Single-shot autofocusing of microscopy images using deep learning

• URL: http://arxiv.org/abs/2003.09585v2
• Date: Fri, 22 Jan 2021 06:20:14 GMT
• Title: Single-shot autofocusing of microscopy images using deep learning
• Authors: Yilin Luo, Luzhe Huang, Yair Rivenson, Aydogan Ozcan
• Abstract summary: Deep learning-based offline autofocusing method, termed Deep-R, is trained to rapidly and blindly autofocus a single-shot microscopy image. Deep-R is significantly faster when compared with standard online algorithmic autofocusing methods.
• Score: 0.30586855806896046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that is acquired at an arbitrary out-of-focus plane. We illustrate the efficacy of Deep-R using various tissue sections that were imaged using fluorescence and brightfield microscopy modalities and demonstrate snapshot autofocusing under different scenarios, such as a uniform axial defocus as well as a sample tilt within the field-of-view. Our results reveal that Deep-R is significantly faster when compared with standard online algorithmic autofocusing methods. This deep learning-based blind autofocusing framework opens up new opportunities for rapid microscopic imaging of large sample areas, also reducing the photon dose on the sample.

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