Tag: Benjamin Midtvedt

Soft Matter Lab presentations at the SPIE Optics+Photonics Digital Forum

Seven members of the Soft Matter Lab (Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Laura Pérez-Garcia, Daniel Midtvedt, Harshith Bachimanchi, Emiliano Gómez) were selected for oral and poster presentations at the SPIE Optics+Photonics Digital Forum, August 24-28, 2020.

The SPIE digital forum is a free, online only event.
The registration for the Digital Forum includes access to all presentations and proceedings.

The Soft Matter Lab contributions are part of the SPIE Nanoscience + Engineering conferences, namely the conference on Emerging Topics in Artificial Intelligence 2020 and the conference on Optical Trapping and Optical Micromanipulation XVII.

The contributions being presented are listed below, including also the presentations co-authored by Giovanni Volpe.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Emerging Topics in Artificial Intelligence 2020

Saga Helgadottir
Digital video microscopy with deep learning (Invited Paper)
26 August 2020, 10:30 AM
SPIE Link: here.

Aykut Argun
Calibration of force fields using recurrent neural networks
26 August 2020, 8:30 AM
SPIE Link: here.

Laura Pérez-García
Deep-learning enhanced light-sheet microscopy
25 August 2020, 9:10 AM
SPIE Link: here.

Daniel Midtvedt
Holographic characterization of subwavelength particles enhanced by deep learning
24 August 2020, 2:40 PM
SPIE Link: here.

Benjamin Midtvedt
DeepTrack: A comprehensive deep learning framework for digital microscopy
26 August 2020, 11:40 AM
SPIE Link: here.

Gorka Muñoz-Gil
The anomalous diffusion challenge: Single trajectory characterisation as a competition
26 August 2020, 12:00 PM
SPIE Link: here.

Meera Srikrishna
Brain tissue segmentation using U-Nets in cranial CT scans
25 August 2020, 2:00 PM
SPIE Link: here.

Juan S. Sierra
Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks
26 August 2020, 11:50 AM
SPIE Link: here.

Harshith Bachimanchi
Digital holographic microscopy driven by deep learning: A study on marine planktons (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Emiliano Gómez
BRAPH 2.0: Software for the analysis of brain connectivity with graph theory (Poster)
24 August 2020, 5:30 PM
SPIE Link: here.

Optical Trapping and Optical Micromanipulation XVII

Laura Pérez-García
Reconstructing complex force fields with optical tweezers
24 August 2020, 5:00 PM
SPIE Link: here.

Alejandro V. Arzola
Direct visualization of the spin-orbit angular momentum conversion in optical trapping
25 August 2020, 10:40 AM
SPIE Link: here.

Isaac Lenton
Illuminating the complex behaviour of particles in optical traps with machine learning
26 August 2020, 9:10 AM
SPIE Link: here.

Fatemeh Kalantarifard
Optical trapping of microparticles and yeast cells at ultra-low intensity by intracavity nonlinear feedback forces
24 August 2020, 11:10 AM
SPIE Link: here.

Note: the presentation times are indicated according to PDT (Pacific Daylight Time) (GMT-7)

Benjamin Midtvedt joins the Soft Matter Lab

Benjamin Midtvedt starts his PhD at the Physics Department of the University of Gothenburg on 1st July 2020.

Benjamin has a Master degree in Engineering Mathematics and Computer Science at Chalmers University of Technology.

In his PhD, he will focus on using deep learning to design particle behaviour when interacting with light.

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

Frida Brogren, Kirill Danilov, Klas Holmgren, Oskar Leinonen, Benjamin Midtvedt & Arian Rohani defended their Bachelor Thesis. Congrats!

Frida Brogren, Kirill Danilov, Klas Holmgren, Oskar Leinonen, Benjamin Midtvedt & Arian Rohani defended their Bachelor Thesis at Chambers University of Technology on 25 May 2018.

Title: Experimentell studie av kritiska fenomen med optiska pincetter

Abstract: I samband med nanoteknologins framfart ses ett växande intresse för kolloida sy- stem för att överkomma många svårigheter med konstruktionen av nanostrukturer. På grund av kritikalitetens skalinvarianta egenskaper kan kolloider användas som analo- ger för nanopartiklar i studier av kritiska fenomen. Detta arbete ämnar att undersöka och utvidga förståelsen av kritiska fluktuationer och kritiska Casimirkrafter, som kan användas för att binda och styra kolloider. En optisk pincett byggdes för att undersö- ka kritisk motorisering och kolloida aggregationer, medan en färdigbyggd holografisk pincett användes för att mäta kritiska Casimirkrafter. De motoriserade kolloiderna uppvisade mer kaotisk rörelse för högre lasereffekter, och de kritiska Casimirkrafterna visades växa skarpt i närheten av den kritiska temperaturen.

Supervisors: Alessandro Magazzù & Giovanni Volpe, Department of Physics, University of Gothenburg
Examiner: Lena Falk, Department of Physics, University of Gothenburg
Opponent: Markus Fällman, Gabriella Grenander, Oskar Holmstedt, Viktor Olsson, Maria Söderberg & Wilhelm Tranheden
Place: FL62
Time: 25 May, 2018, 11:05-11:50