Wavefront-sensing-based autofocusing in microscopy

Xu, Jing; Tian, Xiaolin; Meng, Xin; Kong, Yan; Gao, Shumei; Cui, Haoyang; Liu, Fei; Li, Xue; Liu, Cheng; Wang, Shouyu

doi:10.1117/1.jbo.22.8.086012

Cited by 12 publications

(7 citation statements)

References 44 publications

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“…Automation in the medicine field leads to faster, cheaper, and more accurate results, specifically by using digital imaging in the digital pathology area. Automation and digitalization of diagnostic procedures reduce the acquisition and processing times and improve accuracy, throughput, and reproducibility of the measurements (Saerens et al, 2019; Xu et al, 2017). In addition, it allows sending the acquired digital images to experts all over the world for consults and automatically storing patient records (Liao, 2018).…”

Section: Introductionmentioning

confidence: 99%

An algorithm selection methodology for automated focusing in optical microscopy

Sanz

Machado

Borromeo

2021

Microscopy Res & Technique

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Autofocus systems are essential in optical microscopy. These systems typically sweep the sample through the focal range and apply an algorithm to determine the contrast value of each image, where the highest value indicates the optimal focus position. As the optimal algorithm may vary according to the images' content, we evaluate the 15 most used algorithms in the field using 150 stacks of images from four different kinds of tissue. We use four measuring criteria and two types of analysis and propose a general methodology to apply to select the best fitting algorithm for any given application. In this paper, we present the results of this evaluation and a detailed discussion of different features: the threshold used for the algorithms, the criteria parameters, the analysis used, the bit depth of the images, their magnification, and the type of tissue, reaching the conclusion that some of these parameters are more relevant to the study than others, and the implementation of the proposed methodology can lead to a fast and reliable autofocus system capable of performing an analysis and selection of algorithms with no supervision required.

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Section: Introductionmentioning

confidence: 99%

An algorithm selection methodology for automated focusing in optical microscopy

Sanz

Machado

Borromeo

2021

Microscopy Res & Technique

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“…This process is naturally time-consuming, does not support high frame-rate imaging of dynamic specimen and increases the probability of sample photobleaching, photodamage, or phototoxicity . As an alternative, wavefront sensing-based autofocusing techniques − also lie at the intersection of optical and algorithmic methods. However, multiple image capture is still required, and therefore, these methods also suffer from similar problems as the other algorithmic autofocusing methods face.…”

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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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“…For example, two focus functions that performed well in approaching these desired qualities were a differentiation based method, such as the squared gradient (SG), and a Laplacian (LA) focus method: where g is the gray scaled image value measured as a function of spatial position, x and y . Faster wavefront sensing autofocusing algorithms have also optimized for the Tamura coefficient (TC) , defined as where σ( g ) is the standard deviation of the grayscale intensity, and g is the average grayscale image intensity. Reported DHM focus methods have also maximized image sharpness using the weighted spectral analysis (SPEC) and cumulated edge detection by gradient calculation (GRA): where F B ( g )( u , v ) is the band-pass filtered Fourier transform of the grayscale image, g , summed over the Fourier domain coordinates, u , v .…”

Section: Solving the Fundamental Problem Of Quantitative Phasementioning

confidence: 99%

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine

et al. 2022

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Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.

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Wavefront-sensing-based autofocusing in microscopy

Cited by 12 publications

References 44 publications

An algorithm selection methodology for automated focusing in optical microscopy

An algorithm selection methodology for automated focusing in optical microscopy

Single-Shot Autofocusing of Microscopy Images Using Deep Learning

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine

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