Deep learning-based single-shot autofocusing of microscopy images

Luzhe Huang; Yilin Luo; Yair Rivenson; Aydogan Ozcan

doi:10.1117/12.2580672

5 March 2021 Deep learning-based single-shot autofocusing of microscopy images

Luzhe Huang, Yilin Luo, Yair Rivenson, Aydogan Ozcan

Proceedings Volume 11647, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX; 116470Y (2021) https://doi.org/10.1117/12.2580672
Event: SPIE BiOS, 2021, Online Only

Abstract

We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, to rapidly and blindly autofocus a single-shot microscopy image captured at an arbitrary out-of-focus plane. Deep-R is experimentally validated using various tissue sections that were imaged with fluorescence and brightfield microscopes. Furthermore, snapshot autofocusing under different defocusing scenarios is demonstrated, including uniform axial-defocusing, sample tilting, cylindrical and spherical distortions within the field-of-view. Compared with other online autofocusing algorithms, Deep-R is significantly faster while having comparable image performance. Deep-R framework will enable high-throughput microscopic imaging over large fields-of-view, improving the overall imaging throughput, also reducing the photon dose on the sample.

Conference Presentation

Citation Download Citation

Luzhe Huang, Yilin Luo, Yair Rivenson, and Aydogan Ozcan "Deep learning-based single-shot autofocusing of microscopy images", Proc. SPIE 11647, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX, 116470Y (5 March 2021); https://doi.org/10.1117/12.2580672

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