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Hannah Heil |
Development of Machine-Learning Guided Super-Resolution Microscopy |
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Friday 8th of July - 16:55
Open technologies for Super-Resolution in BioImaging
Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires specialised methods to collect and process a large volume of recorded data and extract quantitative information.
In recent years, the Henriques Lab and collaborators have built an open-source ecosystem of computational, biochemical and optical approaches for live-cell super-resolution microscopy, designed to combine high performance and ease of use.
This talk will present some of these approaches such as SRRF, SQUIRREL and VirusMapper, and especially highlight our most recent contribution: eSRRF (enhanced-SRRF), which gains a significant improvement by refining the reconstruction principles of super-resolution radial fluctuation (SRRF) microscopy while providing a first-of-its-kind automated parameter optimisation tool.
Furthermore, we extended the eSRRF reconstruction algorithm to perform 3D super-resolution microscopy when combined with multifocus microscopy. With this approach, we realised live-cell friendly volumetric super- resolution imaging of live cells with acquisition speed on the order of 1 volume / second.
Find out more about Hannah's research
Coming from a background of semiconductor physics and nanofabrication switched over to the Biophysics and fluorescence microscopy field for my Master studies and my PhD. I love working in an interdisciplinary team to study cell biology by combining biophotonics and high-end microscopy. In the Henriques Lab I will establish automated and intelligent super-resolution microscopy to study host pathogen interactions.
