Autofocusing of microscopy images using deep learning  January 25, 2021
Autofocusing is a critical step for high-quality microscopic imaging of specimens, especially for measurements that extend over time covering large fields of view. Hardware-based optical autofocusing methods rely on additional distance sensors that are integrated with a microscopy system; Algorithmic autofocusing methods require axial scanning through the sample volume, leading to longer imaging times, which might also introduce phototoxicity and photobleaching on the sample. Researchers at UCLA have demonstrated 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. They illustrated the efficacy of Deep-R using various tissue sections that were imaged using fluorescence and brightfield microscopy modalities and demonstrated snapshot autofocusing under different scenarios. The results reveal that Deep-R is significantly faster when compared with standard online algorithmic autofocusing methods it opens new opportunities for rapid microscopic imaging of large sample areas, also reducing the photon dose on the sample…read more. TECHNICAL ARTICLE¬†

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