Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening

Biophysical profiling of red blood cells from thin-film blood smears using deep learning

Microscopic inspection of thin-film blood smears is widely used to identify red blood cell (RBC) pathologies, including malaria parasitism and hemoglobinopathies, such as sickle cell disease and thalassemia. Emerging research indicates that non-pathologic changes in RBCs can also be detected in images, such as deformability and morphological changes resulting from the storage lesion. In transfusion medicine, cell deformability is a potential biomarker for the quality of donated RBCs. However, a major impediment to the clinical translation of this biomarker is the difficulty associated with performing this measurement. To address this challenge, we developed an approach for biophysical profiling of RBCs based on cell images in thin-film blood smears. We hypothesize that subtle cellular changes are evident in blood smear images, but this information is inaccessible to human expert labellers. To test this hypothesis, we developed a deep learning strategy to analyze Giemsa-stained blood smears to assess the subtle morphologies indicative of RBC deformability and storage-based degradation. Specifically, we prepared thin-film blood smears from 27 RBC samples (9 donors evaluated at 3 storage time points) and imaged them using high-resolution microscopy. Using this dataset, we trained a convolutional neural network to evaluate image-based morphological features related to cell deformability. The prediction of donor deformability is strongly correlated to the microfluidic scores and can be used to categorize images into specific deformability groups with high accuracy. We also used this model to evaluate differences in RBC morphology resulting from cold storage. Together, our results demonstrate that deep learning models can detect subtle cellular morphology differences resulting from deformability and cold storage. This result suggests the potential to assess donor blood quality from thin-film blood smears, which can be acquired ubiquitously in clinical workflows.

DHM/SERS reveals cellular morphology and molecular changes during iPSCs-derived activation of astrocytes

The activation of astrocytes derived from induced pluripotent stem cells (iPSCs) is of great significance in neuroscience research, and it is crucial to obtain both cellular morphology and biomolecular information non-destructively in situ, which is still complicated by the traditional optical microscopy and biochemical methods such as immunofluorescence and western blot. In this study, we combined digital holographic microscopy (DHM) and surface-enhanced Raman scattering (SERS) to investigate the activation characteristics of iPSCs-derived astrocytes. It was found that the projected area of activated astrocytes decreased by 67%, while the cell dry mass increased by 23%, and the cells changed from a flat polygonal shape to an elongated star-shaped morphology. SERS analysis further revealed an increase in the intensities of protein spectral peaks (phenylalanine 1001 cm-1, proline 1043 cm-1, etc.) and lipid-related peaks (phosphatidylserine 524 cm-1, triglycerides 1264 cm-1, etc.) decreased in intensity. Principal component analysis-linear discriminant analysis (PCA-LDA) modeling based on spectral data distinguished resting and reactive astrocytes with a high accuracy of 96.5%. The increase in dry mass correlated with the increase in protein content, while the decrease in projected area indicated the adjustment of lipid composition and cell membrane remodeling. Importantly, the results not only reveal the cellular morphology and molecular changes during iPSCs-derived astrocytes activation but also reflect their mapping relationship, thereby providing new insights into diagnosing and treating neurodegenerative diseases.

Open-access database for digital lensless holographic microscopy and its application on the improvement of deep-learning-based autofocusing models

Among modern optical microscopy techniques, digital lensless holographic microscopy (DLHM) is one of the simplest label-free coherent imaging approaches. However, the hardware simplicity provided by the lensless configuration is often offset by the demanding computational postprocessing required to match the retrieved sample information to the user's expectations. A promising avenue to simplify this stage is the integration of artificial intelligence and machine learning (ML) solutions into the DLHM workflow. The biggest challenge to do so is the preparation of an extensive and high-quality experimental dataset of curated DLHM recordings to train ML models. In this work, a diverse, open-access dataset of DLHM recordings is presented as support for future research, contributing to the data needs of the applied research community. The database comprises 11,760 experimental DLHM holograms of bio and non-bio samples with diversity on the main recording parameters of the DLHM architecture. The database is divided into two datasets of 10 independent imaged samples. The first group, named multi-wavelength dataset, includes 8160 holograms and was recorded using laser diodes emitting at 654 nm, 510 nm, and 405 nm; the second group, named single-wavelength dataset, is composed of 3600 recordings and was acquired using a 633 nm He-Ne laser. All the experimental parameters related to the dataset acquisition, preparation, and calibration are described in this paper. The advantages of this large dataset are validated by re-training an existing autofocusing model for DLHM and as the training set for a simpler architecture that achieves comparable performance, proving its feasibility for improving existing ML-based models and the development of new ones.

A dataset of digital holograms of normal and thalassemic cells

Digital holographic microscopy (DHM) is an intriguing medical diagnostic tool due to its label-free and quantitative nature, providing high-contrast images of phase samples. By capturing both intensity and phase information, DHM enables the numerical reconstruction of quantitative phase images. However, the lateral resolution is limited by the diffraction limit, which prompted the recent suggestion of microsphere-assisted DHM to enhance the DHM resolution straightforwardly. The use of such a technique as a medical diagnostic tool requires testing and validation of the proposed assays to prove their feasibility and viability. This paper publishes 760 and 609 microsphere-assisted DHM images of normal and thalassemic red blood cells obtained from a normal and thalassemic male individual, respectively.

On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.

Complete holography-based system for the identification of microparticles in water samples

Here, we present a comprehensive holography-based system designed for detecting microparticles through microscopic holographic projections of water samples. This system is designed for researchers who may be unfamiliar with holographic technology but are engaged in microparticle research, particularly in the field of water analysis. Additionally, our innovative system can be deployed for environmental monitoring as a component of an autonomous sailboat robot. Our system's primary application is for large-scale classification of diverse microplastics that are prevalent in water bodies worldwide. This paper provides a step-by-step guide for constructing our system and outlines its entire processing pipeline, including hologram acquisition for image reconstruction.

A Novel Image Processing Method for Obtaining an Accurate Three-Dimensional Profile of Red Blood Cells in Digital Holographic Microscopy

Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep learning techniques and digital holographic microscope (DHM) techniques. However, three-dimensional (3D) profile obtained by DHM has a problem of random noise caused by the overlapping DC spectrum and sideband in the Fourier domain, which has the probability of misjudging diseases in deep learning technology. To reduce random noise and obtain a more accurate 3D profile, in this paper, we propose a novel image processing method which randomly selects the center of the high-frequency sideband (RaCoHS) in the Fourier domain. This proposed algorithm has the advantage of filtering while using only recorded hologram information to maintain high-frequency information. We compared and analyzed the conventional filtering method and the general image processing method to verify the effectiveness of the proposed method. In addition, the proposed image processing algorithm can be applied to all digital holography technologies including DHM, and in particular, it is expected to have a great effect on the accuracy of disease diagnosis technologies using DHM.

Photolithographic patterning on multi-wavelength quantum dot film of the improved conversion efficiency for digital holography

A triple-wavelength patterned quantum dot film was fabricated for the light source of digital holography to improve both the axial measurement range and noise reduction. The patterned quantum dot film was fabricated after optimizing the photolithography process condition based on the UV-curable quantum dot solution, which was capable of multiple patterning processes. In addition, an optimized pattern structure was developed by adding TiO2 nanoparticles to both the quantum dot and bank layers to increase the scattering effect for the improved photoluminescence intensity. Finally, the newly developed light source with the balanced spectral distribution was applied to the digital holography, rendering it applicable as an improved light source.

Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning

A plaque assay-the gold-standard method for measuring the concentration of replication-competent lytic virions-requires staining and usually more than 48 h of runtime. Here we show that lens-free holographic imaging and deep learning can be combined to expedite and automate the assay. The compact imaging device captures phase information label-free at a rate of approximately 0.32 gigapixels per hour per well, covers an area of about 30 × 30 mm2 and a 10-fold larger dynamic range of virus concentration than standard assays, and quantifies the infected area and the number of plaque-forming units. For the vesicular stomatitis virus, the automated plaque assay detected the first cell-lysing events caused by viral replication as early as 5 h after incubation, and in less than 20 h it detected plaque-forming units at rates higher than 90% at 100% specificity. Furthermore, it reduced the incubation time of the herpes simplex virus type 1 by about 48 h and that of the encephalomyocarditis virus by about 20 h. The stain-free assay should be amenable for use in virology research, vaccine development and clinical diagnosis.

Label-Free Digital Holotomography Reveals Ibuprofen-Induced Morphological Changes to Red Blood Cells

Understanding the dose-dependent effect of over-the-counter drugs on red blood cells (RBCs) is crucial for hematology and digital pathology. Yet, it is challenging to continuously record the real-time, drug-induced shape changes of RBCs in a label-free manner. Here, we demonstrate digital holotomography (DHTM)-enabled real-time, label-free concentration-dependent and time-dependent monitoring of ibuprofen on RBCs from a healthy donor. The RBCs are segmented based on three-dimensional (3D) and four-dimensional (4D) refractive index tomograms, and their morphological and chemical parameters are retrieved with their shapes classified using machine learning. We directly observed the formation and motion of spicules on the RBC membrane when aqueous solutions of ibuprofen were drop-cast on wet blood, creating rough-membraned echinocyte forms. At low concentrations of 0.25-0.50 mM, the ibuprofen-induced morphological change was transient, but at high concentrations (1-3 mM) the spiculated RBC remained over a period of up to 1.5 h. Molecular simulations confirmed that aggregates of ibuprofen molecules at high concentrations significantly disrupted the RBC membrane structural integrity and lipid order but produced negligible effect at low ibuprofen concentrations. Control experiments on the effect of urea, hydrogen peroxide, and aqueous solutions on RBCs showed zero spicule formation. Our work clarifies the dose-dependent chemical effects on RBCs using label-free microscopes that can be deployed for the rapid detection of overdosage of over-the-counter and prescribed drugs.

Label-free optical interferometric microscopy to characterize morphodynamics in living plants

During the last century, fluorescence microscopy has played a pivotal role in a range of scientific discoveries. The success of fluorescence microscopy has prevailed despite several shortcomings like measurement time, photobleaching, temporal resolution, and specific sample preparation. To bypass these obstacles, label-free interferometric methods have been developed. Interferometry exploits the full wavefront information of laser light after interaction with biological material to yield interference patterns that contain information about structure and activity. Here, we review recent studies in interferometric imaging of plant cells and tissues, using techniques such as biospeckle imaging, optical coherence tomography, and digital holography. These methods enable quantification of cell morphology and dynamic intracellular measurements over extended periods of time. Recent investigations have showcased the potential of interferometric techniques for precise identification of seed viability and germination, plant diseases, plant growth and cell texture, intracellular activity and cytoplasmic transport. We envision that further developments of these label-free approaches, will allow for high-resolution, dynamic imaging of plants and their organelles, ranging in scales from sub-cellular to tissue and from milliseconds to hours.

Holographic flow scanning cytometry overcomes depth of focus limits and smartly adapts to microfluidic speed

Space-time digital holography (STDH) maps holograms in a hybrid space-time domain to achieve extended field of view, resolution enhanced, quantitative phase-contrast microscopy and velocimetry of flowing objects in a label-free modality. In STDH, area sensors can be replaced by compact and faster linear sensor arrays to augment the imaging throughput and to compress data from a microfluidic video sequence into one single hybrid hologram. However, in order to ensure proper imaging, the velocity of the objects in microfluidic channels has to be well-matched to the acquisition frame rate, which is the major constraint of the method. Also, imaging all the flowing samples in focus at the same time, while avoiding hydrodynamic focusing devices, is a highly desirable goal. Here we demonstrate a novel processing pipeline that addresses non-ideal flow conditions and is capable of returning the correct and extended focus phase contrast mapping of an entire microfluidic experiment in a single image. We apply this novel processing strategy to recover phase imaging of flowing HeLa cells in a lab-on-a-chip platform even when severely undersampled due to too fast flow while ensuring that all cells are in focus.

Adapting a Blu-ray optical pickup unit as a point source for digital lensless holographic microscopy

The adaptation of an off-the-shelf Blu-ray optical pickup unit (OPU) into a highly versatile point source for digital lensless holographic microscopy (DLHM) is presented. DLHM performance is mostly determined by the optical properties of the point source of spherical waves used for free-space magnification of the sample's diffraction pattern; in particular, its wavelength and numerical aperture define the achievable resolution, and its distance to the recording medium sets the magnification. Through a set of straightforward modifications, a commercial Blu-ray OPU can be transformed into a DLHM point source with three selectable wavelengths, a numerical aperture of up to 0.85, and integrated micro-displacements in both axial and transversal directions. The functionality of the OPU-based point source is then experimentally validated in the observation of micrometer-sized calibrated samples and biological specimens of common interest, showing the feasibility of obtaining sub-micrometer resolution and offering a versatile option for the development of new cost-effective and portable microscopy devices.

SHAPR predicts 3D cell shapes from 2D microscopic images

Reconstruction of shapes and sizes of three-dimensional (3D) objects from two- dimensional (2D) information is an intensely studied subject in computer vision. We here consider the level of single cells and nuclei and present a neural network-based SHApe PRediction autoencoder. For proof-of-concept, SHAPR reconstructs 3D shapes of red blood cells from single view 2D confocal microscopy images more accurately than naïve stereological models and significantly increases the feature-based prediction of red blood cell types from F1 = 79% to F1 = 87.4%. Applied to 2D images containing spheroidal aggregates of densely grown human induced pluripotent stem cells, we find that SHAPR learns fundamental shape properties of cell nuclei and allows for prediction-based morphometry. Reducing imaging time and data storage, SHAPR will help to optimize and up-scale image-based high-throughput applications for biomedicine.

White blood cell classification via a discriminative region detection assisted feature aggregation network

White blood cell (WBC) classification plays an important role in human pathological diagnosis since WBCs will show different appearance when they fight with various disease pathogens. Although many previous white blood cell classification have been proposed and earned great success, their classification accuracy is still significantly affected by some practical issues such as uneven staining, boundary blur and nuclear intra-class variability. In this paper, we propose a deep neural network for WBC classification via discriminative region detection assisted feature aggregation (DRFA-Net), which can accurately locate the WBC area to boost final classification performance. Specifically, DRFA-Net uses an adaptive feature enhancement module to refine multi-level deep features in a bilateral manner for efficiently capturing both high-level semantic information and low-level details of WBC images. Considering the fact that background areas could inevitably produce interference, we design a network branch to detect the WBC area with the supervision of segmented ground truth. The bilaterally refined features obtained from two directions are finally aggregated for final classification, and the detected WBC area is utilized to highlight the features of discriminative regions by an attention mechanism. Extensive experiments on several public datasets are conducted to validate that our proposed DRFA-Net can obtain higher accuracies when compared with other state-of-the-art WBC classification methods.

COVID-19 screening with digital holographic microscopy using intra-patient probability functions of spatio-temporal bio-optical attributes

We present an automated method for COVID-19 screening using the intra-patient population distributions of bio-optical attributes extracted from digital holographic microscopy reconstructed red blood cells. Whereas previous approaches have aimed to identify infection by classifying individual cells, here, we propose an approach to incorporate the attribute distribution information from the population of a given human subjects' cells into our classification scheme and directly classify subjects at the patient level. To capture the intra-patient distribution information in a generalized way, we propose an approach based on the Bag-of-Features (BoF) methodology to transform histograms of bio-optical attribute distributions into feature vectors for classification via a linear support vector machine. We compare our approach with simpler classifiers directly using summary statistics such as mean, standard deviation, skewness, and kurtosis of the distributions. We also compare to a k-nearest neighbor classifier using the Kolmogorov-Smirnov distance as a distance metric between the attribute distributions of each subject. We lastly compare our approach to previously published methods for classification of individual red blood cells. In each case, the methodology proposed in this paper provides the highest patient classification performance, correctly classifying 22 out of 24 individuals and achieving 91.67% classification accuracy with 90.00% sensitivity and 92.86% specificity. The incorporation of distribution information for classification additionally led to the identification of a singular temporal-based bio-optical attribute capable of highly accurate patient classification. To the best of our knowledge, this is the first report of a machine learning approach using the intra-patient probability distribution information of bio-optical attributes obtained from digital holographic microscopy for disease screening.

An Efficient Method of Observing Diatom Frustules via Digital Holographic Microscopy

Herein, we propose a convenient method to enable pretreatment of target objects using digital holographic microscopy (DHM). As a test sample, we used diatom frustules (Nitzschia sp.) as the target objects. In the generally used sample preparation method, the frustule suspension is added dropwise onto a glass substrate or into a glass chamber. While our work confirms good observation of purified frustules using the typical sample preparation method, we also demonstrate a new procedure to observe unseparated structures of frustules prepared by baking them on a mica surface. The baked frustules on the mica surface were transferred to a glass chamber with 1% sodium dodecyl sulfate solution. In this manner, the unseparated structures of the diatom frustules were clearly observed. Furthermore, metal-coated frustules prepared by sputtering onto them on a mica surface were also clearly observed using the same procedure. Our method can be applied for the observation of any target object that is pretreated on a solid surface. We expect our proposed method to be a basis for establishing DHM techniques for microscopic observations of biomaterials.

Label-free hematology analysis method based on defocusing phase-contrast imaging under illumination of 415 nm light

Label-free imaging technology is a trending way to simplify and improve conventional hematology analysis by bypassing lengthy and laborious staining procedures. However, the existing methods do not well balance system complexity, data acquisition efficiency, and data analysis accuracy, which severely impedes their clinical translation. Here, we propose defocusing phase-contrast imaging under the illumination of 415 nm light to realize label-free hematology analysis. We have verified that the subcellular morphology of blood components can be visualized without complex staining due to the factor that defocusing can convert the second-order derivative distribution of samples' optical phase into intensity and the illumination of 415 nm light can significantly enhance the contrast. It is demonstrated that the defocusing phase-contrast images for the five leucocyte subtypes can be automatically discriminated by a trained deep-learning program with high accuracy (the mean F1 score: 0.986 and mean average precision: 0.980). Since this technique is based on a regular microscope, it simultaneously realizes low system complexity and high data acquisition efficiency with remarkable quantitative analysis ability. It supplies a label-free, reliable, easy-to-use, fast approach to simplifying and reforming the conventional way of hematology analysis.

A multiple instance learning approach for detecting COVID-19 in peripheral blood smears

A wide variety of diseases are commonly diagnosed via the visual examination of cell morphology within a peripheral blood smear. For certain diseases, such as COVID-19, morphological impact across the multitude of blood cell types is still poorly understood. In this paper, we present a multiple instance learning-based approach to aggregate high-resolution morphological information across many blood cells and cell types to automatically diagnose disease at a per-patient level. We integrated image and diagnostic information from across 236 patients to demonstrate not only that there is a significant link between blood and a patient's COVID-19 infection status, but also that novel machine learning approaches offer a powerful and scalable means to analyze peripheral blood smears. Our results both backup and enhance hematological findings relating blood cell morphology to COVID-19, and offer a high diagnostic efficacy; with a 79% accuracy and a ROC-AUC of 0.90.

Detection of intralayer alignment in multicomponent lipids by dynamic speckle pattern analysis

Multicomponent mixtures of bilayer lipids, thanks to the coexistence of liquid-crystalline phases in their structures, may be used in the development of functional membranes. In such membranes interlayer ordering distributes across membrane lamellae, resulting in long-range alignment of phase-separated domains. In this paper, we explore the dynamics of this phenomenon by laser speckle pattern analysis. We show that cholesterol content decreases the activity, and the rate of the domains size development is related to the change of physical roughness of the multicomponent lipid mixture. Our results are in agreement with the previous experimental reports. However, our experimental procedure is an easy-to-implement and effective methodology.

Single-Shot 3D Topography of Transmissive and Reflective Samples with a Dual-Mode Telecentric-Based Digital Holographic Microscope

Common path DHM systems are the most robust DHM systems as they are based on self-interference and are thus less prone to external fluctuations. A common issue amongst these DHM systems is that the two replicas of the sample’s information overlay due to self-interference, making them only suitable for imaging sparse samples. This overlay has restricted the use of common-path DHM systems in material science. The overlay can be overcome by limiting the sample’s field of view to occupy only half of the imaging field of view or by using an optical spatial filter. In this work, we have implemented optical spatial filtering in a common-path DHM system using a Fresnel biprism. We have analyzed the optimal pinhole size by evaluating the frequency content of the reconstructed phase images of a star target. We have also measured the accuracy of the system and the sensitivity to noise for different pinhole sizes. Finally, we have proposed the first dual-mode common-path DHM system using a Fresnel biprism. The performance of the dual-model DHM system has been evaluated experimentally using transmissive and reflective microscopic samples.

Even patients with mild COVID-19 symptoms after SARS-CoV-2 infection show prolonged altered red blood cell morphology and rheological parameters

Infection with the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the associated coronavirus disease-19 (COVID-19) might affect red blood cells (RBC); possibly altering oxygen supply. However, investigations of cell morphology and RBC rheological parameters during a mild disease course are lacking and thus, the aim of the study. Fifty individuals with mild COVID-19 disease process were tested after the acute phase of SARS-CoV-2 infection (37males/13 females), and the data were compared to n = 42 healthy controls (30 males/12 females). Analysis of venous blood samples, taken at rest, revealed a higher percentage of permanently elongated RBC and membrane extensions in COVID-19 patients. Haematological parameters and haemoglobin concentration, MCH and MCV in particular, were highly altered in COVID-19. RBC deformability and deformability under an osmotic gradient were significantly reduced in COVID-19 patients. Higher RBC-NOS activation was not capable to at least in part counteract these reductions. Impaired RBC deformability might also be related to morphological changes and/or increased oxidative state. RBC aggregation index remained unaffected. However, higher shear rates were necessary to balance the aggregation-disaggregation in COVID-19 patients which might be, among others, related to morphological changes. The data suggest prolonged modifications of the RBC system even during a mild COVID-19 disease course.

Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning

Tomographic flow cytometry by digital holography is an emerging imaging modality capable of collecting multiple views of moving and rotating cells with the aim of recovering their refractive index distribution in 3D. Although this modality allows us to access high-resolution imaging with high-throughput, the huge amount of time-lapse holographic images to be processed (hundreds of digital holograms per cell) constitutes the actual bottleneck. This prevents the system from being suitable for lab-on-a-chip platforms in real-world applications, where fast analysis of measured data is mandatory. Here we demonstrate a significant speeding-up reconstruction of phase-contrast tomograms by introducing in the processing pipeline a multi-scale fully-convolutional context aggregation network. Although it was originally developed in the context of semantic image analysis, we demonstrate for the first time that it can be successfully adapted to a holographic lab-on-chip platform for achieving 3D tomograms through a faster computational process. We trained the network with input-output image pairs to reproduce the end-to-end holographic reconstruction process, i.e. recovering quantitative phase maps (QPMs) of single cells from their digital holograms. Then, the sequence of QPMs of the same rotating cell is used to perform the tomographic reconstruction. The proposed approach significantly reduces the computational time for retrieving tomograms, thus making them available in a few seconds instead of tens of minutes, while essentially preserving the high-content information of tomographic data. Moreover, we have accomplished a compact deep convolutional neural network parameterization that can fit into on-chip SRAM and a small memory footprint, thus demonstrating its possible exploitation to provide onboard computations for lab-on-chip devices with low processing hardware resources.

COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy

We present an automated method for COVID-19 screening based on reconstructed phase profiles of red blood cells (RBCs) and a highly comparative time-series analysis (HCTSA). Video digital holographic data -was obtained using a compact, field-portable shearing microscope to capture the temporal fluctuations and spatio-temporal dynamics of live RBCs. After numerical reconstruction of the digital holographic data, the optical volume is calculated at each timeframe of the reconstructed data to produce a time-series signal for each cell in our dataset. Over 6000 features are extracted on the time-varying optical volume sequences using the HCTSA to quantify the spatio-temporal behavior of the RBCs, then a linear support vector machine is used for classification of individual RBCs. Human subjects are then classified for COVID-19 based on the consensus of their cells' classifications. The proposed method is tested on a dataset of 1472 RBCs from 24 human subjects (10 COVID-19 positive, 14 healthy) collected at UConn Health Center. Following a cross-validation procedure, our system achieves 82.13% accuracy, with 92.72% sensitivity, and 73.21% specificity (area under the receiver operating characteristic curve: 0.8357). Furthermore, the proposed system resulted in 21 out of 24 human subjects correctly labeled. To the best of our knowledge this is the first report of a highly comparative time-series analysis using digital holographic microscopy data.

Differential diagnosis of hereditary anemias from a fraction of blood drop by digital holography and hierarchical machine learning

Anemia affects about the 25% of the global population and can provoke severe diseases, ranging from weakness and dizziness to pregnancy problems, arrhythmias and hearth failures. About 10% of the patients are affected by rare anemias of which 80% are hereditary. Early differential diagnosis of anemia enables prescribing patients a proper treatment and diet, which is effective to mitigate the associated symptoms. Nevertheless, the differential diagnosis of these conditions is often difficult due to shared and overlapping phenotypes. Indeed, the complete blood count and unaided peripheral blood smear observation cannot always provide a reliable differential diagnosis, so that biomedical assays and genetic tests are needed. These procedures are not error-free, require skilled personnel, and severely impact the financial resources of national health systems. Here we show a differential screening system for hereditary anemias that relies on holographic imaging and artificial intelligence. Label-free holographic imaging is aided by a hierarchical machine learning decider that works even in the presence of a very limited dataset but is enough accurate for discerning between different anemia classes with minimal morphological dissimilarities. It is worth to notice that only a few tens of cells from each patient are sufficient to obtain a correct diagnosis, with the advantage of significantly limiting the volume of blood drawn. This work paves the way to a wider use of home screening systems for point of care blood testing and telemedicine with lab-on-chip platforms.

Deep learning for digital holography: a review

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities for DH. Here, we survey recent developments in various DH applications powered by deep learning algorithms. This article starts with a brief introduction to digital holographic imaging, then summarizes the most relevant deep learning techniques for DH, with discussions on their benefits and challenges. We then present case studies covering a wide range of problems and applications in order to highlight research achievements to date. We provide an outlook of several promising directions to widen the use of deep learning in various DH applications.

Single-shot wavelength-multiplexing for off-axis digital holography with a spectral filter

We present a single-shot wavelength-multiplexing technique for off-axis digital holography based on a spectral filter. Only a spectral filter is inserted between beam splitter and mirror in reflection off-axis digital holography (RODH). The spectral filter can transmit a well-defined wavelength band of light, while reject other unwanted radiation. By adjusting the filter and mirror separately, the propagation orientation of different reference beams of two wavelengths can be separated, and thus two off- axis holograms with different fringe directions are simultaneously captured by a monochrome camera. The wavefront interference analysis of using a spectral filter is discussed in detail. Our scheme is available for real-time wavelength-multiplexing but requires fewer optical elements and system modifications. Numerical simulation and experiment results of different types of spectral filters demonstrate the validity of proposed method.

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.