Bichromatic tetraphasic full-field optical coherence microscopy.

Significance: Full-field optical coherence microscopy (FF-OCM) is a prevalent technique for backscattering and phase imaging with epi-detection. Traditional methods have two limitations: suboptimal utilization of functional information about the sample and complicated optical design with several moving parts for phase contrast. Aim: We report an OCM setup capable of generating dynamic intensity, phase, and pseudo-spectroscopic contrast with single-shot full-field video-rate imaging called bichromatic tetraphasic (BiTe) full-field OCM with no moving parts. Approach: BiTe OCM resourcefully uses the phase-shifting properties of anti-reflection (AR) coatings outside the rated bandwidths to create four unique phase shifts, which are detected with two emission filters for spectroscopic contrast. Results: BiTe OCM overcomes the disadvantages of previous FF-OCM setup techniques by capturing both the intensity and phase profiles without any artifacts or speckle noise for imaging scattering samples in three-dimensional (3D). BiTe OCM also utilizes the raw data effectively to generate three complementary contrasts: intensity, phase, and color. We demonstrate BiTe OCM to observe cellular dynamics, image live, and moving micro-animals in 3D, capture the spectroscopic hemodynamics of scattering tissues along with dynamic intensity and phase profiles, and image the microstructure of fall foliage with two different colors. Conclusions: BiTe OCM can maximize the information efficiency of FF-OCM while maintaining overall simplicity in design for quantitative, dynamic, and spectroscopic characterization of biological samples.

Quantitative phase imaging by gradient retardance optical microscopy.

Quantitative phase imaging (QPI) has become a vital tool in bioimaging, offering precise measurements of wavefront distortion and, thus, of key cellular metabolism metrics, such as dry mass and density. However, only a few QPI applications have been demonstrated in optically thick specimens, where scattering increases background and reduces contrast. Building upon the concept of structured illumination interferometry, we introduce Gradient Retardance Optical Microscopy (GROM) for QPI of both thin and thick samples. GROM transforms any standard Differential Interference Contrast (DIC) microscope into a QPI platform by incorporating a liquid crystal retarder into the illumination path, enabling independent phase-shifting of the DIC microscope's sheared beams. GROM greatly simplifies related configurations, reduces costs, and eradicates energy losses in parallel imaging modalities, such as fluorescence. We successfully tested GROM on a diverse range of specimens, from microbes and red blood cells to optically thick (~ 300 µm) plant roots without fixation or clearing.

Machine learning approach for recognition and morphological analysis of isolated astrocytes in phase contrast microscopy.

Astrocytes are glycolytically active cells in the central nervous system playing a crucial role in various brain processes from homeostasis to neurotransmission. Astrocytes possess a complex branched morphology, frequently examined by fluorescent microscopy. However, staining and fixation may impact the properties of astrocytes, thereby affecting the accuracy of the experimental data of astrocytes dynamics and morphology. On the other hand, phase contrast microscopy can be used to study astrocytes morphology without affecting them, but the post-processing of the resulting low-contrast images is challenging. The main result of this work is a novel approach for recognition and morphological analysis of unstained astrocytes based on machine-learning recognition of microscopic images. We conducted a series of experiments involving the cultivation of isolated astrocytes from the rat brain cortex followed by microscopy. Using the proposed approach, we tracked the temporal evolution of the average total length of branches, branching, and area per astrocyte in our experiments. We believe that the proposed approach and the obtained experimental data will be of interest and benefit to the scientific communities in cell biology, biophysics, and machine learning.

Modern approaches to improving phase contrast electron microscopy.

Although defocus can be used to generate partial phase contrast in transmission electron microscope images, cryo-electron microscopy (cryo-EM) can be further improved by the development of phase plates which increase contrast by applying a phase shift to the unscattered part of the electron beam. Many approaches have been investigated, including the ponderomotive interaction between light and electrons. We review the recent successes achieved with this method in high-resolution, single-particle cryo-EM. We also review the status of using pulsed or near-field enhanced laser light as alternatives, along with approaches that use scanning transmission electron microscopy (STEM) with a segmented detector rather than a phase plate.

Rapid fiber-detection technique by artificial intelligence in phase-contrast microscope images of simulated atmospheric samples.

Since the manufacture, import, and use of asbestos products have been completely abolished in Japan, the main cause of asbestos emissions into the atmosphere is the demolition and removal of buildings built with asbestos-containing materials. To detect and correct asbestos emissions from inappropriate demolition and removal operations at an early stage, a rapid method to measure atmospheric asbestos fibers is required. The current rapid measurement method is a combination of short-term atmospheric sampling and phase-contrast microscopy counting. However, visual counting takes a considerable amount of time and is not sufficiently fast. Using artificial intelligence (AI) to analyze microscope images to detect fibers may greatly reduce the time required for counting. Therefore, in this study, we investigated the use of AI image analysis for detecting fibers in phase-contrast microscope images. A series of simulated atmospheric samples prepared from standard samples of amosite and chrysotile were observed using a phase-contrast microscope. Images were captured, and training datasets were created from the counting results of expert analysts. We adopted 2 types of AI models-an instance segmentation model, namely the mask region-based convolutional neural network (Mask R-CNN), and a semantic segmentation model, namely the multi-level aggregation network (MA-Net)-that were trained to detect asbestos fibers. The accuracy of fiber detection achieved with the Mask R-CNN model was 57% for recall and 46% for precision, whereas the accuracy achieved with the MA-Net model was 95% for recall and 91% for precision. Therefore, satisfactory results were obtained with the MA-Net model. The time required for fiber detection was less than 1 s per image in both AI models, which was faster than the time required for counting by an expert analyst.

Integrating X-ray phase-contrast imaging and histology for comparative evaluation of breast tissue malignancies in virtual histology analysis.

Detecting breast tissue alterations is essential for cancer diagnosis. However, inherent bidimensionality limits histological procedures' effectiveness in identifying these changes. Our study applies a 3D virtual histology method based on X-ray phase-contrast microtomography (PhC µ CT), performed at a synchrotron facility, to investigate breast tissue samples including different types of lesions, namely intraductal papilloma, micropapillary intracystic carcinoma, and invasive lobular carcinoma. One-to-one comparisons of X-ray and histological images explore the clinical potential of 3D X-ray virtual histology. Results show that PhC µ CT technique provides high spatial resolution and soft tissue sensitivity, while being non-destructive, not requiring a dedicated sample processing and being compatible with conventional histology. PhC µ CT can enhance the visualization of morphological characteristics such as stromal tissue, fibrovascular core, terminal duct lobular unit, stromal/epithelium interface, basement membrane, and adipocytes. Despite not reaching the (sub) cellular level, the three-dimensionality of PhC µ CT images allows to depict in-depth alterations of the breast tissues, potentially revealing pathologically relevant details missed by a single histological section. Compared to serial sectioning, PhC µ CT allows the virtual investigation of the sample volume along any orientation, possibly guiding the pathologist in the choice of the most suitable cutting plane. Overall, PhC µ CT virtual histology holds great promise as a tool adding to conventional histology for improving efficiency, accessibility, and diagnostic accuracy of pathological evaluation.

AI-based Apoptosis Cell Classification Using Phase-contrast Images of K562 Cells.

BACKGROUND/AIM: This study aimed to automate the classification of cells, particularly in identifying apoptosis, using artificial intelligence (AI) in conjunction with phase-contrast microscopy. The objective was to reduce reliance on manual observation, which is often time-consuming and subject to human error. MATERIALS AND METHODS: K562 cells were used as a model system and apoptosis was induced following administration of gamma-secretase inhibitors. Fluorescence staining was applied to detect DNA fragmentation and caspase activity. Cell images were obtained using both phase-contrast and fluorescence microscopy. Two AI models, Lobe(R) and a server-based ResNet50, were trained using these images and evaluated using F-values through five-fold cross-validation. RESULTS: Both AI models demonstrated effectively categorized individual cells into three groups: caspase-negative/no DNA fragmentation, caspase-positive/no DNA fragmentation, and caspase-positive/DNA fragmentation. Notably, the AI models' ability to differentiate cells relied on subtle variations in phase-contrast images, potentially linked to changes in refractive indices during apoptosis progression. Both AI models exhibited high accuracy, with the server-based ResNet50 model showing improved performance through repeated training. CONCLUSION: This study demonstrates the potential of AI-assisted phase-contrast microscopy as a powerful tool for automating cell classification, especially in the context of apoptosis research and the discovery of anticancer substances. By reducing the need for manual labor and enhancing classification accuracy, this approach holds promise for expediting high-throughput cell screening, significantly contributing to advancements in medical diagnostics and drug development.

Phase contrast reflectance confocal brain imaging at 1650 nm.

Significance: The imaging depth of microscopy techniques is limited by the ability of light to penetrate biological tissue. Recent research has addressed this limitation by combining a reflectance confocal microscope with the NIR-II (or shortwave infrared) spectrum. This approach offers significant imaging depth, is straightforward in design, and remains cost-effective. However, the imaging system, which relies on intrinsic signals, could benefit from adjustments in its optical design and post-processing methods to differentiate cortical cells, such as neurons and small blood vessels. Aim: We implemented a phase contrast detection scheme to a reflectance confocal microscope using NIR-II spectral range as illumination. Approach: We analyzed the features retrieved in the images while testing the imaging depth. Moreover, we introduce an acquisition method for distinguishing dynamic signals from the background, allowing the creation of vascular maps similar to those produced by optical coherence tomography. Results: The phase contrast implementation is successful to retrieve deep images in the cortex up to 800 µm using a cranial window. Vascular maps were retrieved at similar cortical depth and the possibility of combining multiple images can provide a vessel network. Conclusions: Phase contrast reflectance confocal microscopy can improve the outlining of cortical cell bodies. With the presented framework, angiograms can be retrieved from the dynamic signal in the biological tissue. Our work presents an optical implementation and analysis techniques from a former microscope design.

Single fiber curvature for muscle impairment assessment: Phase contrast imaging of stroke-induced animals.

There are technical challenges in imaging studies that can three-dimensionally (3D) analyze a single fiber (SF) to observe the functionality of the entire muscle after stroke. This study proposes a 3D assessment technique that only segments the SF of the right stroke-induced soleus muscle of a gerbil using synchrotron radiation x-ray microcomputed tomography (SR-µCT), which is capable of muscle structure analysis. Curvature damage in the SF of the left soleus muscle (impaired) progressed at 7-day intervals after the stroke in the control; particularly on the 7 days (1 week) and 14 days (2 weeks), as observed through visualization analysis. At 2 weeks, the SF volume was significantly reduced in the control impaired group (p = .033), and was significantly less than that in the non-impaired group (p = .009). We expect that animal post-stroke studies will improve the basic field of rehabilitation therapy by diagnosing the degree of SF curvature. RESEARCH HIGHLIGHTS: Muscle evaluation after ischemic stroke using synchrotron radiation x-ray microcomputed tomography (SR-µCT). Curvature is measured by segmenting a single fiber (SF) in the muscle. Structural changes in the SF of impaired gerbils at 7-day intervals were assessed.

Beyond fluorescence: advances in computational label-free full specificity in 3D quantitative phase microscopy.

Despite remarkable progresses in quantitative phase imaging (QPI) microscopes, their wide acceptance is limited due to the lack of specificity compared with the well-established fluorescence microscopy. In fact, the absence of fluorescent tag prevents to identify subcellular structures in single cells, making challenging the interpretation of label-free 2D and 3D phase-contrast data. Great effort has been made by many groups worldwide to address and overcome such limitation. Different computational methods have been proposed and many more are currently under investigation to achieve label-free microscopic imaging at single-cell level to recognize and quantify different subcellular compartments. This route promises to bridge the gap between QPI and FM for real-world applications.

A Condensed History Approach to X-Ray Dark Field Effects in Edge Illumination Phase Contrast Simulations.

X-ray dark field signals, measurable in many x-ray phase contrast imaging (XPCI) setups, stem from unresolvable microstructures in the scanned sample. This makes them ideally suited for the detection of certain pathologies, which correlate with changes in the microstructure of a sample. Simulations of x-ray dark field signals can aid in the design and optimization of XPCI setups, and the development of new reconstruction techniques. Current simulation tools, however, require explicit modelling of the sample microstructures according to their size and spatial distribution. This process is cumbersome, does not translate well between different samples, and considerably slows down simulations. In this work, a condensed history approach to modelling x-ray dark field effects is presented, under the assumption of an isotropic distribution of microstructures, and applied to edge illumination phase contrast simulations. It substantially simplifies the sample model, can be easily ported between samples, and is two orders of magnitude faster than conventional dark field simulations, while showing equivalent results.Clinical relevance- Dark field signal provides information on the microstructure distribution within the investigated sample, which can be applied in areas such as histology and lung x-ray imaging. Efficient simulation tools for this dark field signal aid in optimizing scanning setups, acquisition schemes and reconstruction techniques.

Intelligent Phase Contrast Meta-Microscope System.

Phase contrast imaging techniques enable the visualization of disparities in the refractive index among various materials. However, these techniques usually come with a cost: the need for bulky, inflexible, and complicated configurations. Here, we propose and experimentally demonstrate an ultracompact meta-microscope, a novel imaging platform designed to accomplish both optical and digital phase contrast imaging. The optical phase contrast imaging system is composed of a pair of metalenses and an intermediate spiral phase metasurface located at the Fourier plane. The performance of the system in generating edge-enhanced images is validated by imaging a variety of human cells, including lung cell lines BEAS-2B, CLY1, and H1299 and other types. Additionally, we integrate the ResNet deep learning model into the meta-microscope to transform bright-field images into edge-enhanced images with high contrast accuracy. This technology promises to aid in the development of innovative miniature optical systems for biomedical and clinical applications.

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use.

DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy.

Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell level with high temporal resolution. Successful application of single-cell time-lapse microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. However, segmentation and tracking of single cells remain challenging for the analysis of time-lapse microscopy images, in particular for widely available and non-toxic imaging modalities such as phase-contrast imaging. This work presents a versatile and trainable deep-learning model, termed DeepSea, that allows for both segmentation and tracking of single cells in sequences of phase-contrast live microscopy images with higher precision than existing models. We showcase the application of DeepSea by analyzing cell size regulation in embryonic stem cells.

Single-shot quantitative differential phase contrast imaging combined with programmable polarization multiplexing illumination.

We propose a single-shot quantitative differential phase contrast method with polarization multiplexing illumination. In the illumination module of our system, a programmable LED array is divided into four quadrants and covered with polarizing films of four different polarization angles. We use a polarization camera with polarizers before the pixels in the imaging module. By matching the polarization angle between the polarizing films over the custom LED array and the polarizers in the camera, two sets of asymmetric illumination acquisition images can be calculated from a single-shot acquisition image. Combined with the phase transfer function, we can calculate the quantitative phase of the sample. We present the design, implementation, and experimental image data demonstrating the ability of our method to obtain quantitative phase images of a phase resolution target, as well as Hela cells.

Untrained deep learning-based differential phase-contrast microscopy.

Quantitative differential phase-contrast (DPC) microscopy produces phase images of transparent objects based on a number of intensity images. To reconstruct the phase, in DPC microscopy, a linearized model for weakly scattering objects is considered; this limits the range of objects to be imaged, and requires additional measurements and complicated algorithms to correct for system aberrations. Here, we present a self-calibrated DPC microscope using an untrained neural network (UNN), which incorporates the nonlinear image formation model. Our method alleviates the restrictions on the object to be imaged and simultaneously reconstructs the complex object information and aberrations, without any training dataset. We demonstrate the viability of UNN-DPC microscopy through both numerical simulations and LED microscope-based experiments.

Deep learning for asbestos counting.

The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As an alternative, we implemented a deep learning procedure on images directly acquired from the untreated airborne samples using standard Mixed Cellulose Ester (MCE) filters. Several samples with a mix of chrysotile and crocidolite with different concentration loads have been prepared. Using a 20x objective lens coupled with a backlight illumination system a number of 140 images were collected from these samples, which along with additional 13 highly fibre loaded artificial images constituted the database. About 7500 fibres were manually recognised and annotated following the National Institute for Occupational Safety and Health (NIOSH) fibre counting Method 7400 as input for the training and validation of the model. The best trained model provides a total precision of 0.84 with F1-Score of 0.77 at a confidence of 0.64. A further post-detection refinement to ignore detected fibres < 5 µm in length improves the final precision. This method can be considered as a reliable and competent alternative to conventional PCM.

Three-dimensional visualization of plant tissues and organs by X-ray micro-computed tomography.

Studies visualizing plant tissues and organs in three-dimension (3D) using micro-computed tomography (CT) published since approximately 2015 are reviewed. In this period, the number of publications in the field of plant sciences dealing with micro-CT has increased along with the development of high-performance lab-based micro-CT systems as well as the continuous development of cutting-edge technologies at synchrotron radiation facilities. The widespread use of commercially available lab-based micro-CT systems enabling phase-contrast imaging technique, which is suitable for the visualization of biological specimens composed of light elements, appears to have facilitated these studies. Unique features of the plant body, which are particularly utilized for the imaging of plant organs and tissues by micro-CT, are having functional air spaces and specialized cell walls, such as lignified ones. In this review, we briefly describe the basis of micro-CT technology first and then get down into details of its application in 3D visualization in plant sciences, which are categorized as follows: imaging of various organs, caryopses, seeds, other organs (reproductive organs, leaves, stems and petioles), various tissues (leaf venations, xylems, air-filled tissues, cell boundaries, cell walls), embolisms and root systems, hoping that wide users of microscopes and other imaging technologies will be interested also in micro-CT and obtain some hints for a deeper understanding of the structure of plant tissues and organs in 3D. Majority of the current morphological studies using micro-CT still appear to be at a qualitative level. Development of methodology for accurate 3D segmentation is needed for the transition of the studies from a qualitative level to a quantitative level in the future.

Comparing x-ray phase-contrast imaging using a Talbot array illuminator to propagation-based imaging for non-homogeneous biomedical samples.

Phase-contrast computed tomography can visualize soft tissue samples with high contrast. At coherent sources, propagation-based imaging (PBI) techniques are among the most common, as they are easy to implement and produce high-resolution images. Their downside is a low degree of quantitative data due to simplifying assumptions of the sample properties in the reconstruction. These assumptions can be avoided, by using quantitative phase-contrast techniques as an alternative. However, these often compromise spatial resolution and require complicated setups. In order to overcome this limitation, we designed and constructed a new imaging setup using a 2D Talbot array illuminator as a wavefront marker and speckle-based imaging phase-retrieval techniques. We developed a post-processing chain that can compensate for wavefront marker drifts and that improves the overall sensitivity. By comparing two measurements of biomedical samples, we demonstrate that the spatial resolution of our setup is comparable to the one of PBI scans while being able to successfully image a sample that breaks the typical homogeneity assumption used in PBI.

Multiscale optical phase fluctuations link disorder strength and fractal dimension of cell structure.

Optical methods for examining cellular structure based on endogenous contrast rely on analysis of refractive index changes to discriminate cell phenotype. These changes can be visualized using techniques such as phase contrast microscopy, detected by light scattering, or analyzed numerically using quantitative phase imaging. The statistical variations of refractive index at the nanoscale can be quantified using disorder strength, a metric seen to increase with neoplastic change. In contrast, the spatial organization of these variations is typically characterized using a fractal dimension, which is also seen to increase with cancer progression. Here, we seek to link these two measurements using multiscale measurements of optical phase to calculate disorder strength and in turn to determine the fractal dimension of the structures. First, quantitative phase images are analyzed to show that the disorder strength metric changes with resolution. The trend of disorder strength with length scales is analyzed to determine the fractal dimension of the cellular structures. Comparison of these metrics is presented for different cell lines with varying phenotypes including MCF10A, MCF7, BT474, HT-29, A431, and A549 cell lines, in addition to three cell populations with modified phenotypes. Our results show that disorder strength and fractal dimension can both be obtained with quantitative phase imaging and that these metrics can independently distinguish between different cell lines. Furthermore, their combined use presents a new approach for better understanding cellular restructuring during different pathways.