Novel fast laser-based auto-focusing microscope

Liu, Chien-Sheng; Hu, Pin-Hao; Wang, Yung-Hsing; Ke, Shun-Sheng; Lin, Yang-Cheng; Chang, Yu-Hsiu; Horng, Ji-Bin

doi:10.1109/icsens.2010.5690153

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…Conventionally, microscopic autofocusing is performed “online”, where the focus plane of each individual field-of-view (FOV) is found during the image acquisition process. Online autofocusing can be generally categorized into two groups: optical − and algorithmic methods. − Optical methods typically adopt additional distance sensors involving, for example, a near-infrared laser, − a light-emitting diode or an additional camera, − , that measure or calculate the relative sample distance needed for the correct focus. These optical methods require modifications to the optical imaging system, which are not always compatible with the existing microscope hardware .…”

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confidence: 99%

Single-Shot Autofocusing of Microscopy Images Using Deep Learning

et al. 2021

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Autofocusing is a critical step for high-quality microscopic imaging of specimens, especially for measurements that extend over time covering large fields-of-view. Autofocusing is generally practiced using two main approaches. Hardware-based optical autofocusing methods rely on additional distance sensors that are integrated with a microscopy system. Algorithmic autofocusing methods, on the other hand, regularly require axial scanning through the sample volume, leading to longer imaging times, which might also introduce phototoxicity and photobleaching on the sample. Here, 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. Focusing criterionAverage time (sec/mm 2 ) Standard deviation (sec/mm 2 ) Vollath4 39 42.91 3.16 Vollath5 39 39.57 3.16 Standard deviation 37.22 3.07 Normalized variance 10 36.50 0.36 Deep-R (CPU) 20.04 0.23 Deep-R (GPU) 2.98 0.08 Table. 1. Comparison of Deep-R computation time per 1 mm 2 of sample FOV (captured using a 20×/0.75NA objective lens) compared against other state-of-the-art autofocusing methods.

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confidence: 99%

Single-Shot Autofocusing of Microscopy Images Using Deep Learning

et al. 2021

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“…Finally, the use of a two-spot design is beneficial. In techniques that employ a single back-reflected beam, the shape of the spot is of utmost importance, as in most cases a circular spot is assumed [9,[12][13][14][15] and any distortions of this beam profile directly result in a focusing error. To overcome this delicate issue, computing the centroid or center-of-mass was proposed [16].…”

Section: Conceptual System Descriptionmentioning

confidence: 99%

A Modular Approach to Active Focus Stabilization for Fluorescence Microscopy

Berg¹,

Eynde²,

Müller

et al. 2020

Preprint

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Maintaining focus during time-lapse experiments remains an important challenge in optical microscopy. In this paper we present an automated focusing system for fluorescence microscopy that can be used in time-lapse experiments. We provide software for flexibly operating the autofocus system and a complete alignment guide. The system is evaluated using a time-lapse experiment with temperature variations on Tetraspeck beads.

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“…The use of low power near infrared radiation for the autofocus can significantly reduce the phototoxic light dose on the sample compared to software-based techniques using primary microscope images. Defocus can be quantified using a metric calculated from an image of this back-reflected laser beam on a secondary (autofocus) camera (such as displacement of images 6 or centroid 7 ). Such calculated metrics typically do not distinguish whether the defocus is positive or negative.…”

Section: Introductionmentioning

confidence: 99%

Robust, single-shot, optical autofocus system utilizing cylindrical lenses to provide high precision and long range of operation

Lightley

Kumar

García

et al. 2022

Preprint

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We present a robust, "real-time" optical autofocus system for microscopy that provides high accuracy (<230 nm) and long range (~130 μm) with a 1.4 numerical aperture oil immersion objective lens. This autofocus can operate in a closed loop, single-shot functionality over a range of ±37.5 μm and can also operate as a 2-step process up to ±68 μm. A real-time autofocus capability is useful for experiments with long image data acquisition times, including single molecule localization microscopy, that may be impacted by defocusing resulting from drift of components, e.g., due to changes in temperature or mechanical drift. It is also vital for automated slide scanning or multiwell plate imaging where the sample may not be in the same horizontal plane for every field of view during the image data acquisition. To realise high precision and long range, we implement orthogonal optical readouts using cylindrical lenses. We demonstrate the performance of this new optical autofocus system with automated multiwell plate imaging and single molecule localisation microscopy and illustrate the benefit of using a superluminescent diode as the autofocus light source.

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Novel fast laser-based auto-focusing microscope

Cited by 10 publications

References 15 publications

Single-Shot Autofocusing of Microscopy Images Using Deep Learning

Single-Shot Autofocusing of Microscopy Images Using Deep Learning

A Modular Approach to Active Focus Stabilization for Fluorescence Microscopy

Robust, single-shot, optical autofocus system utilizing cylindrical lenses to provide high precision and long range of operation

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