Quantitative Digital Microscopy with Deep Learning
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe
Applied Physics Reviews 8, 011310 (2021)
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
FORMA: expanding applications of optical tweezers
Laura Pérez García
Invited talk at SPIE Photonics West OPTO
6 March 2021
In this presentation, Laura Pérez will talk about FORMA (force reconstruction via maximum-likelihood-estimator analysis) which addresses the need of measuring the force fields acting on microscopic particles. Compared to alternative established methods, FORMA is faster, simpler, more accurate, and more precise. Furthermore, FORMA can also measure non-conservative and out-of-equilibrium force fields. Here, after a brief introduction to FORMA, I will present its use, advantages, and limitations. I will conclude with some recent work where we exploit Bayesian inference to expand the scope of application of FORMA.
Laura Pérez García, Jaime Donlucas Pérez, Giorgio Volpe, Alejandro V. Arzola & Giovanni Volpe, High-Performance Reconstruction of Microscopic Force Fields from Brownian Trajectories, Nature Communications 9, 5166 (2018)
Optical trapping and critical Casimir forces
Agnese Callegari, Alessandro Magazzù, Andrea Gambassi & Giovanni Volpe
The European Physical Journal Plus (EPJP), 136, 213 (2021)
Critical Casimir forces emerge between objects, such as colloidal particles, whenever their surfaces spatially confine the fluctuations of the order parameter of a critical liquid used as a solvent. These forces act at short but microscopically large distances between these objects, reaching often hundreds of nanometers. Keeping colloids at such distances is a major experimental challenge, which can be addressed by the means of optical tweezers. Here, we review how optical tweezers have been successfully used to quantitatively study critical Casimir forces acting on particles in suspensions. As we will see, the use of optical tweezers to experimentally study critical Casimir forces can play a crucial role in developing nano-technologies, representing an innovative way to realize self-assembled devices at the nano- and microscale.
Nils Jacobson defended his Master thesis in MPCAS at the Chalmers University of Technology on 16 February 2021. Congrats!
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
QED Casimir vs Critical Casimir Forces: Trapping and Releasing of metal flake particles
Falko Schmidt, Agnese Callegari, Giovanni Volpe
(online at) MPI Stuttgart, Germany
11 February 2021, 14:30-16.00
We propose a mechanism for restoration of collapsed structures using critical Casimir forces by investigating the diffusion of metal flake-like particles. By tuning temperature near-criticality and employing selective self-assembled monolayers the resulting repulsive critical Casimir force is large enough to lift off particle and enable transitions previously impeded by QED Casimir attraction.
Intercellular communication induces glycolytic synchronization waves between individually oscillating cells Martin Mojica-Benavides, David D. van Niekerk, Mite Mijalkov, Jacky L. Snoep, Bernhard Mehlig, Giovanni Volpe, Caroline B. Adiels & Mattias Goksör
PNAS 118(6), e2010075118 (2021)
Metabolic oscillations in single cells underlie the mechanisms behind cell synchronization and cell-cell communication. For example, glycolytic oscillations mediated by biochemical communication between cells may synchronize the pulsatile insulin secretion by pancreatic tissue, and a link between glycolytic synchronization anomalies and type-2 diabetes has been hypotesized. Cultures of yeast cells have provided an ideal model system to study synchronization and propagation waves of glycolytic oscillations in large populations. However, the mechanism by which synchronization occurs at individual cell-cell level and overcome local chemical concentrations and heterogenic biological clocks, is still an open question because of experimental limitations in sensitive and specific handling of single cells. Here, we show how the coupling of intercellular diffusion with the phase regulation of individual oscillating cells induce glycolytic synchronization waves. We directly measure the single-cell metabolic responses from yeast cells in a microfluidic environment and characterize a discretized cell-cell communication using graph theory. We corroborate our findings with simulations based on a kinetic detailed model for individual yeast cells. These findings can provide insight into the roles cellular synchronization play in biomedical applications, such as insulin secretion regulation at the cellular level.
Optical Manipulation encompasses all areas of manipulation and measurement using light, from optical manipulation of microparticles to photoactivated materials and optogenetics, emphasizing new and developing application areas in biophysics and biomedicine.
The categories of topics in the conference are Optical Manipulation in Biophysics and Biomedicine, Optical Manipulation Fundamentals, Optical Manipulation Applications, and Alternative Manipulation Techniques.
The conference will be held online 12-16 April 2021.