TY  - THES
A1  - Rahm, Johanna Viola
T1  - Exploring the application of deep learning for super-resolution microscopy
N2  - Fluorescence microscopy has significantly impacted our understanding of cell biology. The extension of diffraction-unlimited super-resolution microscopy opened an observation window that allows for the scrutiny of cellular organization at a molecular level. The non-invasive nature of visible light in super-resolution microscopy methods renders them suitable for observations in living cells and organisms. Building upon these advancements, a promising synergy between super-resolution fluorescence microscopy and deep learning becomes evident, extending the capabilities of the imaging methods. Tasks such as image modality translation, restoration, single-molecule fitting, virtual labeling, spectral demixing, and molecular counting, are enabled with high precision. The techniques explored in this thesis address three critical facets in advanced microscopy, namely the reduction in image acquisition time, saving photon budget during measurement, and increasing the multiplexing capability. Furthermore, descriptors of protein distributions and their motion on cell membranes were developed.
KW  - deep learning
KW  - super-resolution microscopy
KW  - denoising
KW  - virtual labeling
KW  - receptor tyrosine kinases
Y1  - 2024
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/85965
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-859658
CY  - Frankfurt am Main
ER  -