Wavefront-sensing-based autofocusing in microscopy

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine

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.

An algorithm selection methodology for automated focusing in optical microscopy

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.

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.

Extended range and aberration-free autofocusing via remote focusing and sequence-dependent learning

Rapid autofocusing over long distances is critical for tracking 3D topological variations and sample motion in real time. Taking advantage of a deformable mirror and Shack-Hartmann wavefront sensor, remote focusing can permit fast axial scanning with simultaneous correction of system-induced aberrations. Here, we report an autofocusing technique that combines remote focusing with sequence-dependent learning via a bidirectional long short term memory network. A 120 µm autofocusing range was achieved in a compact reflectance confocal microscope both in air and in refractive-index-mismatched media, with similar performance under arbitrary-thickness liquid layers up to 1 mm. The technique was validated on sample types not used for network training, as well as for tracking of continuous axial motion. These results demonstrate that the proposed technique is suitable for real-time aberration-free autofocusing over a large axial range, and provides unique advantages for biomedical, holographic and other related applications.

Acceleration of autofocusing with improved edge extraction using structure tensor and Schatten norm

Determining the optimal focal plane amongst a stack of blurred images in a short response time is a non-trivial task in optical imaging like microscopy and photography. An autofocusing algorithm, or in other words, a focus metric, is key to effectively dealing with such problem. In previous work, we proposed a structure tensor-based autofocusing algorithm for coherent imaging, i.e., digital holography. In this paper, we further extend the realm of this method in more imaging modalities. With an optimized computation scheme of structure tensor, a significant acceleration of about fivefold in computation speed without sacrificing the autofocusing accuracy is achieved by using the Schatten matrix norm instead of the vector norm. Besides, we also demonstrate its edge extraction capability by retrieving the intermediate tensor image. Synthesized and experimental data acquired in various imaging scenarios such as incoherent microscopy and photography are demonstrated to verify the efficacy of this method.

Measurements on ATP induced cellular fluctuations using real-time dual view transport of intensity phase microscopy

Dual view transport of intensity phase microscopy is adopted to quantitatively study the regulation of adenosine triphosphate (ATP) on cellular mechanics. It extracts cell phases in real time from simultaneously captured under- and over-focus images. By computing the root-mean-square phase and correlation time, it is found that the cellular fluctuation amplitude and speed increased with ATP compared to those with ATP depletion. Besides, when adenylyl-imidodiphosphate (AMP-PNP) was introduced, it competed with ATP to bind to the ATP binding site, and the cellular fluctuation amplitude and speed decreased. The results prove that ATP is a factor in the regulation of cellular mechanics. To our best knowledge, it is the first time that the dual view transport of intensity phase microscopy was used for live cell phase imaging and analysis. Our work not only provides direct measurements on cellular fluctuations to study ATP regulation on cellular mechanics, but it also proves that our proposed dual view transport of intensity phase microscopy can be well used, especially in quantitative phase imaging of live cells in biological and medical applications.