scholarly journals A deep convolutional neural network approach for astrocyte detection

2017 ◽  
Author(s):  
Ilida Suleymanova ◽  
Tamas Balassa ◽  
Sushil Tripathi ◽  
Csaba Molnar ◽  
Mart Saarma ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automatic Segmentation ◽  
Brain Regions ◽  
Cell Counting ◽  
Detection Accuracy ◽  
Cell Detection ◽  
Strong Positive Correlation ◽  
Deep Convolutional Neural Networks ◽  
Neural Network Approach ◽  
Microscopy Images

AbstractAstrocytes are involved in brain pathologies such as trauma or stroke, neurodegenerative disorders like Alzheimer’s and Parkinson’s disease, chronic pain, and many others. Determining cell density and timing of morphological and biochemical changes is important for a proper understanding of the role of astrocytes in physiological and pathological conditions. One of the most important of such analyses is astrocytes count within a complex tissue environment in microscopy images. The most widely used approaches for the quantification of microscopy images data are either manual stereological cell counting or semi-automatic segmentation techniques. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. In this study, we developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides detection accuracy and precision comparable to that of human experts. Additionally, the runtime of cell detection is significantly less than other three analyzed computational methods, and it is faster than human observers by orders of magnitude. We applied DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT) as morphine tolerance is believed to activate glial cells in the brain. We observed strong positive correlation between manual cell detection and DCNN-based analysis method for counting astrocytes in the brains of experimental animals.

scholarly journals Non-invasive single-cell morphometry in living bacterial biofilms

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Mingxing Zhang ◽  
Ji Zhang ◽  
Yibo Wang ◽  
Jie Wang ◽  
Alecia M. Achimovich ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Bacterial Cell ◽  
Cell Segmentation ◽  
Live Cells ◽  
Bacterial Biofilms ◽  
Cell Detection ◽  
Deep Convolutional Neural Networks ◽  
Segmentation Accuracy ◽  
Cell Shapes

AbstractFluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. Accurate cell detection and cellular shape measurement in densely packed biofilms are challenging because of the limited resolution and low signal to background ratios (SBRs) in fluorescence microscopy images. In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images. In BCM3D, deep convolutional neural networks (CNNs) are trained using simulated biofilm images with experimentally realistic SBRs, cell densities, labeling methods, and cell shapes. We systematically evaluate the segmentation accuracy of BCM3D using both simulated and experimental images. Compared to state-of-the-art bacterial cell segmentation approaches, BCM3D consistently achieves higher segmentation accuracy and further enables automated morphometric cell classifications in multi-population biofilms.

scholarly journals Non-Invasive Single-Cell Morphometry in Living Bacterial Biofilms

2020 ◽  
Author(s):  
Mingxing Zhang ◽  
Ji Zhang ◽  
Yibo Wang ◽  
Jie Wang ◽  
Alecia M. Achimovich ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Bacterial Cell ◽  
Cell Segmentation ◽  
Live Cells ◽  
Bacterial Biofilms ◽  
Cell Detection ◽  
Deep Convolutional Neural Networks ◽  
Segmentation Accuracy ◽  
Cell Shapes

AbstractFluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. Accurate cell detection and cellular shape measurement in densely packed biofilms are challenging because of the limited resolution and low signal to background ratios (SBRs) in fluorescence microscopy images. In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images. In BCM3D, deep convolutional neural networks (CNNs) are trained using simulated biofilm images with experimentally realistic SBRs, cell densities, labeling methods, and cell shapes. We systematically evaluate the segmentation accuracy of BCM3D using both simulated and experimental images. Compared to state-of-the-art bacterial cell segmentation approaches, BCM3D consistently achieves higher segmentation accuracy and further enables automated morphometric cell classifications in multi-population biofilms.

scholarly journals Automatic cell segmentation from brightfield microscopy images of pseudohyphal cell-aggregates

2019 ◽  
Author(s):  
Valdinei Luís Belini ◽  
Orides Morandin Junior ◽  
Sandra Regina Ceccato-Antonini ◽  
Philipp Wiedemann ◽  
Hajo Suhr
Keyword(s):  
Imaging System ◽  
Statistical Significance ◽  
Automatic Segmentation ◽  
Cell Counting ◽  
Cell Segmentation ◽  
Simultaneous Occurrence ◽  
Watershed Algorithm ◽  
Cell Aggregates ◽  
Brightfield Microscopy ◽  
Microscopy Images

Abstract Background: The automatic segmentation of pseudohyphal cell-aggregates from brightfield microscopy images for counting forming cells is a challenging task due to the heterogeneous optical appearances of the cells as they may lie on different focal planes. The current cell counting method is based on a time-consuming manual counting of stained cells on a hemocytometer and in most cases, it represents estimates of low statistical significance due to the effort needed to prepare and analyze many samples. In this work, we evaluated the effectiveness of a marker-controlled watershed algorithm for automatic segmentation of pseudohyphae from brightfield microscopic images. The cell heterogeneity problem was addressed by processing intracellular contents of focused and defocused cells to extract initial foreground markers for the watershed method. By properly segmenting cells of different classes within a pseudohypha allows increasing the number of cells analyzed contributing thus to more reliable estimates. To facilitate the evaluation of the proposal by acquiring images containing a diversity of cells´ appearances, we utilized in situ microscopy, an imaging system used to capture images directly from suspensions.Results: The performance of the method was evaluated on 120 portraits of a yeast exhibiting a diversity of pseudohyphal morphologies. Automatic results were compared with manual references obtained by visual inspection of the images. Despite the simultaneous occurrence of a representative mixture of focused, over-, and under-focused cells, the method produced robust results with an average segmentation sensitivity, specificity, and accuracy of 76%, 89%, and 76%, respectively. On average, each microscopic image was processed within 3 s.Conclusions: Our approach was capable to segment pseudohyphae formed by cells exhibiting a large diversity of appearances. The application of a marker-controlled watershed algorithm as a simple, yet effective technique for segmenting pseudohyphae demonstrated satisfactory overall performance to support automated analysis of pseudohyphal cell-aggregates from brightfield images.

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

1995 ◽  
Vol 53 ◽  
pp. 650-651
Author(s):  
D. E. Becker
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Laser Scanning ◽  
Cell Counting ◽  
Wide Area ◽  
Data Set ◽  
Computational Synthesis ◽  
Automated Cell Counting ◽  
Partial View ◽  
The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

1996 ◽  
Vol 54 ◽  
pp. 282-283
Author(s):  
Badrinath Roysam ◽  
Hakan Ancin ◽  
Douglas E. Becker ◽  
Robert W. Mackin ◽  
Matthew M. Chestnut ◽  
...  
Keyword(s):  
Image Analysis ◽  
Structural Changes ◽  
Sampling Methods ◽  
Spatial Clustering ◽  
Three Dimensional ◽  
Field Of View ◽  
Cell Counting ◽  
Depth Of Field ◽  
Tissue Cell ◽  
Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

scholarly journals Deeply-supervised density regression for automatic cell counting in microscopy images

2021 ◽  
Vol 68 ◽  
pp. 101892
Author(s):  
Shenghua He ◽  
Kyaw Thu Minn ◽  
Lilianna Solnica-Krezel ◽  
Mark A. Anastasio ◽  
Hua Li
Keyword(s):  
Cell Counting ◽  
Density Regression ◽  
Microscopy Images

SAU-Net: A Unified Network for Cell Counting in 2D and 3D Microscopy Images

2021 ◽  
pp. 1-1
Author(s):  
Yue Guo ◽  
Oleh Krupa ◽  
Jason Stein ◽  
Guorong Wu ◽  
Ashok Krishnamurthy
Keyword(s):  
Cell Counting ◽  
3D Microscopy ◽  
2D And 3D ◽  
Microscopy Images

scholarly journals Central Noradrenergic Neurotransmission and Weight Loss 6 Months After Gastric Bypass Surgery in Patients with Severe Obesity

2021 ◽  
Author(s):  
J. Marvin Soeder ◽  
Julia Luthardt ◽  
Michael Rullmann ◽  
Georg A. Becker ◽  
Mohammed K. Hankir ◽  
...  
Keyword(s):  
Weight Loss ◽  
Gastric Bypass ◽  
Severe Obesity ◽  
Occipital Cortex ◽  
Brain Regions ◽  
General Tendency ◽  
Strong Positive Correlation ◽  
Reference Tissue ◽  
Efficient Treatment ◽  
Post Surgery

Abstract Purpose Roux-en-Y gastric bypass (RYGB) surgery is currently the most efficient treatment to achieve long-term weight loss in individuals with severe obesity. This is largely attributed to marked reductions in food intake mediated in part by changes in gut-brain communication. Here, we investigated for the first time whether weight loss after RYGB is associated with alterations in central noradrenaline (NA) neurotransmission. Materials and Methods We longitudinally studied 10 individuals with severe obesity (8 females; age 43.9 ± 13.1 years; body mass index (BMI) 46.5 ± 4.8 kg/m2) using (S,S)-[11C]O-methylreboxetine and positron emission tomography to estimate NA transporter (NAT) availability before and 6 months after surgery. NAT distribution volume ratios (DVR) were calculated by volume-of-interest analysis and the two-parameter multilinear reference tissue model (reference region: occipital cortex). Results The participants responded to RYGB surgery with a reduction in BMI of 12.0 ± 3.5 kg/m2 (p < 0.001) from baseline. This was paralleled by a significant reduction in DVR in the dorsolateral prefrontal cortex (pre-surgery 1.12 ± 0.04 vs. post-surgery 1.07 ± 0.04; p = 0.019) and a general tendency towards reduced DVR throughout the brain. Furthermore, we found a strong positive correlation between pre-surgery DVR in hypothalamus and the change in BMI (r = 0.78; p = 0.01). Conclusion Reductions in BMI after RYGB surgery are associated with NAT availability in brain regions responsible for decision-making and homeostasis. However, these results need further validation in larger cohorts, to assess whether brain NAT availability could prognosticate the outcome of RYGB on BMI. Graphical abstract

scholarly journals Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

2021 ◽  
Author(s):  
Antonio Giovannetti ◽  
Gianluca Susi ◽  
Paola Casti ◽  
Arianna Mencattini ◽  
Sandra Pusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Classifiers ◽  
Deep Convolutional Neural Networks ◽  
Early Signs ◽  
Data Representations ◽  
The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

scholarly journals Characterization of Tumor-Associated Macrophages and the Immune Microenvironment in Limited-Stage Neuroendocrine-High and -Low Small Cell Lung Cancer

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 502
Author(s):  
David Dora ◽  
Christopher Rivard ◽  
Hui Yu ◽  
Shivaun Lueke Pickard ◽  
Viktoria Laszlo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Cell Counting ◽  
M2 Macrophage ◽  
Strong Positive Correlation ◽  
Small Cell Lung ◽  
Limited Stage ◽  
Macrophage Marker

This study aims to characterize tumor-infiltrating macrophages (TAMs), myeloid-derived suppressor cells (MDSC), and the related molecular milieu regulating anti-tumor immunity in limited-stage neuroendocrine (NE)-high and NE-low small cell lung cancer. Primary tumors and matched lymph node (LN) metastases of 32 resected, early-stage SCLC patients were analyzed by immunohistochemistry (IHC) with antibodies against pan-macrophage marker CD68, M2-macrophage marker CD163, and MDSC marker CD33. Area-adjusted cell counting on TMAs showed that TAMs are the most abundant cell type in the TME, and their number in tumor nests exceeds the number of CD3 + T-cells (64% vs. 38% in NE-low and 71% vs. 18% in NE-high). Furthermore, the ratio of CD163-expressing M2-polarized TAMs in tumor nests was significantly higher in NE-low vs. NE-high tumors (70% vs. 31%). TAM density shows a strong positive correlation with CD45 and CD3 in tumor nests, but not in the stroma. fGSEA analysis on a targeted RNAseq oncological panel of 2560 genes showed that NE-high tumors exhibited increased enrichment in pathways related to cell proliferation, whereas in NE-low tumors, immune response pathways were significantly upregulated. Interestingly, we identified a subset of NE-high tumors representing an immune-oasis phenotype, but with a different gene expression profile compared to NE-low tumors. In contrast, we found that a limited subgroup of NE-low tumors is immune-deserted and express distinct cellular pathways from NE-high tumors. Furthermore, we identified potential molecular targets based on our expression data in NE-low and immune-oasis tumor subsets, including CD70, ANXA1, ITGB6, TP63, IFI27, YBX3 and CXCR2.

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