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

All-optical dual module platform for motility-based functional scrutiny of microencapsulated probiotic bacteria

Probiotic bacteria are widely used in pharmaceutics to offer health benefits. Microencapsulation is used to deliver probiotics into the human body. Capsules in the stomach have to keep bacteria constrained until release occurs in the intestine. Once outside, bacteria must maintain enough motility to reach the intestine walls. Here, we develop a platform based on two label-free optical modules for rapidly screening and ranking probiotic candidates in the laboratory. Bio-speckle dynamics assay tests the microencapsulation effectiveness by simulating the gastrointestinal transit. Then, a digital holographic microscope 3D-tracks their motility profiles at a single element level to rank the strains.

Blood cell image segmentation and classification: a systematic review

Background

Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc.

Methodology

This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs.

Results

Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively.

Conclusion

The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

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-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning

We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.

Algal cell viability assessment: The role of environmental factors in phytoplankton population dynamics

The viability of algal cells is one of the most fundamental issues in marine ecological research. In this work, a method was designed to identify algal cell viability based on digital holography and deep learning, which divided algal cells into three categories: active, weak, and dead cells. This method was applied to measure algal cells in surface waters of the East China Sea in spring, revealing about 4.34 %-23.29 % weak cells and 3.98 %-19.47 % dead cells. Levels of nitrate and chlorophyll a were the main factors affecting the viability of algal cells. Furthermore, algal viability changes during the heating and cooling were observed in laboratory experiments: high temperatures led to an increase in weak algal cells. This may provide an explanation for why most harmful algal blooms occur in warming months. This study provided a novel insight into how to identify the viability of algal cells and understand their significance in the ocean.

Leukemia segmentation and classification: A comprehensive survey

Blood is made up of leukocytes (WBCs), erythrocytes (RBCs), and thrombocytes. The ratio of blood cancer diseases is increasing rapidly, among which leukemia is one of the famous cancer which may lead to death. Leukemia cancer is initiated by the unnecessary growth of immature WBCs present in the sponge tissues of bone marrow. It is generally analyzed by etiologists by perceiving slides of blood smear images under a microscope. The morphological features and blood cells count facilitated the etiologists to detect leukemia. Due to the late detection and expensive instruments used for leukemia analysis, the death rate has risen significantly. The fluorescence-based cell sorting technique and manual recounts using a hemocytometer are error-prone and imprecise. Leukemia detection methods consist of pre-processing, segmentation, features extraction, and classification. In this article, recent deep learning methodologies and challenges for leukemia detection are discussed. These methods are helpful to examine the microscopic blood smears images and for the detection of leukemia more accurately.

Automatic whole blood cell analysis from blood smear using label-free multi-modal imaging with deep neural networks

Whole blood cell analysis is widely used in medical applications since its results are indicators for diagnosing a series of diseases. In this work, we report automatic whole blood cell analysis from blood smear using label-free multi-modal imaging with deep neural networks. First, a commercial microscope equipped with our developed Phase Real-time Microscope Camera (PhaseRMiC) obtains both bright-field and quantitative phase images. Then, these images are automatically processed by our designed blood smear recognition networks (BSRNet) that recognize erythrocytes, leukocytes and platelets. Finally, blood cell parameters such as counts, shapes and volumes can be extracted according to both quantitative phase images and automatic recognition results. The proposed whole blood cell analysis technique provides high-quality blood cell images and supports accurate blood cell recognition and analysis. Moreover, this approach requires rather simple and cost-effective setups as well as easy and rapid sample preparations. Therefore, this proposed method has great potential application in blood testing aiming at disease diagnostics.

Identification of Anemia and Its Severity Level in a Peripheral Blood Smear Using 3-Tier Deep Neural Network

The automatic detection of blood cell elements for identifying morphological deformities is still a challenging research domain. It has a pivotal role in cognition and detecting the severity level of disease. Using a simple microscope, manual disease detection, and morphological disorders in blood cells is mostly time-consuming and erroneous. Due to the overlapped structure of RBCs, pathologists face challenges in differentiating between normal and abnormal cell shape and size precisely. Currently, convolutional neural network-based algorithms are effective tools for addressing this issue. Existing techniques fail to provide effective anemia detection, and severity level prediction due to RBCs’ dense and overlapped structure, non-availability of standard datasets related to blood diseases, and severity level detection techniques. This work proposed a three tier deep convolutional fused network (3-TierDCFNet) to extract optimum morphological features and identify anemic images to predict the severity of anemia. The proposed model comprises two modules: Module-I classifies the input image into two classes, i.e., Healthy and Anemic, while Module-II detects the anemia severity level and categorizes it into Mild or Chronic. After each tier’s training, a validation function is employed to reduce the inappropriate feature selection. To authenticate the proposed model for healthy, anemic RBC classification and anemia severity level detection, a state-of-the-art anemic and healthy RBC dataset was developed in collaboration with Shaukat Khanum Hospital and Research Center (SKMCH&RC), Pakistan. To evaluate the proposed model, the training, validation, and test accuracies were computed along with recall, F1-Score, and specificity. The global results reveal that the proposed model achieved 91.37%, 88.85%, and 86.06% training, validation, and test accuracies with 98.95%, 98.12%, and 98.12% recall F1-Score and specificity, respectively.

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.

Gabor's holography: seven decades influencing optics [Invited]

Dennis Gabor's seminal idea of a simple all-optical setup aimed at reconstructing the object wavefront stored on a photographic plate gave birth a little over seven decades ago to the field of holography. In 1971 Gabor obtained the Nobel Prize in Physics for this invention. Still, the road in the early days after his two first papers on the subject was one full of obstacles, so his scientific and engineering contemporaries put his idea to rest for more than 10 years, until the invention of the laser. This fact made his holographic concept take off to new and unsuspected applications. This invited review paper is a homage to Dennis Gabor's 50th anniversary of his Nobel Prize accolade. For this purpose, the review departs from the typical common route, i.e., those written following a timeline fashion, and instead is written with the intent to cover only a few of the holography applications in optics while scanning the electromagnetic spectrum. In doing this, the authors are aware that other invited papers for this special issue will tackle other subjects not dealt with in this review non-timeline paper.

Red Blood Cell Classification Based on Attention Residual Feature Pyramid Network

Clinically, red blood cell abnormalities are closely related to tumor diseases, red blood cell diseases, internal medicine, and other diseases. Red blood cell classification is the key to detecting red blood cell abnormalities. Traditional red blood cell classification is done manually by doctors, which requires a lot of manpower produces subjective results. This paper proposes an Attention-based Residual Feature Pyramid Network (ARFPN) to classify 14 types of red blood cells to assist the diagnosis of related diseases. The model performs classification directly on the entire red blood cell image. Meanwhile, a spatial attention mechanism and channel attention mechanism are combined with residual units to improve the expression of category-related features and achieve accurate extraction of features. Besides, the RoI align method is used to reduce the loss of spatial symmetry and improve classification accuracy. Five hundred and eighty eight red blood cell images are used to train and verify the effectiveness of the proposed method. The Channel Attention Residual Feature Pyramid Network (C-ARFPN) model achieves an mAP of 86%; the Channel and Spatial Attention Residual Feature Pyramid Network (CS-ARFPN) model achieves an mAP of 86.9%. The experimental results indicate that our method can classify more red blood cell types and better adapt to the needs of doctors, thus reducing the doctor's time and improving the diagnosis efficiency.

Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.

Identification of living and dead microalgae cells with digital holography and verified in the East China Sea

The death of microalgae plays an important role in ocean energy flow and material circulation. The existing methods for the identification of dead and living microalgae cells all have defects such as the need for staining and pre-treatment. In this work, a label-free method to identify living and dead algae cells based on digital holography microscopy and machine learning was designed. At the stage of model training, ten feature vectors were extracted from the holograms, and twelve classification models of machine learning algorithm were trained. Compared with the staining method results, the accuracy of this method can reach 94.8%. At the stage of field verification, the death rate calculated by this method was also consistent with staining method. The method proposed in this paper provides a new method for the study of marine microalgae death which has the advantages of label-free, non-invasive, high accuracy and potential for in-situ application.

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.

Simultaneous measurements of a specimen quantitative-phase signal and its surrounding medium refractive index using quantitative-phase imaging

This Letter demonstrates a method to simultaneously measure the quantitative-phase signal (QPS) of the observed specimen and the refractive index of its surrounding medium (n_m) in a time-resolved manner using a micro-structured coverslip. Such coverslips, easily integrated into perfused live-cell imaging chambers, allow to use various quantitative-phase imaging techniques to achieve this dual measurement. Since QPS is crucially dependent on n_m, the measurement of the latter paves the way for its manipulation in a controlled manner leading to a QPS contrast modulation for appealing applications, including visualizing the interior of cells.

Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition

SIGNIFICANCE

Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification.

AIM

We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes.

APPROACH

The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images.

RESULTS

In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes.

CONCLUSIONS

Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.

Deep learning-based cell identification and disease diagnosis using spatio-temporal cellular dynamics in compact digital holographic microscopy

We demonstrate a successful deep learning strategy for cell identification and disease diagnosis using spatio-temporal cell information recorded by a digital holographic microscopy system. Shearing digital holographic microscopy is employed using a low-cost, compact, field-portable and 3D-printed microscopy system to record video-rate data of live biological cells with nanometer sensitivity in terms of axial membrane fluctuations, then features are extracted from the reconstructed phase profiles of segmented cells at each time instance for classification. The time-varying data of each extracted feature is input into a recurrent bi-directional long short-term memory (Bi-LSTM) network which learns to classify cells based on their time-varying behavior. Our approach is presented for cell identification between the morphologically similar cases of cow and horse red blood cells. Furthermore, the proposed deep learning strategy is demonstrated as having improved performance over conventional machine learning approaches on a clinically relevant dataset of human red blood cells from healthy individuals and those with sickle cell disease. The results are presented at both the cell and patient levels. To the best of our knowledge, this is the first report of deep learning for spatio-temporal-based cell identification and disease detection using a digital holographic microscopy system.

Common-path phase-shift microscope based on measurement of Stokes parameters S2 and S3 for 3D phase extraction

In this paper, we report a common-path, phase-shift optical microscope based on measurement of Stokes parameters S₂ and S₃ to extract the three-dimensional (3D) phase map of transparent objects with high precision. The microscope employs three polarizers and two identical quarter-wave plates to extract S₂ and S₃. The reference phase in the absence of the object is subtracted from the total phase in the presence of the object to extract the 3D phase of the object. The microscope is tested on imaging a USAF resolution test target and a reticle test pattern with excellent results. To the best of our knowledge, this is the first report of a common-path phase-shift optical microscope for 3D phase extraction based on measurement of Stokes parameters S₂ and S₃.

Deep learning-based hologram generation using a white light source

Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3-5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.

Sub-nanometer height sensitivity by phase shifting interference microscopy under environmental fluctuations

Phase shifting interferometric (PSI) techniques are among the most sensitive phase measurement methods. Owing to its high sensitivity, any minute phase change caused due to environmental instability results into, inaccurate phase measurement. Consequently, a well calibrated piezo electric transducer (PZT) and highly-stable environment is mandatory for measuring accurate phase map using PSI implementation. Here, we present an inverse approach, which can retrieve phase maps of the samples with negligible errors under environmental fluctuations. The method is implemented by recording a video of continuous temporally phase shifted interferograms and phase shifts were calculated between all the data frames using Fourier transform algorithm with a high accuracy ≤ 5.5 × 10^-4 π rad. To demonstrate the robustness of the proposed method, a manual translation of the stage was employed to introduce continuous temporal phase shift between data frames. The developed algorithm is first verified by performing quantitative phase imaging of optical waveguide and red blood cells using uncalibrated PZT under the influence of vibrations/air turbulence and compared with the well calibrated PZT results. Furthermore, we demonstrated the potential of the proposed approach by acquiring the quantitative phase imaging of an optical waveguide with a rib height of only 2 nm and liver sinusoidal endothelial cells (LSECs). By using 12-bit CMOS camera the height of shallow rib waveguide is measured with a height sensitivity of 4 Å without using PZT and in presence of environmental fluctuations.vn.

Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images

Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. The proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.

Immune cell type, cell activation, and single cell heterogeneity revealed by label-free optical methods

Measurement techniques that allow the global analysis of cellular responses while retaining single-cell sensitivity are increasingly needed in order to understand complex and dynamic biological processes. In this context, compromises between sensitivity, degree of multiplexing, throughput, and invasiveness are often unavoidable. We present here a noninvasive optical approach that can retrieve quantitative biomarkers of both morphological and molecular phenotypes of individual cells, based on a combination of quantitative phase imaging and Raman spectroscopy measurements. We then develop generalized statistical tools to assess the influence of both controlled (cell sub-populations, immune stimulation) and uncontrolled (culturing conditions, animal variations, etc.) experimental parameters on the label-free biomarkers. These indicators can detect different macrophage cell sub-populations originating from different progenitors as well as their activation state, and how these changes are related to specific differences in morphology and molecular content. The molecular indicators also display further sensitivity that allow identification of other experimental conditions, such as differences between cells originating from different animals, allowing the detection of outlier behaviour from given cell sub-populations.

Structured illumination in compact and field-portable 3D-printed shearing digital holographic microscopy for resolution enhancement

A compact and field-portable three-dimensional (3D)-printed structured illumination (SI) digital holographic microscope based on shearing geometry is presented. By illuminating the sample using a SI pattern, the lateral resolution in both reconstructed phase and amplitude images can be improved up to twice the resolution provided by conventional illumination. The use of a 3D-printed system and shearing geometry reduces the complexity of the system, while providing high temporal stability. The experimental results for the USAF resolution target show a resolution improvement of a factor of two which corroborates the theoretical prediction. Resolution enhancement in phase imaging is also demonstrated by imaging a biological sample. To the best of our knowledge, this is the first report of a compact and field-portable SI digital holographic system based on shearing geometry.

Learning-based automatic sensing and size classification of microparticles using smartphone holographic microscopy

A microparticle classifier is established by synergetic integration of smartphone-based digital in-line holographic microscopy and supervised machine learning.

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.

Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells

Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality is essential for healthcare in developing countries that lack the expertise of trained microscopists. In the present study, a new automatic sensing method using digital in-line holographic microscopy (DIHM) combined with machine learning algorithms was proposed to sensitively detect unstained malaria-infected red blood cells (iRBCs). To identify the RBC characteristics, 13 descriptors were extracted from segmented holograms of individual RBCs. Among the 13 descriptors, 10 features were highly statistically different between healthy RBCs (hRBCs) and iRBCs. Six machine learning algorithms were applied to effectively combine the dominant features and to greatly improve the diagnostic capacity of the present method. Among the classification models trained by the 6 tested algorithms, the model trained by the support vector machine (SVM) showed the best accuracy in separating hRBCs and iRBCs for training (n = 280, 96.78%) and testing sets (n = 120, 97.50%). This DIHM-based artificial intelligence methodology is simple and does not require blood staining. Thus, it will be beneficial and valuable in the diagnosis of malaria.

Sickle cell disease diagnosis based on spatio-temporal cell dynamics analysis using 3D printed shearing digital holographic microscopy

We present a spatio-temporal analysis of cell membrane fluctuations to distinguish healthy patients from patients with sickle cell disease. A video hologram containing either healthy red blood cells (h-RBCs) or sickle cell disease red blood cells (SCD-RBCs) was recorded using a low-cost, compact, 3D printed shearing interferometer. Reconstructions were created for each hologram frame (time steps), forming a spatio-temporal data cube. Features were extracted by computing the standard deviations and the mean of the height fluctuations over time and for every location on the cell membrane, resulting in two-dimensional standard deviation and mean maps, followed by taking the standard deviations of these maps. The optical flow algorithm was used to estimate the apparent motion fields between subsequent frames (reconstructions). The standard deviation of the magnitude of the optical flow vectors across all frames was then computed. In addition, seven morphological cell (spatial) features based on optical path length were extracted from the cells to further improve the classification accuracy. A random forest classifier was trained to perform cell identification to distinguish between SCD-RBCs and h-RBCs. To the best of our knowledge, this is the first report of machine learning assisted cell identification and diagnosis of sickle cell disease based on cell membrane fluctuations and morphology using both spatio-temporal and spatial analysis.

Noninvasive detection of macrophage activation with single-cell resolution through machine learning

We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.

Morphometric analysis of erythrocytes from patients with thalassemia using tomographic diffractive microscopy

Complete blood count is the most common test to detect anemia, but it is unable to obtain the abnormal shape of erythrocytes, which highly correlates with the hematologic function. Tomographic diffractive microscopy (TDM) is an emerging technique capable of quantifying three-dimensional (3-D) refractive index (RI) distributions of erythrocytes without labeling. TDM was used to characterize optical and morphological properties of 172 erythrocytes from healthy volunteers and 419 erythrocytes from thalassemic patients. To efficiently extract and analyze the properties of erythrocytes, we developed an adaptive region-growing method for automatically delineating erythrocytes from 3-D RI maps. The thalassemic erythrocytes not only contained lower hemoglobin content but also showed doughnut shape and significantly lower volume, surface area, effective radius, and average thickness. A multi-indices prediction model achieved perfect accuracy of diagnosing thalassemia using four features, including the optical volume, surface-area-to-volume ratio, sphericity index, and surface area. The results demonstrate the ability of TDM to provide quantitative, hematologic measurements and to assess morphological features of erythrocytes to distinguish healthy and thalassemic erythrocytes.

Automated classification of cell morphology by coherence-controlled holographic microscopy

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

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.

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.