Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images.

The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics.

Automated Dual-Mode Cell Monitoring To Simultaneously Explore Calcium Dynamics and Contraction-Relaxation Kinetics within Drug-Treated Stem Cell-Derived Cardiomyocytes.

This manuscript proposes a new dual-mode cell imaging system for studying the relationships between calcium dynamics and the contractility process of cardiomyocytes derived from human-induced pluripotent stem cells. Practically, this dual-mode cell imaging system provides simultaneously both live cell calcium imaging and quantitative phase imaging based on digital holographic microscopy. Specifically, thanks to the development of a robust automated image analysis, simultaneous measurements of both intracellular calcium, a key player of excitation-contraction coupling, and the quantitative phase image-derived dry mass redistribution, reflecting the effective contractility, namely, the contraction and relaxation processes, were achieved. Practically, the relationships between calcium dynamics and the contraction-relaxation kinetics were investigated in particular through the application of two drugs─namely, isoprenaline and E-4031─known to act precisely on calcium dynamics. Specifically, this new dual-mode cell imaging system enabled us to establish that calcium regulation can be divided into two phases, an early phase influencing the occurrence of the relaxation process followed by a late phase, which although not having a significant influence on the relaxation process affects significantly the beat frequency. In combination with cutting-edge technologies allowing the generation of human stem cell-derived cardiomyocytes, this dual-mode cell monitoring approach therefore represents a very promising technique, particularly in the fields of drug discovery and personalized medicine, to identify compounds likely to act more selectively on specific steps that compose the cardiomyocyte contractility.

Automated analysis of human cardiomyocytes dynamics with holographic image-based tracking for cardiotoxicity screening.

This paper proposes a new non-invasive, low-cost, and fully automated platform to quantitatively analyze dynamics of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) at the single-cell level by holographic image-based tracking for cardiotoxicity screening. A dense Farneback optical flow method and holographic imaging informatics were combined to characterize the contractile motion of a single CM, which obviates the need for costly equipment to monitor a CM's mechanical beat activity. The reliability of the proposed platform was tested by single-cell motion characterization, synchronization analysis, motion speed measurement of fixed CMs versus live CMs, and noise sensitivity. The applicability of the motion characterization method was tested to determine the pharmacological effects of two cardiovascular drugs, isoprenaline (166 nM) and E-4031 (500 µM). The experiments were done using single CMs and multiple cells, and the results were compared to control conditions. Cardiomyocytes responded to isoprenaline by increasing the action potential (AP) speed and shortening the resting period, thus increasing the beat frequency. In the presence of E-4031, the AP speed was decreased, and the resting period was prolonged, thus decreasing the beat frequency. The findings offer insights into single hiPS-CMs' contractile motion and a deep understanding of their kinetics at the single-cell level for cardiotoxicity screening.

Deep Learning-Based Phenotypic Assessment of Red Cell Storage Lesions for Safe Transfusions.

This study presents a novel approach to automatically perform instant phenotypic assessment of red blood cell (RBC) storage lesion in phase images obtained by digital holographic microscopy. The proposed model combines a generative adversarial network (GAN) with marker-controlled watershed segmentation scheme. The GAN model performed RBC segmentations and classifications to develop ageing markers, and the watershed segmentation was used to completely separate overlapping RBCs. Our approach achieved good segmentation and classification accuracy with a Dice's coefficient of 0.94 at a high throughput rate of about 152 cells per second. These results were compared with other deep neural network architectures. Moreover, our image-based deep learning models recognized the morphological changes that occur in RBCs during storage. Our deep learning-based classification results were in good agreement with previous findings on the changes in RBC markers (dominant shapes) affected by storage duration. We believe that our image-based deep learning models can be useful for automated assessment of RBC quality, storage lesions for safe transfusions, and diagnosis of RBC-related diseases.

Automated phase unwrapping in digital holography with deep learning.

Digital holography can provide quantitative phase images related to the morphology and content of biological samples. After the numerical image reconstruction, the phase values are limited between -π and π; thus, discontinuity may occur due to the modulo 2π operation. We propose a new deep learning model that can automatically reconstruct unwrapped focused-phase images by combining digital holography and a Pix2Pix generative adversarial network (GAN) for image-to-image translation. Compared with numerical phase unwrapping methods, the proposed GAN model overcomes the difficulty of accurate phase unwrapping due to abrupt phase changes and can perform phase unwrapping at a twice faster rate. We show that the proposed model can generalize well to different types of cell images and has high performance compared to recent U-net models. The proposed method can be useful in observing the morphology and movement of biological cells in real-time applications.

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.

Noise-free quantitative phase imaging in Gabor holography with conditional generative adversarial network.

This paper shows that deep learning can eliminate the superimposed twin-image noise in phase images of Gabor holographic setup. This is achieved by the conditional generative adversarial model (C-GAN), trained by input-output pairs of noisy phase images obtained from synthetic Gabor holography and the corresponding quantitative noise-free contrast-phase image obtained by the off-axis digital holography. To train the model, Gabor holograms are generated from digital off-axis holograms with spatial shifting of the real image and twin image in the frequency domain and then adding them with the DC term in the spatial domain. Finally, the digital propagation of the Gabor hologram with Fresnel approximation generates a super-imposed phase image for the C-GAN model input. Two models were trained: a human red blood cell model and an elliptical cancer cell model. Following the training, several quantitative analyses were conducted on the bio-chemical properties and similarity between actual noise-free phase images and the model output. Surprisingly, it is discovered that our model can recover other elliptical cell lines that were not observed during the training. Additionally, some misalignments can also be compensated with the trained model. Particularly, if the reconstruction distance is somewhat incorrect, this model can still retrieve in-focus images.

Automated single cardiomyocyte characterization by nucleus extraction from dynamic holographic images using a fully convolutional neural network.

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) beating can be efficiently characterized by time-lapse quantitative phase imaging (QPIs) obtained by digital holographic microscopy. Particularly, the CM's nucleus section can precisely reflect the associated rhythmic beating pattern of the CM suitable for subsequent beating pattern characterization. In this paper, we describe an automated method to characterize single CMs by nucleus extraction from QPIs and subsequent beating pattern reconstruction and quantification. However, accurate CM's nucleus extraction from the QPIs is a challenging task due to the variations in shape, size, orientation, and lack of special geometry. To this end, we propose a novel fully convolutional neural network (FCN)-based network architecture for accurate CM's nucleus extraction using pixel classification technique and subsequent beating pattern characterization. Our experimental results show that the beating profile of multiple extracted single CMs is less noisy and more informative compared to the whole image slide. Applying this method allows CM characterization at the single-cell level. Consequently, several single CMs are extracted from the whole slide QPIs and multiple parameters regarding their beating profile of each isolated CM are efficiently measured.

Quantitative analysis of three-dimensional morphology and membrane dynamics of red blood cells during temperature elevation.

The optimal functionality of red blood cells is closely associated with the surrounding environment. This study was undertaken to analyze the changes in membrane profile, mean corpuscular hemoglobin (MCH), and cell membrane fluctuations (CMF) of healthy red blood cells (RBC) at varying temperatures. The temperature was elevated from 17 °C to 41 °C within a duration of less than one hour, and the holograms were recorded by an off-axis configuration. After hologram reconstruction, we extracted single RBCs and evaluated their morphologically related features (projected surface area and sphericity coefficient), MCH, and CMF. We observed that elevating the temperature results in changes in the three-dimensional (3D) profile. Since CMF amplitude is highly correlated to the bending curvature of RBC membrane, temperature-induced shape changes can alter CMF's map and amplitude; mainly larger fluctuations appear on dimple area at a higher temperature. Regardless of the shape changes, no alterations in MCH were seen with temperature variation.

Secure storage and retrieval schemes for multiple encrypted digital holograms with orthogonal phase encoding multiplexing.

Recent developments in 3D computational optical imaging such as digital holographic microscopy has ushered in a new era for biological research. Therefore, efficient and secure storage and retrieval of digital holograms is a challenging task for future cloud computing services. In this study, we propose a novel scheme to securely store and retrieve multiple encrypted digital holograms by using phase encoding multiplexing. In the proposed schemes, an encrypted hologram can only be accessed using a binary phase mask, which is the key to retrieve the image. In addition, it is possible to independently store, retrieve, and manage the encrypted digital holograms without affecting other groups of the encrypted holograms multiplexed using different sets of binary phase masks, due to the orthogonality properties of the Hadamard matrices with high autocorrelation and low cross-correlation. The desired encrypted holograms may also be searched for, removed, and added independently of other groups of the encrypted holograms. More and more 3D images or digital holograms can be securely and efficiently stored, retrieved, and managed.

No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network.

Digital propagation of an off-axis hologram can provide the quantitative phase-contrast image if the exact distance between the sensor plane (such as CCD) and the reconstruction plane is correctly provided. In this paper, we present a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best reconstruction distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately predict the propagation distance from a filtered hologram. The result is compared with the conventional automatic focus-evaluation function. Additionally, our approach can be utilized at the single-cell level, which is useful for cell-to-cell depth measurement and cell adherent studies.

Automated quantification study of human cardiomyocyte synchronization using holographic imaging.

This paper investigates the rhythm strip and parameters of synchronization of human induced pluripotent stem cell (iPS) derived cardiomyocytes. The synchronization is evaluated from quantitative phase images of beating cardiomyocytes which are obtained using the time-lapse digital holographic imaging method. By quantitatively monitoring the dry mass redistribution, digital holography provides the physical contraction-relaxation signal caused by autonomous cardiac action potential. In order to analyze the synchronicity at the cell-to-cell level, we extracted single cardiac muscle cells, which contain the nuclei, from the phase images of cardiomyocytes containing multiple cells resulting from the fusion of k-means clustering and watershed segmentation algorithms. We demonstrate that mature cardiomyocyte cell synchronization can be automatically evaluated by time-lapse microscopic holographic imaging. Our proposed method can be applied for studies on cardiomyocyte disorders and drug safety testing systems.

Quantification of stored red blood cell fluctuations by time-lapse holographic cell imaging.

We propose methods to quantitatively calculate the fluctuation rate of red blood cells with nanometric axial and millisecond temporal sensitivity at the single-cell level by using time-lapse holographic cell imaging. For this quantitative analysis, cell membrane fluctuations (CMFs) were measured for RBCs stored at different storage times. Measurements were taken over the whole membrane for both the ring and dimple sections separately. The measurements show that healthy RBCs that maintain their discocyte shape become stiffer with storage time. The correlation analysis demonstrates a significant negative correlation between CMFs and the sphericity coefficient, which characterizes the morphological type of erythrocyte. In addition, we show the correlation results between CMFs and other morphological properties such as projected surface area, surface area, mean corpuscular volume, and mean corpuscular hemoglobin.

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.

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.

Three-dimensional image authentication scheme using sparse phase information in double random phase encoded integral imaging.

In recent years, many studies have focused on authentication of two-dimensional (2D) images using double random phase encryption techniques. However, there has been little research on three-dimensional (3D) imaging systems, such as integral imaging, for 3D image authentication. We propose a 3D image authentication scheme based on a double random phase integral imaging method. All of the 2D elemental images captured through integral imaging are encrypted with a double random phase encoding algorithm and only partial phase information is reserved. All the amplitude and other miscellaneous phase information in the encrypted elemental images is discarded. Nevertheless, we demonstrate that 3D images from integral imaging can be authenticated at different depths using a nonlinear correlation method. The proposed 3D image authentication algorithm can provide enhanced information security because the decrypted 2D elemental images from the sparse phase cannot be easily observed by the naked eye. Additionally, using sparse phase images without any amplitude information can greatly reduce data storage costs and aid in image compression and data transmission.

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.

Red blood cells ageing markers: a multi-parametric analysis.

BACKGROUND: Red blood cells collected in citrate-phosphate-dextrose can be stored for up to 42 days at 4 °C in saline-adenine-glucose-mannitol additive solution. During this controlled, but nevertheless artificial, ex vivo ageing, red blood cells accumulate lesions that can be reversible or irreversible upon transfusion. The aim of the present study is to follow several parameters reflecting cell metabolism, antioxidant defences, morphology and membrane dynamics during storage. MATERIALS AND METHODS: Five erythrocyte concentrates were followed weekly during 71 days. Extracellular glucose and lactate concentrations, total antioxidant power, as well as reduced and oxidised intracellular glutathione levels were quantified. Microvesiculation, percentage of haemolysis and haematologic parameters were also evaluated. Finally, morphological changes and membrane fluctuations were recorded using label-free digital holographic microscopy. RESULTS: The antioxidant power as well as the intracellular glutathione concentration first increased, reaching maximal values after one and two weeks, respectively. Irreversible morphological lesions appeared during week 5, where discocytes began to transform into transient echinocytes and finally spherocytes. At the same time, the microvesiculation and haemolysis started to rise exponentially. After six weeks (expiration date), intracellular glutathione was reduced by 25%, reflecting increasing oxidative stress. The membrane fluctuations showed decreased amplitudes during shape transition from discocytes to spherocytes. DISCUSSION: Various types of lesions accumulated at different chemical and cellular levels during storage, which could impact their in vivo recovery after transfusion. A marked effect was observed after four weeks of storage, which corroborates recent clinical data. The prolonged follow-up period allowed the capture of deep storage lesions. Interestingly, and as previously described, the severity of the changes differed among donors.

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.