Deep learning-based super-resolution fluorescence microscopy on small datasets

Varun Mannam; Yide Zhang; Xiaotong Yuan; Scott Howard

doi:10.1117/12.2578519

5 March 2021 Deep learning-based super-resolution fluorescence microscopy on small datasets

Varun Mannam, Yide Zhang, Xiaotong Yuan, Scott Howard

Proceedings Volume 11650, Single Molecule Spectroscopy and Superresolution Imaging XIV; 116500O (2021) https://doi.org/10.1117/12.2578519
Event: SPIE BiOS, 2021, Online Only

Abstract

Fluorescence microscopy has enabled a dramatic development in modern biology by visualizing biological organ- isms with micrometer scale resolution. However, due to the diffraction limit, sub-micron/nanometer features are difficult to resolve. While various super-resolution techniques are developed to achieve nanometer-scale resolu- tion, they often either require expensive optical setup or specialized fluorophores. In recent years, deep learning has shown potentials to reduce the technical barrier and obtain super-resolution from diffraction-limited images. For accurate results, conventional deep learning techniques require thousands of images as a training dataset. Obtaining large datasets from biological samples is not often feasible due to photobleaching of fluorophores, phototoxicity, and dynamic processes occurring within the organism. Therefore, achieving deep learning-based super-resolution using small datasets is challenging. We address this limitation with a new convolutional neural network based approach that is successfully trained with small datasets and achieves super-resolution images. We captured 750 images in total from 15 different field-of-views as the training dataset to demonstrate the technique. In each FOV, a single target image is generated using the super-resolution radial fluctuation method. As expected, this small dataset failed to produce a usable model using traditional super-resolution architecture. However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset. This deep learning model can be applied to other biomedical imaging modalities such as MRI and X-ray imaging, where obtaining large training datasets is challenging.

Conference Presentation

Citation Download Citation

Varun Mannam, Yide Zhang, Xiaotong Yuan, and Scott Howard "Deep learning-based super-resolution fluorescence microscopy on small datasets", Proc. SPIE 11650, Single Molecule Spectroscopy and Superresolution Imaging XIV, 116500O (5 March 2021); https://doi.org/10.1117/12.2578519

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