A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

Roadmap on digital holography [Invited]

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

Predictive search algorithm for phase holography

We present an algorithm for generating high-quality holograms for computer generated holography: holographic predictive search. This approach is presented as an alternative to traditional holographic search algorithms such as direct search (DS) and simulated annealing (SA). We first introduce the current search-based methods and then introduce an analytical model of the underlying Fourier elements. This is used to make prescient judgments regarding the next iteration of the algorithm. This approach is developed for the case of phase-modulating devices with phase-sensitive reconstructions. When compared to conventional iterative approaches such as DS and SA on a multiphase device, holographic predictive search offered a fivefold improvement in quality as well as up to a 10-fold improvement in convergence time. This comes at the cost of an increased iteration overhead.

Improving holographic search algorithms using sorted pixel selection

Traditional search algorithms for computer hologram generation such as Direct Search and Simulated Annealing offer some of the best hologram qualities at convergence when compared to rival approaches. Their slow generation times and high processing power requirements mean, however, that they see little use in performance critical applications. This paper presents the novel sorted pixel selection (SPS) modification for holographic search algorithms that offers mean square error reductions in the range of 14.7-19.2% for the test images used. SPS operates by substituting a weighted search selection procedure for traditional random pixel selection processes. While small, the improvements seen are observed consistently across a wide range of test cases and require limited overhead for implementation.

Adaptive frequency filtering based on convolutional neural networks in off-axis digital holographic microscopy

Digital holographic microscopy (DHM) as a label-free quantitative imaging tool has been widely used to investigate the morphology of living cells dynamically. In the off-axis DHM, the spatial filtering in the frequency spectrum of the hologram is vital to the quality of the reconstructed images. In this paper, we propose an adaptive spatial filtering approach based on convolutional neural networks (CNN) to automatically extracts the optimal shape of frequency components. For achieving robust and precise recognition performance, the net model is trained by using the tens of thousands of frequency spectrums with a variety of specimens and imaging conditions. The experimental results demonstrate that the trained network produce an adaptive spatial filtering window which can accurately select the frequency components of the object term and eliminate the frequency components of the interference terms, especially the coherent noise that overlaps with the object term in the spatial frequency domain. We find that the proposed approach has a fast, robust, and outstanding frequency filtering capability without any manual intervention and initial input parameters compared to previous techniques. Furthermore, the applicability of the proposed method in off-axis DHM for dynamic analysis is demonstrated by real-time monitoring the morphologic changes of living MLO-Y4 cells that are constantly subject to Fluid Shear Stress (FSS).

Sample and substrate preparation for exploring living neurons in culture with quantitative-phase imaging

Quantitative-phase imaging (QPI) has recently emerged as a powerful new quantitative microscopy technique suitable for the noninvasive exploration of the structure and dynamics of transparent specimens, including living cells in culture. Indeed, the quantitative-phase signal (QPS), induced by transparent living cells, can be detected with a nanometric axial sensitivity, and contains a wealth of information about both cell morphology and content. However, QPS is also sensitive to various sources of experimental noise. In this chapter, we emphasize how to properly and specifically measure the cell-mediated QPS in a wet-lab environment, when measuring with a digital holographic microscope (DHM). First, we present the substrate-requisite characteristics for properly achieving such cell-mediated QPS measurements at single-cell level. Then, we describe how quantitative-phase digital holographic microscopy (QP-DHM) can be used to numerically process holograms and subsequently reshape wavefronts in association with post-processing algorithms, thereby allowing for highly stable QPS obtainable over extended periods of time. Such stable QPS is a prerequisite for exploring the dynamics of specific cellular processes. We also describe experimental procedures that make it possible to extract important biophysical cellular parameters from QPS including absolute cell volume, transmembrane water permeability, and the movements of water in and out of the cell. To illustrate how QP-DHM can reveal the dynamics of specific cellular processes, we show how the monitoring of transmembrane water movements can be used to resolve the neuronal network dynamics at single-cell level. This is possible because QPS can measure the activity of electroneutral cotransports, including NKCC1 and KCC2, during a neuronal firing mediated by glutamate, the main excitatory neurotransmitter in the brain. Finally, we added a supplemental section, with more technical details, for readers who are interested in troubleshooting live-cell QP-DHM.

Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy

Automated label-free quantitative imaging of biological samples can greatly benefit high throughput diseases diagnosis. Digital holographic microscopy (DHM) is a powerful quantitative label-free imaging tool that retrieves structural details of cellular samples non-invasively. In off-axis DHM, a proper spatial filtering window in Fourier space is crucial to the quality of reconstructed phase image. Here we describe a region-recognition approach that combines shape recognition with an iterative thresholding method to extracts the optimal shape of frequency components. The region recognition technique offers fully automated adaptive filtering that can operate with a variety of samples and imaging conditions. When imaging through optically scattering biological hydrogel matrix, the technique surpasses previous histogram thresholding techniques without requiring any manual intervention. Finally, we automate the extraction of the statistical difference of optical height between malaria parasite infected and uninfected red blood cells. The method described here paves way to greater autonomy in automated DHM imaging for imaging live cell in thick cell cultures.

Nanomechanics of a fibroblast suspended using point-like anchors reveal cytoskeleton formation

Cells are suspended and stretched using two microbeads. The formation of inner cytoskeleton structures is reported using displacement, QPM phase change and fluorescent micrographs.

Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders

Quantitative phase microscopy (QPM) has recently emerged as a new powerful quantitative imaging technique well suited to noninvasively explore a transparent specimen with a nanometric axial sensitivity. In this review, we expose the recent developments of quantitative phase-digital holographic microscopy (QP-DHM). Quantitative phase-digital holographic microscopy (QP-DHM) represents an important and efficient quantitative phase method to explore cell structure and dynamics. In a second part, the most relevant QPM applications in the field of cell biology are summarized. A particular emphasis is placed on the original biological information, which can be derived from the quantitative phase signal. In a third part, recent applications obtained, with QP-DHM in the field of cellular neuroscience, namely the possibility to optically resolve neuronal network activity and spine dynamics, are presented. Furthermore, potential applications of QPM related to psychiatry through the identification of new and original cell biomarkers that, when combined with a range of other biomarkers, could significantly contribute to the determination of high risk developmental trajectories for psychiatric disorders, are discussed.

Recent advances in digital holography [invited]

This article presents an overview of recent advances in the field of digital holography, ranging from holographic techniques designed to increase the resolution of microscopic images, holographic imaging using incoherent illumination, phase retrieval with incoherent illumination, imaging of occluded objects, and the holographic recording of depth-extended objects using a frequency-comb laser, to the design of an infrastructure for remote laboratories for digital-holographic microscopy and metrology. The paper refers to current trends in digital holography and explains them using new results that were recently achieved at the Institute for Applied Optics of the University Stuttgart.

3D morphometry of red blood cells by digital holography

Three dimensional (3D) morphometric analysis of flowing and not-adherent cells is an important aspect for diagnostic purposes. However, diagnostics tools need to be quantitative, label-free and, as much as possible, accurate. Recently, a simple holographic approach, based on shape from silhouette algorithm, has been demonstrated for accurate calculation of cells biovolume and displaying their 3D shapes. Such approach has been adopted in combination with holographic optical tweezers and successfully applied to cells with convex shape. Nevertheless, unfortunately, the method fails in case of specimen with concave surfaces. Here, we propose an effective approach to achieve correct 3D shape measurement that can be extended in case of cells having concave surfaces, thus overcoming the limit of the previous technique. We prove the new procedure for healthy red blood cells (RBCs) (i.e., discocytes) having a concave surface in their central region. Comparative analysis of experimental results with a theoretical 3D geometrical model of RBC is discussed in order to evaluate accuracy of the proposed approach. Finally, we show that the method can be also useful to classify, in terms of morphology, different varieties of RBCs.

Digital holographic microscopy with coupled optical fiber trap for cell measurement and manipulation

We present an integrated optical system for three-dimensional (3D) imaging of micrometer-sized samples, while immobilizing and manipulating the samples by means of an optical fiber trap. Optical traps allow us to apply and measure pico-Newton-sized forces, and perform detailed measurements of micrometer-sized dielectric systems in the field of biology. The integrated 3D system can be used as a major tool in the field of biophysics. The trap is built using a tapered optical fiber to enhance the effective numerical aperture of the fiber. The trapping system is mounted on a conventional microscope, in which the two eyepieces' output ports are used as the paths of an off-axis self-referencing digital holographic microscopy (DHM) setup. The trap is calibrated using a high-speed camera, and trap stiffness is determined through the power spectrum method. The compact setup provides an elegant apparatus for temporally stable DHM for 3D imaging of optically controlled samples. Three-dimensional information and quantitative phase contrast images of the trapped samples are obtained by postprocessing the recorded digital holograms. Experiments were performed on lipids and red blood cells. Quantitative phase contrast images and temporal evolution of optical thickness of trapped samples are presented.

Exploring neural cell dynamics with digital holographic microscopy

In this review, we summarize how the new concept of digital optics applied to the field of holographic microscopy has allowed the development of a reliable and flexible digital holographic quantitative phase microscopy (DH-QPM) technique at the nanoscale particularly suitable for cell imaging. Particular emphasis is placed on the original biological information provided by the quantitative phase signal. We present the most relevant DH-QPM applications in the field of cell biology, including automated cell counts, recognition, classification, three-dimensional tracking, discrimination between physiological and pathophysiological states, and the study of cell membrane fluctuations at the nanoscale. In the last part, original results show how DH-QPM can address two important issues in the field of neurobiology, namely, multiple-site optical recording of neuronal activity and noninvasive visualization of dendritic spine dynamics resulting from a full digital holographic microscopy tomographic approach.

Twin-beams digital holography for 3D tracking and quantitative phase-contrast microscopy in microfluidics

We report on a compact twin-beam interferometer that can be adopted as a flexible diagnostic tool in microfluidic platforms with twofold functionality. The novel configuration allows 3D tracking of micro-particles and, at same time, can simultaneously furnish Quantitative Phase-contrast maps of tracked micro-objects by interference microscopy, without changing the configuration. Experimental demonstration is given on for in vitro cells in a microfluidic environment.

Driving and analysis of micro-objects by digital holographic microscope in microfluidics

We propose an optical configuration in which floating particles in a microfluidic chamber can be characterized by an interference microscopy configuration to obtain quantitative phase-contrast maps. The configuration is simply made by two laser beams from the same laser source. One beam provides the optical forces for driving the particle along appropriate paths, but at same time works as the object illumination beam in the holographic microscope. The second beam plays the role of the reference beam, allowing recording of an interference fringe pattern (i.e., the digital hologram) in an out-of-focus image plane. The system and method are illustrated and experimental results are offered for polymeric particles as well as for in vitro cells with the aim to demonstrate the approach.