Single-Molecule Localization Microscopy Reconstruction Using Noise2Noise for Super-Resolution Imaging of Actin Filaments
| Autor: | Joël Lefebvre, Bohdan Lewkow, Avelino Javer, Edward S. Allgeyer, Jens Rittscher, Daniel St Johnston, George Sirinakis, Mariia Dmitrieva |
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| Rok vydání: | 2020 |
| Předmět: |
Single molecule localization
Diffraction 0303 health sciences Artificial neural network Computer science business.industry Single-Molecule Localization Microscopy 02 engineering and technology Iterative reconstruction 021001 nanoscience & nanotechnology Superresolution Self-supervision 03 medical and health sciences Microscopy Image Reconstruction Computer vision Artificial intelligence 0210 nano-technology Representation (mathematics) business Actin 030304 developmental biology |
| Zdroj: | ISBI |
| Popis: | Single-molecule localization microscopy (SMLM) is a super-resolution imaging technique developed to image structures smaller than the diffraction limit. This modality results in sparse and non-uniform sets of localized blinks that need to be reconstructed to obtain a super-resolution representation of a tissue. In this paper, we explore the use of the Noise2Noise (N2N) paradigm to reconstruct the SMLM images. Noise2Noise is an image denoising technique where a neural network is trained with only pairs of noisy realizations of the data instead of using pairs of noisy/clean images, as performed with Noise2Clean (N2C). Here we have adapted Noise2Noise to the 2D SMLM reconstruction problem, exploring different pair creation strategies (fixed and dynamic). The approach was applied to synthetic data and to real 2D SMLM data of actin filaments. This revealed that N2N can achieve reconstruction performances close to the Noise2Clean training strategy, without having access to the super-resolution images. This could open the way to further improvement in SMLM acquisition speed and reconstruction performance. |
| Databáze: | OpenAIRE |
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