Automated segmentation of multiple red blood cells with digital holographic microscopy

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.

Hough transform-based multi-object autofocusing compressive holography

Reconstruction of multiple objects from one hologram can be affected by the focus metric judgment of autofocusing. Various segmentation algorithms are applied to obtain a single object in the hologram. Each object is unambiguously reconstructed to acquire its focal position, which produces complicated calculations. Herein, Hough transform (HT)-based multi-object autofocusing compressive holography is presented. The sharpness of each reconstructed image is computed by using a focus metric such as entropy or variance. According to the characteristics of the object, the standard HT is further used for calibration to remove redundant extreme points. The compressive holographic imaging framework with a filter layer can eliminate the inherent noise in in-line reconstruction including cross talk noise of different depth layers, two-order noise, and twin image noise. The proposed method can effectively obtain 3D information on multiple objects and achieve noise elimination by only reconstructing from one hologram.

Biomass measurements of single neurites in vitro using optical wavefront microscopy

Quantitative phase microscopies (QPMs) enable label-free, non-invasive observation of living cells in culture, for arbitrarily long periods of time. One of the main benefits of QPMs compared with fluorescence microscopy is the possibility to measure the dry mass of individual cells or organelles. While QPM dry mass measurements on neural cells have been reported this last decade, dry mass measurements on their neurites has been very little addressed. Because neurites are tenuous objects, they are difficult to precisely characterize and segment using most QPMs. In this article, we use cross-grating wavefront microscopy (CGM), a high-resolution wavefront imaging technique, to measure the dry mass of individual neurites of primary neurons in vitro. CGM is based on the simple association of a cross-grating positioned in front of a camera, and can detect wavefront distortions smaller than a hydrogen atom (∼0.1 nm). In this article, an algorithm for dry-mass measurement of neurites from CGM images is detailed and provided. With objects as small as neurites, we highlight the importance of dealing with the diffraction rings for proper image segmentation and accurate biomass measurements. The high precision of the measurements we obtain using CGM and this semi-manual algorithm enabled us to detect periodic oscillations of neurites never observed before, demonstrating the sufficient degree of accuracy of CGM to capture the cell dynamics at the single neurite level, with a typical precision of 2%, i.e., 0.08 pg in most cases, down to a few fg for the smallest objects.

Recording of Long Low-Amplitude Bulk Elastic Waves in Transparent Solid Waveguides by Digital and Classical Holography

In this paper we compare two implementations of the holographic technique for recording long, nonlinear, elastic waves of low amplitude in solid polymer waveguides: classical holographic interferometry and digital holography. Both implementations are realized in transmission configuration, with recording in the off-axis schematic. The advantages and disadvantages of these implementations are discussed as applied to the investigation of the evolution of shock waves and strain solitons in transparent solid waveguides.

High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks

SIGNIFICANCE

Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable.

AIM

Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future.

APPROACH

The raw diffraction patterns of RBCs were recorded by use of DHM. Ground-truth mask images were labeled based on phase images reconstructed from RBC holograms using numerical reconstruction algorithm. A deep FCN, which is UNet, was trained on the diffraction pattern images to achieve the label-free cell detection and counting.

RESULTS

The implemented deep FCNs provide a promising way to high-throughput and label-free counting of RBCs with a counting accuracy of 99% at a throughput rate of greater than 288 cells per second and 200  μm  ×  200  μm field of view at the single cell level. Compared to convolutional neural networks, the FCNs can get much better results in terms of accuracy and throughput rate.

CONCLUSIONS

High-throughput label-free cell detection and counting were successfully achieved from diffraction patterns with deep FCNs. It is a promising approach for biological specimen analysis based on raw hologram directly.

Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network

SIGNIFICANCE

Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase images efficiently, their applications in diagnostic testing are limited by the lack of transparency. More interpretable results such as morphological and biochemical characteristics of individual RBCs are highly desirable.

AIM

An end-to-end deep-learning model was developed to efficiently discriminate thalassemic RBCs (tRBCs) from healthy RBCs (hRBCs) in quantitative phase images and segment RBCs for single-cell characterization.

APPROACH

Two-dimensional quantitative phase images of hRBCs and tRBCs were acquired using digital holographic microscopy. A mask region-based convolutional neural network (Mask R-CNN) model was trained to discriminate tRBCs and segment individual RBCs. Characterization of tRBCs was achieved utilizing SHapley Additive exPlanation analysis and canonical correlation analysis on automatically segmented RBC phase images.

RESULTS

The implemented model achieved 97.8% accuracy in detecting tRBCs. Phase-shift statistics showed the highest influence on the correct classification of tRBCs. Associations between the phase-shift features and three-dimensional morphological features were revealed.

CONCLUSIONS

The implemented Mask R-CNN model accurately identified tRBCs and segmented RBCs to provide single-RBC characterization, which has the potential to aid clinical decision-making.

Role of pH level on the morphology and growth rate of myelin figures

The myelin figure (MF) is one of the basic structures of lipids, and the study of their formation and the effect of various parameters on their growth is useful in understanding several biological processes. In this paper, we address the influence of the pH degree of the surrounding medium on MF dynamics. We introduce a tunable shearing digital holographic microscopy arrangement to obtain quantitative and volumetric information about the complex growth of MFs. Our results show that (1) the time evolution of relative length and volume changes of MFs follows a power-law, (2) the acidity facilitates the growth rate, and (3) the acidic environment causes the formation of thicker MFs.

Classification of cell morphology with quantitative phase microscopy and machine learning

We describe and compare two machine learning approaches for cell classification based on label-free quantitative phase imaging with transport of intensity equation methods. In one approach, we design a multilevel integrated machine learning classifier including various individual models such as artificial neural network, extreme learning machine and generalized logistic regression. In another approach, we apply a pretrained convolutional neural network using transfer learning for the classification. As a validation, we show the performances of both approaches on classification between macrophages cultured in normal gravity and microgravity with quantitative phase imaging. The multilevel integrated classifier achieves average accuracy 93.1%, which is comparable to the average accuracy 93.5% obtained by convolutional neural network. The presented quantitative phase imaging system with two classification approaches could be helpful to biomedical scientists for easy and accurate cell analysis.

Spectroscopic Evaluation of Red Blood Cells of Thalassemia Patients with Confocal Microscopy: A Pilot Study

Hemoglobinopathies represent the most common single-gene defects in the world and pose a major public health problem, particularly in tropical countries, where they occur with high frequency. Diagnosing hemoglobinopathies can sometimes be difficult due to the coexistence of different causes of anemia, such as thalassemia and iron deficiency, and blood transfusions, among other factors, and requires expensive and complex molecular tests. This work explores the possibility of using spectral confocal microscopy as a diagnostic tool for thalassemia in pediatric patients, a disease caused by mutations in the globin genes that result in changes of the globin chains that form hemoglobin-in pediatric patients. Red blood cells (RBCs) from patients with different syndromes of alpha-thalassemia and iron deficiency (including anemia) as well as healthy (control) subjects were analyzed under a Leica TCS SP8 confocal microscope following different image acquisition protocols. We found that diseased RBCs exhibited autofluorescence when excited at 405 nm and their emission was collected in the spectral range from 425 nm to 790 nm. Three experimental descriptors calculated from the mean emission intensities at 502 nm, 579 nm, 628 nm, and 649 nm allowed us to discriminate between diseased and healthy cells. According to the results obtained, spectral confocal microscopy could serve as a tool in the diagnosis of thalassemia.

Automated quantitative analysis of multiple cardiomyocytes at the single-cell level with three-dimensional holographic imaging informatics

Cardiomyocytes derived from human pluripotent stem cells are a promising tool for disease modeling, drug compound testing, and cardiac toxicity screening. Bio-image segmentation is a prerequisite step in cardiomyocyte image analysis by digital holography (DH) in microscopic configuration and has provided satisfactory results. In this study, we quantified multiple cardiac cells from segmented 3-dimensional DH images at the single-cell level and measured multiple parameters describing the beating profile of each individual cell. The beating profile is extracted by monitoring dry-mass distribution during the mechanical contraction-relaxation activity caused by cardiac action potential. We present a robust two-step segmentation method for cardiomyocyte low-contrast image segmentation based on region and edge information. The segmented single-cell contains mostly the nucleus of the cell since it is the best part of the cardiac cell, which can be perfectly segmented. Clustering accuracy was assessed by a silhouette index evaluation for k-means clustering and the Dice similarity coefficient (DSC) of the final segmented image. 3D representation of single of cardiomyocytes. The cell contains mostly the nucleus section and a small area of cytoplasm.

Wavelength-multiplexed digital holography for quantitative phase measurement using quantum dot film

We propose an enhanced quantitative three-dimensional measurement system using wavelength-multiplexed digital holography. To simplify the configuration, a dual-peak quantum dot wavelength converter, combined with a blue LED, is adapted as a single low-coherence light source. Rather than a conventional dual-wavelength method, which records and reconstruct the object wave for each wavelength, the proposed system can capture the holograms of two wavelengths simultaneously with fewer acquisitions, simple setup, and low noise. To verify the system's performance, the measurements of the step height sample are presented.

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.

Dual-wavelength off-axis digital holography using a single light-emitting diode

We propose a new low-coherence interferometry system for dual-wavelength off-axis digital holography. By utilizing diffraction gratings, two beams with narrower bandwidths and different center wavelengths could be filtered in a single light-emitting diode. The characteristics of the system are analytically determined to extend the coherence length and field-of-view enough for off-axis configuration. The proposed system enables the fast and accurate measurement of the surface profile with more than a micrometer step height and less noise. The performance of the system is verified by the experimental results of a standard height sample.

Automated red blood cells extraction from holographic images using fully convolutional neural networks

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm.

Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes

We present unsupervised clustering methods for automatic grouping of human red blood cells (RBCs) extracted from RBC quantitative phase images obtained by digital holographic microscopy into three RBC clusters with regular shapes, including biconcave, stomatocyte, and sphero-echinocyte. We select some good features related to the RBC profile and morphology, such as RBC average thickness, sphericity coefficient, and mean corpuscular volume, and clustering methods, including density-based spatial clustering applications with noise, k-medoids, and k-means, are applied to the set of morphological features. The clustering results of RBCs using a set of three-dimensional features are compared against a set of two-dimensional features. Our experimental results indicate that by utilizing the introduced set of features, two groups of biconcave RBCs and old RBCs (suffering from the sphero-echinocyte process) can be perfectly clustered. In addition, by increasing the number of clusters, the three RBC types can be effectively clustered in an automated unsupervised manner with high accuracy. The performance evaluation of the clustering techniques reveals that they can assist hematologists in further diagnosis.

Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements

High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rate, without any computationally-intensive, post-acquisition processing. We also detail how the method can be expanded to bacterial cell segmentation with 98% success rates with substantially reduced processing requirements in comparison to existing methods. We attribute this improved performance to the remarkably uniform background, elimination of cell-to-cell and intracellular optical artifacts, and enhanced signal-to-background ratio-all innate properties of imaging in the optical-phase domain. © 2017 International Society for Advancement of Cytometry.

Automatic extraction of cell nuclei from H&E-stained histopathological images

Extraction of cell nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an essential preprocessing step in computerized image analysis for disease detection, diagnosis, and prognosis. We present an automated cell nuclei segmentation approach that works with H&E-stained images. A color deconvolution algorithm was first applied to the image to get the hematoxylin channel. Using a morphological operation and thresholding technique on the hematoxylin channel image, candidate target nuclei and background regions were detected, which were then used as markers for a marker-controlled watershed transform segmentation algorithm. Moreover, postprocessing was conducted to split the touching nuclei. For each segmented region from the previous steps, the regional maximum value positions were identified as potential nuclei centers. These maximum values were further grouped into [Formula: see text]-clusters, and the locations within each cluster were connected with the minimum spanning tree technique. Then, these connected positions were utilized as new markers for a watershed segmentation approach. The final number of nuclei at each region was determined by minimizing an objective function that iterated all of the possible [Formula: see text]-values. The proposed method was applied to the pathological images of the tumor tissues from The Cancer Genome Atlas study. Experimental results show that the proposed method can lead to promising results in terms of segmentation accuracy and separation of touching nuclei.

Lossless and lossy compression of quantitative phase images of red blood cells obtained by digital holographic imaging

In this paper, we evaluate lossless and lossy compression techniques to compress quantitative phase images of red blood cells (RBCs) obtained by an off-axis digital holographic microscopy (DHM). The RBC phase images are numerically reconstructed from their digital holograms and are stored in 16-bit unsigned integer format. In the case of lossless compression, predictive coding of JPEG lossless (JPEG-LS), JPEG2000, and JP3D are evaluated, and compression ratio (CR) and complexity (compression time) are compared against each other. It turns out that JP2k can outperform other methods by having the best CR. In the lossy case, JP2k and JP3D with different CRs are examined. Because some data is lost in a lossy way, the degradation level is measured by comparing different morphological and biochemical parameters of RBC before and after compression. Morphological parameters are volume, surface area, RBC diameter, sphericity index, and the biochemical cell parameter is mean corpuscular hemoglobin (MCH). Experimental results show that JP2k outperforms JP3D not only in terms of mean square error (MSE) when CR increases, but also in compression time in the lossy compression way. In addition, our compression results with both algorithms demonstrate that with high CR values the three-dimensional profile of RBC can be preserved and morphological and biochemical parameters can still be within the range of reported values.

Investigation on dynamics of red blood cells through their behavior as biophotonic lenses

The possibility to adopt biological matter as photonic optical elements can open scenarios in biophotonics research. Recently, it has been demonstrated that a red blood cell (RBC) can be seen as an optofluidic microlens by showing its imaging capability as well as its focal tunability. Moreover, correlation between an RBC’s morphology and its behavior as a refractive optical element has been established and its exploitation for biomedical diagnostic purposes has been foreseen. In fact, any deviation from the healthy RBC morphology can be seen as additional aberration in the optical wavefront passing through the cell. By this concept, accurate localization of focal spots of RBCs can become very useful in the blood disorders identification. We investigate the three-dimensional positioning of such focal spots over time for samples with two different osmolarity conditions, i.e., when they assume discocyte and spherical shapes, respectively. We also demonstrate that a temporal variation of an RBC’s focal points along the optical axis is correlated to the temporal fluctuations in the RBC’s thickness maps. Furthermore, we show a sort of synchronization of the whole erythrocytes ensemble.

Human red blood cell recognition enhancement with three-dimensional morphological features obtained by digital holographic imaging

The classification of erythrocytes plays an important role in the field of hematological diagnosis, specifically blood disorders. Since the biconcave shape of red blood cell (RBC) is altered during the different stages of hematological disorders, we believe that the three-dimensional (3-D) morphological features of erythrocyte provide better classification results than conventional two-dimensional (2-D) features. Therefore, we introduce a set of 3-D features related to the morphological and chemical properties of RBC profile and try to evaluate the discrimination power of these features against 2-D features with a neural network classifier. The 3-D features include erythrocyte surface area, volume, average cell thickness, sphericity index, sphericity coefficient and functionality factor, MCH and MCHSD, and two newly introduced features extracted from the ring section of RBC at the single-cell level. In contrast, the 2-D features are RBC projected surface area, perimeter, radius, elongation, and projected surface area to perimeter ratio. All features are obtained from images visualized by off-axis digital holographic microscopy with a numerical reconstruction algorithm, and four categories of biconcave (doughnut shape), flat-disc, stomatocyte, and echinospherocyte RBCs are interested. Our experimental results demonstrate that the 3-D features can be more useful in RBC classification than the 2-D features. Finally, we choose the best feature set of the 2-D and 3-D features by sequential forward feature selection technique, which yields better discrimination results. We believe that the final feature set evaluated with a neural network classification strategy can improve the RBC classification accuracy.

Cell morphology-based classification of red blood cells using holographic imaging informatics

We present methods that automatically select a linear or nonlinear classifier for red blood cell (RBC) classification by analyzing the equality of the covariance matrices in Gabor-filtered holographic images. First, the phase images of the RBCs are numerically reconstructed from their holograms, which are recorded using off-axis digital holographic microscopy (DHM). Second, each RBC is segmented using a marker-controlled watershed transform algorithm and the inner part of the RBC is identified and analyzed. Third, the Gabor wavelet transform is applied to the segmented cells to extract a series of features, which then undergo a multivariate statistical test to evaluate the equality of the covariance matrices of the different shapes of the RBCs using selected features. When these covariance matrices are not equal, a nonlinear classification scheme based on quadratic functions is applied; otherwise, a linear classification is applied. We used the stomatocyte, discocyte, and echinocyte RBC for classifier training and testing. Simulation results demonstrated that 10 of the 14 RBC features are useful in RBC classification. Experimental results also revealed that the covariance matrices of the three main RBC groups are not equal and that a nonlinear classification method has a much lower misclassification rate. The proposed automated RBC classification method has the potential for use in drug testing and the diagnosis of RBC-related diseases.

Automated tracking of temporal displacements of a red blood cell obtained by time-lapse digital holographic microscopy

Red blood cell (RBC) phase images that are numerically reconstructed by digital holographic microscopy (DHM) can describe the cell structure and dynamics information beneficial for a quantitative analysis of RBCs. However, RBCs investigated with time-lapse DHM undergo temporal displacements when their membranes are loosely attached to the substrate during sedimentation on a glass surface or due to the microscope drift. Therefore, we need to develop a tracking algorithm to localize the same RBC among RBC image sequences and dynamically monitor its biophysical cell parameters; this information is helpful for studies on RBC-related diseases and drug tests. Here, we propose a method, which is a combination of the mean-shift algorithm and Kalman filter, to track a single RBC and demonstrate that the optical path length of the single RBC can be continually extracted from the tracked RBC. The Kalman filter is utilized to predict the target RBC position in the next frame. Then, the mean-shift algorithm starts execution from the predicted location, and a robust kernel, which is adaptive to changes in the RBC scale, shape, and direction, is designed to improve the accuracy of the tracking. Finally, the tracked RBC is segmented and parameters such as the RBC location are extracted to update the Kalman filter and the kernel function for mean-shift tracking; the characteristics of the target RBC are dynamically observed. Experimental results show the feasibility of the proposed algorithm.

Quantitative investigation of red blood cell three-dimensional geometric and chemical changes in the storage lesion using digital holographic microscopy

Quantitative phase information obtained by digital holographic microscopy (DHM) can provide new insight into the functions and morphology of single red blood cells (RBCs). Since the functionality of a RBC is related to its three-dimensional (3-D) shape, quantitative 3-D geometric changes induced by storage time can help hematologists realize its optimal functionality period. We quantitatively investigate RBC 3-D geometric changes in the storage lesion using DHM. Our experimental results show that the substantial geometric transformation of the biconcave-shaped RBCs to the spherocyte occurs due to RBC storage lesion. This transformation leads to progressive loss of cell surface area, surface-to-volume ratio, and functionality of RBCs. Furthermore, our quantitative analysis shows that there are significant correlations between chemical and morphological properties of RBCs.

Microscopic images dataset for automation of RBCs counting

A method for Red Blood Corpuscles (RBCs) counting has been developed using RBCs light microscopic images and Matlab algorithm. The Dataset consists of Red Blood Corpuscles (RBCs) images and there RBCs segmented images. A detailed description using flow chart is given in order to show how to produce RBCs mask. The RBCs mask was used to count the number of RBCs in the blood smear image.

Red blood cell as an adaptive optofluidic microlens

The perspective of using live cells as lenses could open new revolutionary and intriguing scenarios in the future of biophotonics and biomedical sciences for endoscopic vision, local laser treatments via optical fibres and diagnostics. Here we show that a suspended red blood cell (RBC) behaves as an adaptive liquid-lens at microscale, thus demonstrating its imaging capability and tunable focal length. In fact, thanks to the intrinsic elastic properties, the RBC can swell up from disk volume of 90 fl up to a sphere reaching 150 fl, varying focal length from negative to positive values. These live optofluidic lenses can be fully controlled by triggering the liquid buffer's chemistry. Real-time accurate measurement of tunable focus capability of RBCs is reported through dynamic wavefront characterization, showing agreement with numerical modelling. Moreover, in analogy to adaptive optics testing, blood diagnosis is demonstrated by screening abnormal cells through focal-spot analysis applied to an RBC ensemble as a microlens array.

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.

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: a quantitative label-free microscopy technique for phenotypic screening

Digital Holographic Microscopy (DHM) is a label-free imaging technique allowing visualization of transparent cells with classical imaging cell culture plates. The quantitative DHM phase contrast image provided is related both to the intracellular refractive index and to cell thickness.

DHM is able to distinguish cellular morphological changes on two representative cell lines (HeLa and H9c2) when treated with doxorubicin and chloroquine, two cytotoxic compounds yielding distinct phenotypes. We analyzed parameters linked to cell morphology and to the intracellular content in endpoint measurements and further investigated them with timelapse recording. The results obtained by DHM were compared with other optical label-free microscopy techniques, namely Phase Contrast, Differential Interference Contrast and Transport of Intensity Equation (reconstructed from three bright-field images). For comparative purposes, images were acquired in a common 96-well plate format on the different motorized microscopes.

In contrast to the other microscopies assayed, images generated with DHM can be easily quantified using a simple automatized on-the-fly analysis method for discriminating the different phenotypes generated in each cell line. The DHM technology is suitable for the development of robust and unbiased image-based assays.

Automated quantitative analysis of 3D morphology and mean corpuscular hemoglobin in human red blood cells stored in different periods

Quantitative phase (QP) images of red blood cells (RBCs), which are obtained by off-axis digital holographic microscopy, can provide quantitative information about three-dimensional (3D) morphology of human RBCs and the characteristic properties such as mean corpuscular hemoglobin (MCH) and MCH surface density (MCHSD). In this paper, we investigate modifications of the 3D morphology and MCH in RBCs induced by the period of storage time for the purpose of classification of RBCs with different periods of storage by using off-axis digital holographic microscopy. The classification of RBCs based on the duration of storage is highly relevant because a long storage of blood before transfusion may alter the functionality of RBCs and, therefore, cause complications in patients. To analyze any changes in the 3D morphology and MCH of RBCs due to storage, we use data sets from RBC samples stored for 8, 13, 16, 23, 27, 30, 34, 37, 40, 47, and 57 days, respectively. The data sets consist of more than 3,300 blood cells in eleven classes, with more than 300 blood cells per class. The classes indicate the storage period of RBCs and are listed in chronological order. Using the RBCs donated by healthy persons, the off-axis digital holographic microscopy reconstructs several quantitative phase images of RBC samples stored for eleven different periods. We employ marker-controlled watershed transform to remove the background in the RBC quantitative phase images obtained by the off-axis digital holographic microscopy. More than 300 single RBCs are extracted from the segmented quantitative phase images for each class. Such a large number of RBC samples enable us to obtain statistical distributions of the characteristic properties of RBCs after a specific period of storage. Experimental results show that the 3D morphology of the RBCs, in contrast to MCH, is essentially related to the aging of the RBCs.

Extraction of target specimens from bioholographic images using interactive graph cuts

It is necessary to extract target specimens from bioholographic images for high-level analysis such as object identification, recognition, and tracking with the advent of application of digital holographic microscopy to transparent or semi-transparent biological specimens. We present an interactive graph cuts approach to segment the needed target specimens in the reconstructed bioholographic images. This method combines both regional and boundary information and is robust to extract targets with weak boundaries. Moreover, this technique can achieve globally optimal results while minimizing an energy function. We provide a convenient user interface, which can easily differentiate the foreground/background for various types of holographic images, as well as a dynamically modified coefficient, which specifies the importance of the regional and boundary information. The extracted results from our scheme have been compared with those from an advanced level-set-based segmentation method using an unbiased comparison algorithm. Experimental results show that this interactive graph cut technique can not only extract different kinds of target specimens in bioholographic images, but also yield good results when there are multiple similar objects in the holographic image or when the object boundaries are very weak.