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4 March 2019 Deep-learning enables cross-modality super-resolution in fluorescence microscopy (Conference Presentation)
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Proceedings Volume 10937, Optical Data Science II; 109370G (2019)
Event: SPIE OPTO, 2019, San Francisco, California, United States
We present a deep learning-based framework for super-resolution image transformations across multiple fluorescence microscopy modalities. By training a neural network using a generative adversarial network (GAN), a single low-resolution image is transformed into a high-resolution image that surpasses the diffraction limit. The deep network’s output also demonstrates improved signal-to-noise ratio and extended depth-of-field. This framework is solely data-driven which means that it does not rely on any physical models of the imaging formation process, and instead learns a statistical transformation from the training image datasets. The inference process is non-iterative and does not require sweeping over parameters to achieve optimal results, in contrast to state-of-the-art deconvolution methods. The success of this framework is demonstrated by super-resolving wide-field images captured with low-numerical aperture objective-lenses to match the resolution of images captured with high-numerical aperture objectives. In another example, we demonstrate the transformation of confocal microscopy images into images that match the performance of stimulated emission depletion (STED) microscopy, by super-resolving the distributions of Histone 3 sites within cell nuclei. We also applied this framework to total-internal-reflection fluorescence (TIRF) microscopy and super-resolved TIRF images to match the resolution of TIRF-based structured illumination microscopy (TIRF-SIM). Our super-resolved TIRF images/movies reveal endocytic protein dynamics in SUM159 cells and amnioserosa tissues of a Drosophila embryo, providing a very good match to TIRF-SIM images/movies of the same samples. Our experimental results demonstrate that the presented data-driven super resolution approach generalizes to new types of images and super-resolves objects that were not present in the training stage.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hongda Wang, Yair Rivenson, Yiyin Jin, Zhensong Wei, Ronald Gao, Harun Günaydın, Laurent A. Bentolila, Comert Kural, and Aydogan Ozcan "Deep-learning enables cross-modality super-resolution in fluorescence microscopy (Conference Presentation)", Proc. SPIE 10937, Optical Data Science II, 109370G (4 March 2019);

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