Deep Learning-Based Nuclear Lobe Count Method for Differential Count of Neutrophils.

Differentiating neutrophils based on the count of nuclear lobulation is useful for diagnosing various hematological disorders, including megaloblastic anemia, myelodysplastic syndrome, and sepsis. It has been reported that one-fifth of sepsis-infected patients worldwide died between 1990 and 2017. Notably, fewer nuclear-lobed and stab-formed neutrophils develop in the peripheral blood during sepsis. This abnormality can serve as an early diagnostic criterion. However, testing this feature is a complex and time-consuming task that is rife with human error. For this reason, we apply deep learning to automatically differentiate neutrophil and nuclear lobulation counts and report the world's first small-scale pilot. Blood films are prepared using venous peripheral blood taken from four healthy volunteers and are stained with May-Grünwald Giemsa stain. Six-hundred 360 × 363-pixel images of neutrophils having five different nuclear lobulations are automatically captured by Cellavision DM-96, an automatic digital microscope camera. Images are input to an original architecture with five convolutional layers built on a deep learning neural-network platform by Sony, Neural Network Console. The deep learning system distinguishes the four groups (i.e., band-formed, two-, three-, and four- and five- segmented) of neutrophils with up to 99% accuracy, suggesting that neutrophils can be automatically differentiated based on their count of segmented nuclei using deep learning.

Automated Quantification of Human Osteoclasts Using Object Detection.

A balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated in vitro from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results.

Towards Automatic Detection of Precipitates in Inconel 625 Superalloy Additively Manufactured by the L-PBF Method.

In our study, the comparison of the automatically detected precipitates in L-PBF Inconel 625, with experimentally detected phases and with the results of the thermodynamic modeling was used to test their compliance. The combination of the complementary electron microscopy techniques with the microanalysis of chemical composition allowed us to examine the structure and chemical composition of related features. The possibility of automatic detection and identification of precipitated phases based on the STEM-EDS data was presented and discussed. The automatic segmentation of images and identifying of distinguishing regions are based on the processing of STEM-EDS data as multispectral images. Image processing methods and statistical tools are applied to maximize an information gain from data with low signal-to-noise ratio, keeping human interactions on a minimal level. The proposed algorithm allowed for automatic detection of precipitates and identification of interesting regions in the Inconel 625, while significantly reducing the processing time with acceptable quality of results.

[Multimodal imaging and evaluation in the age of artificial intelligence]. / Multimodale Bildgebung und -auswertung im Zeitalter von künstlicher Intelligenz.

Multimodal imaging is able to image the retina in unprecedented detail, and the joint analysis (integration) of these data not only enables the securing of diagnoses, but also a more precise definition; however, humans encounter temporal and cognitive limitations in the analysis of this amount of information, so that the potential of a joint examination of the findings is largely unused to date. Automatic image processing and methods, which are summarized under the collective term of artificial intelligence (AI), are able to overcome the bottleneck in the evaluation and to exploit the full potential of the available data. A basic understanding of AI methods and the ability to implement them will become increasingly more important for ophthalmologists in the future. In this article we give an insight into the functionality of AI methods and the current state of research in the field of automatic image analysis.

Convolutional Neural Networks-Based Image Analysis for the Detection and Quantification of Neutrophil Extracellular Traps.

Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infections. Currently applied methods for NETs release quantification are often limited by the use of unspecific dyes and technical difficulties. Therefore, we aimed to develop a fully automatic image processing method for the detection and quantification of NETs based on live imaging with the use of DNA-staining dyes. For this purpose, we adopted a recently proposed Convolutional Neural Network (CNN) model called Mask R-CNN. The adopted model detected objects with quality comparable to manual counting-Over 90% of detected cells were classified in the same manner as in manual labelling. Furthermore, the inhibitory effect of GW 311616A (neutrophil elastase inhibitor) on NETs release, observed microscopically, was confirmed with the use of the CNN model but not by extracellular DNA release measurement. We have demonstrated that a modern CNN model outperforms a widely used quantification method based on the measurement of DNA release and can be a valuable tool to quantitate the formation process of NETs.

A novel deep learning method for automatic assessment of human sperm images.

Sperm morphology analysis (SMA) is a very important factor in the diagnosis process of male infertility. This research proposes a novel deep learning algorithm for malformation detection of sperm morphology using human sperm cell images. Our proposed method detects and analyzes different parts of human sperms. First of all, we have prepared an image collection, called the MHSMA dataset, which can be used as a standard benchmark for future machine learning studies in this problem. This collection consists of 1,540 sperm images from 235 patients with male factor infertility. This unique dataset is freely available to the public. After applying data augmentation techniques, we have proposed a sampling method for fixing data imbalance. Then, we have designed a deep neural network architecture and trained it to detect morphological deformities in different parts of human sperm-head, acrosome, and vacuole. Our proposed method is one of the first algorithms that considers the acrosome. In addition, our method can work very well with non-stained and low-resolution images. Our experimental results on the proposed benchmark show the high accuracy of our deep learning algorithm for detection of morphological deformities from images. In these experiments, the proposed algorithm has achieved F0.5 scores of 84.74%, 83.86%, and 94.65% in acrosome, head, and vacuole abnormality detection, respectively. It should be noted that our algorithm achieves a better accuracy than existing state-of-the-art methods in acrosome and vacuole abnormality detection on the proposed benchmark. Also, our method works very fast. It can classify images in real-time, even on a mainstream laptop computer. This allows an embryologist to quickly decide whether or not the analyzed sperm should be selected.

Automatic Methods for Carotid Contrast-Enhanced Ultrasound Imaging Quantification of Adventitial Vasa Vasorum.

Adventitial vasa vasorum are physiologic microvessels that nourish artery walls. In the presence of cardiovascular risk factors, these microvessels proliferate abnormally. Studies have reported that they are the first stage of atheromatous disease. Contrast-enhanced ultrasound (CEUS) of the carotid allows direct, quantitative and non-invasive visualization of the adventitial vasa vasorum. Hence, the development of computer-assisted methods that speed image analysis and eliminate user subjectivity is important. We developed methods for automatic analyses and quantification of vasa vasorum neovascularization in CEUS and tested these methods in a cohort of 186 individuals, 63 of whom were healthy volunteers. We implemented alternative automatic strategies for using the images to stratify patients according to their risk group and compare the strategies with respect to diagnostic performance. An automatic single-parameter strategy performs less effectively than the corresponding Arcidiacono method based on manual interpretation of the images (68 < area under the receiver operating characteristic curve [AUROC] for the manual Arcidiacono method < 82; 60 < AUROC for the automatic single-parameter strategy < 63). However, by use of additional image parameters, an automatic multiparameter strategy has significantly improved performance with respect to the manual Arcidiacono method (78 < AUROC < 90). The automatic multiparameter strategy is a valuable alternative to the manual Arcidiacono method, improving both diagnostic speed and diagnostic accuracy.

Innovative unattended SEM-EDS analysis for asbestos fiber quantification.

Scanning electron microscopy with energy dispersive spectrometry (SEM-EDS) is the only affordable analytical technique that can discriminate both morphology and elemental composition of inorganic fibers. SEM-EDS is indeed required to quantify asbestos in confounding natural matrixes (e.g. ophiolites), but is also time-consuming, operator dependent, and strongly relies on the stochastic distribution of the fibers on the filter surface. The balance between analytical time/cost and the method sensibility allows only about 0.5% of the filter to be analyzed, strongly affecting the statistical significance of results. To improve sensitivity and precision and enhance productivity, an unattended quantitative measurement of the asbestos fibers by SEM-EDS is proposed. The method identifies the particle shape first and determines their chemical composition later, saving EDS analytical time. Our approach was tested on four asbestos standards and the relative error on replicated measurements was < 10%. The proposed unattended method quantifies asbestos in natural confounding matrix, also with a very low asbestos content.

Image Processing for Bioluminescence Resonance Energy Transfer Measurement-BRET-Analyzer.

A growing number of tools now allow live recordings of various signaling pathways and protein-protein interaction dynamics in time and space by ratiometric measurements, such as Bioluminescence Resonance Energy Transfer (BRET) Imaging. Accurate and reproducible analysis of ratiometric measurements has thus become mandatory to interpret quantitative imaging. In order to fulfill this necessity, we have developed an open source toolset for Fiji-BRET-Analyzer-allowing a systematic analysis, from image processing to ratio quantification. We share this open source solution and a step-by-step tutorial at https://github.com/ychastagnier/BRET-Analyzer. This toolset proposes (1) image background subtraction, (2) image alignment over time, (3) a composite thresholding method of the image used as the denominator of the ratio to refine the precise limits of the sample, (4) pixel by pixel division of the images and efficient distribution of the ratio intensity on a pseudocolor scale, and (5) quantification of the ratio mean intensity and standard variation among pixels in chosen areas. In addition to systematize the analysis process, we show that the BRET-Analyzer allows proper reconstitution and quantification of the ratiometric image in time and space, even from heterogeneous subcellular volumes. Indeed, analyzing twice the same images, we demonstrate that compared to standard analysis BRET-Analyzer precisely define the luminescent specimen limits, enlightening proficient strengths from small and big ensembles over time. For example, we followed and quantified, in live, scaffold proteins interaction dynamics in neuronal sub-cellular compartments including dendritic spines, for half an hour. In conclusion, BRET-Analyzer provides a complete, versatile and efficient toolset for automated reproducible and meaningful image ratio analysis.

Automatic detection of carotid arteries in computed tomography angiography: a proof of concept protocol.

Atherosclerosis is one of the leading causes of mortality in the western world. Computed tomography angiography (CTA) is the conventional imaging method used for pre-surgery assessment of the blood flow within the carotid vessel. In this paper, we present a proof of concept of a novel, fast and operator independent protocol for the automatic detection (seeding) of the carotid arteries in CTA in the thorax and upper neck region. The dataset is composed of 14 patients' CTA images of the neck region. The performance of this method is compared with manual seeding by four trained operators. Inter-operator variation is also assessed based on the dataset. The minimum, average and maximum coefficient of variation among the operators was (0, 2, 5 %), respectively. The performance of our method is comparable with the state of the art alternative, presenting a detection rate of 75 and 71 % for the lowest and uppermost image levels, respectively. The mean processing time is 167 s per patient versus 386 s for manual seeding. There are no significant differences between the manual and automatic seed positions in the volumes (p = 0.29). A fast, operator independent protocol was developed for the automatic detection of carotid arteries in CTA. The results are encouraging and provide the basis for the creation of automatic detection and analysis tools for carotid arteries.

Using CellProfiler for Automatic Identification and Measurement of Biological Objects in Images.

Visual analysis is required to perform many biological experiments, from counting colonies to measuring the size or fluorescence intensity of individual cells or organisms. This unit outlines the use of CellProfiler, a free, open-source image analysis tool that extracts quantitative information from biological images. It includes a step-by-step protocol for automated analysis of the number, color, and size of yeast colonies growing on agar plates, but the methods can be adapted to identify and measure many other types of objects in images. The flexibility of the software allows experimenters to create pipelines of adjustable modules to fit different biological experiments and to generate accurate measurements from dozens or even hundreds of thousands of images.

The modulatory effect of Chinese herb substances BP on apoptosis of lymphocytes in the elderly / 中国免疫学杂志

Objective:To study the changes of lymphocytes subpopulation and apoptosis process of lymphocytes in the elderly and to investigate the modulatory effect of BP from Chinese herb on apoptosis.Methods:Lymphocytes phenotype were determinated using indirect immunofluorescence technique.The proliferative responses were measured with MTT method.Apoptosis of lymphocytes was evaluated by flow cytometry and automatic image analysis.Results:The proliferative responses of lymphocytes in the elderly were lower than that of young adults.Decreased CD45RA positive cells and increased CD 45 RO positive cells were found in lymphocytes population of aged people compared to that of young adults. The CD 45 PO positive cells were prone to apoptosis.There is an imhibitory effect of BP on apoptosis of lymphocytes in the elderly.Conclusion:It was implicated that the susceptibility of lymphocyte in the elderly to apoptosis depends on activation,so called activation induced cell death.Present results suggested that apoptosis of lymphocytes of aged people play an important role in the pathogenesis of immunosenescence.The possibility appeared for development of modulatory drugs on apoptosis from Chinese herbs.