Agenda

14:00-14:45 Pawel Sikorski: Nanotechnology meets bioengineering with some examples from nano-,  microfabrications and biomaterials.

14:45:15:15 Coffee Break

15:15-16:00 Giovanni Volpe: Deep Learning for Imaging and Microscopy

Speaker information and abstracts

Pawel Sikorski, Professor at the Department of Physics, Norwegian University of Science and Technology (NTNU)

Nanotechnology meets bioengineering with some examples from nano-,  microfabrications and biomaterials.

In this presentation, I aim to highlight connections between bioengineering, biomaterials, and nanotechnology. I will introduce nanoscale effects that can be important for bioengineering research and technology development. Nanotechnology has the potential to contribute to biomedical research by providing new tools and new experimental methods. Compared to traditional approaches, these techniques often allow for miniaturization and better control of the experimental system. In our research, we are interested in the fabrication of nanostructured and microstructured surfaces that are easy and inexpensive to make,  and that are compatible with typical workflows in biomedical research. I will describe two different fabrication approaches and how they are used to study biological systems.
 

Giovanni Volpe, Professor at the Department of Physics, University of Gothenburg

Deep Learning for Imaging and Microscopy

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 have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.

Recommended reading

https://pubs.aip.org/aip/apr/article/8/1/011310/238663/Quantitative-digital-microscopy-with-deep-learning
(open access so everyone can download the fulltext in pdf)

Extra note for students enrolled in the Nanoscience Breadth Course: the talk by Giovanni Volpe is included in this course and the enrolled students will be informed separately by Jonas Johansson.