary network can be improved by using knowledge distillation.
Three different knowledge distillation methods were tested for various network types. Additionally, different architectures of a residual block in ResNet were suggested and tested. Test on CIFAR10 showed an 1.5% increase in accuracy when using knowledge distillation and an increase of 1.1% when testing on ImageNet dataset. The results indicate that the suggested knowledge distillation method can improve the accuracy of a binary network. Further testing needs to be done to verify the results, especially longer training. However, there is great potential that knowledge distillation can be used to boost the accuracy of binary networks.

Master programme: MPCAS – Complex Adaptive Systems
Supervisor: Giovanni Volpe
Supervisors @ Bit Addict: Karl Svensson, Fredrik Ring and Niclas Wikström
Examiner: Giovanni Volpe
Opponent: Viktor Olsson, Wilhelm Tranheden

Time: June 4, 2020 at 15:00
Place: Online via Zoom

Dennis Kristiansson, Adrian Lundell, Fredrik Meisingseth, David Tonderski defended their Bachelor Thesis. Congrats!

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


Fatemeh Kalantarifard defended her PhD Thesis on 10 June 2019. Congrats!

Fatemeh Kalantarifard defended her PhD Thesis on 10 June 2019 in the Department of Physics Seminar Room SA-240 – Bilkent University.
Her Ph.D. Thesis Defense was live streamed on 10 June 2019 at 15:30 CEST in the Raven & Fox room.

Assoc. Prof. Ömer Ilday (UNAM, Bilkent University),  Assoc. Prof. Alpan Bek (Middle-East Technical University), Assist. Prof. Burcin Ünlü (Bogazici University), Dr. Seymour Jahangirov (UNAM), Prof. Oguz Gülseren (Bilkent University) and Assist. Prof. Giovanni Volpe (Bilkent University) will be the thesis committee members.

Thesis title: Intra-cavity optical trapping with fiber laser

Thesis abstract: Standard optical tweezers rely on optical forces arising when a focused laser beam interacts with a microscopic particle: scattering forces, pushing the particle along the beam direction, and gradient forces, attracting it towards the high-intensity focal spot. Importantly, the incoming laser beam is not affected by the particle position because the particle is outside the laser cavity. Here, we demonstrate that intra-cavity nonlinear feedback forces emerge when the particle is placed inside the optical cavity, resulting in orders-of-magnitude higher confinement along the three axes per unit laser intensity on the sample. This scheme allows trapping at very low numerical apertures and reduces the laser intensity to which the particle is exposed by two orders of magnitude compared to a standard 3D optical tweezers. These results are highly relevant for many applications requiring manipulation of samples that are subject to photodamage, such as in biophysics and nano-sciences.

Thesis Advisor  Giovanni Volpe, Department of Physics, Bilkent University

Place: Physics Department seminar room (SA240), Bilkent University
Time: 10 June, 2019, 16:30 TRT (Turkey Time)

Place: Meeting room Raven & Fox, Gothenburg University
Time: 10 June, 2019, 15:30 CEST


Martin Selin defended his Master Thesis. Congrats!

Martin Selin defended his Master thesis in Physics at Chalmers University of Technology on 5 June 2019

Title: Growing Artificial Neural Networks. Novel approaches to Deep Learning for Image Analysis and Particle Tracking

Deep-learning has recently emerged as one of the most successful methods for an- alyzing large amounts of data and constructing models from it. It has virtually revolutionized the field of image analysis and the algorithms are now being employed in research field outside of computer science. The methods do however suffer from several drawbacks such as large computational costs.

In this thesis alternative methods for training the networks underlying networks are evaluated based on gradually growing networks during training using layer-by- layer training as well as a method based on increasing network width dubbed breadth training.

These training methods lends themselves to easily implementing networks of tune- able size allowing for choice between high accuracy or fast execution or the construc- tion of modular network in which one can chose to execute only a small part of the network to get a very fast prediction at the cost of some accuracy. The layer-by-layer method is applied to multiple different image analysis tasks and the performance is evaluated and compared to that of regular training. Both the layer by layer training and the breadth training comparable to normal training in performance and in some cases slightly superior while in others slightly inferior. The modular nature of the networks make them suitable for applications within multi-particle tracking.

​Name of the master programme: MPPHYS – Physics
Supervisor: Giovanni Volpe, Department of Physics, University of Gothenburg
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Henry Yang, MP Complex Adaptive Systems, Department of Physics, Chalmers University of Technology

Place: Raven & Fox room
Time: 5 June, 2019, 15:00

Adrian Leidegren defended his Master Thesis. Congrats!

Adrian Leidegren defended his Master thesis in Physics at the University of Gothenburg on 5 June 2019

Title: Estimating the validity of synthetic data using neural network ensembles

The use of synthetic data has the potential to yield unlimited amounts of resources for use in training neural networks. This is however contin- gent on finding the right parameters to use with the data-generating system. As a worst-case scenario this would be done by careful guesswork. Herein is presented an alternative that has the potential to automate this work. The Deeptrack system for particle tracking in digital video microscopy was used as a framework, due to its ability to generate synthetic data from a handful of parameters. An ensemble was trained according to one set of parameter values and tested against a set of test data generated by the same parameters except for one, which was made to vary over a wide range. To contrast this, using a new parameter set, another set of test data was generated alongside several ensembles where one parameter was varied for each ensemble’s training data.

It was found that the limiting density of discreet points as a function of the vary- ing parameter had a local minimum around the region where the variable matched the same parameter’s value in the other data set, be it training or testing. This shows the possibility of using ensembles of neural networks to identify the most suitable parameter values in Deeptrack to ensure that the synthetic training data is represen- tative of the laboratory test data. There may also be a wider use case to this technique as a means of estimating confidence in the networks’ predictions.

​Name of the master programme: Physics
Supervisor: Saga Helgadottir, Department of Physics, University of Gothenburg
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg

Place: Faraday room
Time: 5 June, 2019, 15:00