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469 results for “Image analysis”
Finite element meshes of graphite foam samples for Image-Based Simulation (IBSim) of experimental laser flash analysis
<p>Image-Based Simulation (IBSim) meshes:<br> Finite element mesh of laser flash analysis (LFA) disc samples made of a graphite foam material (KFoam). The IBSim meshes are generated directly from a 3D volumetric image of a graphite foam block. The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Segmentation of the data into a binarized image was achieved with ImageJ. Conversion of the segmented data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The graphite foam has anisotropic properties partly due to its microstructure. This dataset contains three meshes, one for each alignment along cartesian axes.</p> <p>The FE meshes use the EnSight Gold file format and may be visualised using Paraview (<a href="https://www.paraview.org">https://www.paraview.org</a>).</p> <p>The CT data used for the mesh is available as a separate dataset:</p> <p>This data was used originally for the following publications (please cite if re-using the data):<br> Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p>
Analysis code for two-photon imaging data
<p>They are the analysis code in the manuscript 'Modulation of cortical representations of sensory and contextual information underlies aversive associative learning'.</p>
Fig. 2 Sectioned solemydid specimens and resulting binary images used for compactness analysis with Bone Profiler. a in Shell bone histology of solemydid turtles (stem Testudines): palaeoecological implications
Fig. 2 Sectioned solemydid specimens and resulting binary images used for compactness analysis with Bone Profiler. a Solemys vermiculata, costal fragment (MCNA-15047). b Solemys vermiculata, shell fragment (MCNA-15046). c Solemys sp., costal fragment (UPUAM-14001)
Orbit Image Analysis: An open-source whole slide image analysis tool
<p>This is a whole slide image (WSI) dataset for glomeruli segmentation on kidney tissue, in total 88 images.</p> <p>The train-set (58 images) and test-set (32 images) has been used in the publication "Orbit Image Analysis: An open-source whole slide image analysis tool" to train and test the<br> glomeruli segmentation model.</p>
Intravoxel incoherent motion model of diffusion weighted imaging and diffusion kurtosis imaging in differentiating of local colorectal cancer recurrence from scar/fibrosis tissue by multivariate logistic regression analysis
<p>We uploaded mean of diffusion coefficient (MD) and mean of diffusional Kurtosis values of 56 patients related to the manuscript: Fusco, Roberta, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori, and Antonella Petrillo. 2020. "Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis" Applied Sciences 10, no. 23: 8609. https://doi.org/10.3390/app10238609</p>
Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions
<p>We uploaded the 15 morphological features of 91 samples of 85 patients analyzed in the manuscript: Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria L. Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti, and Antonella Petrillo. 2021. "Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions" Applied Sciences 11, no. 4: 1880. https://doi.org/10.3390/app11041880</p>
Figure 3 in Analysis of VCR images of pre-oviposition and oviposition behaviour of Culex quinquefasciatus (Diptera: Culicidae) females, in the laboratory
Figure 3. Pre-oviposition and oviposition behavioural sequences of Culex quinquefasciatus gravid females in response to grass infusion after landing on the habitat inner walls.
Figure 2 in Analysis of VCR images of pre-oviposition and oviposition behaviour of Culex quinquefasciatus (Diptera: Culicidae) females, in the laboratory
Figure 2. Variation of the relative frequency (%) of the oviposition times of Culex quinquefasciatus during scotophase in laboratory.
Figure 4 in Analysis of VCR images of pre-oviposition and oviposition behaviour of Culex quinquefasciatus (Diptera: Culicidae) females, in the laboratory
Figure 4. Pre-oviposition and oviposition behavioural sequences of Culex quinquefasciatus gravid females in response to grass infusion after landing directly on the infusion surface.
Figure 1 in Analysis of VCR images of pre-oviposition and oviposition behaviour of Culex quinquefasciatus (Diptera: Culicidae) females, in the laboratory
Figure 1. Mean raft number of Culex quinquefasciatus observed for gravid females, artificial blood feeding in laboratory conditions.
Analysis of RNA polymerase II phosphorylation in two-color STED microscopy images
<p>This data set includes the raw image data and MatLab analysis scripts to assess the relative distribution of RNA polymerase II C-terminal domain serine 5 and serine 2 phosphorylation in zebrafish embryos. Phosphorylated polymerase II was labeled by immunofluorescence, microscopy images were acquired by STED microscopy and analyzed using MatLab scripts. This extended version now contains image data and analysis scripts to two alternative sets of antibodies.</p>
Images and statistical analysis of alfalfa root crowns from inside and outside disease rings caused by cotton root rot
<p>This repository contains raw image data of root crowns imaged using the backlit RhizoVision Crown platform of alfalfa plants from either inside or outside disease rings caused by cotton root rot for a manuscript to be submitted. Data files and the R scripts are included for complete statistical analysis associated with the imaged root crown set.</p> <p>Please cite both this repository and below journal article if reusing this data for a publication.</p> <p><strong>Manuscript Title:</strong> Digital imaging to evaluate root system architectural changes associated with soil biotic factors</p> <p><strong>Authors: </strong>Chakradhar Mattupalli, Anand Seethepalli, Larry M. York, Carolyn A. Young</p> <p><strong>Journal Article: </strong><a href="https://doi.org/10.1094/PBIOMES-12-18-0062-R">https://doi.org/10.1094/PBIOMES-12-18-0062-R</a> (open access)</p> <p><strong>Image Files</strong></p> <p>afalfa_roots_raw_images.zip - 264 images in PNG format directly from a monochrome camera with gamma at 3.9 to make near-segmented raw images in greyscale</p> <p>alfalfa_feature_images.zip - 264 images in PNG format with a subset of computed features overlaid</p> <p>alfalfa_segmented_images.zip - 264 black and white, binary images in PNG format that result from simple thresholding of the raw images</p> <p>I_scale_1.png - an image of a 6 inch ruler for extracting pixel to physical unit conversion</p> <p>metadata.csv - metadata output from RhizoVision Analyzer with diameter ranges and other options used for image analysis</p> <p><strong>Statistical Analysis</strong></p> <p>alfalfaCRR_features_10012018.csv - extracted features from RhizoVision Analyzer v1.0.3 - called by name in R script</p> <p>RootArchitectureFieldStudyimagetoIDmap.csv - mapping of image file names to plot identity - called by name in R script</p> <p>manuscript complete root rot RVC analysis.R - R script with all analysis that used the data from the imaged root crowns</p> <p> </p> <p> </p>
Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis
<p>Relevant data for the publication titled: Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis</p>
High-Resolution Sporadic E Layer Observation Based on Ionosonde using a Cross-Spectrum Analysis Imaging Technique
<p>The package includes the data used in Figures 1 to 6 in the paper “High-Resolution Sporadic E Layer Observation Based on Ionosonde using a Cross-Spectrum Analysis Imaging Technique".</p> <p>Since the raw data of the ionosonde is quite large and difficult to upload, the echo data of the Es layer after high-resolution imaging processing is packed together and uploaded here.</p> <p>All data can be imported and viewed directly by MATLAB with the function of "imagesc".</p> <p>Due to internal delay, the actual range should be obtained by subtracting 5 range bins.</p>
Experimental Data for: Machine learning enabled image analysis of time-temperature sensing colloidal arrays
<p>This dataset contains images of colloidal arrays functioning as time-temperature integrating, autonomous sensors. Each image shows a sensor consisting of multiple colloidal crystals with varying compositions of particles with different glass transition temperatures. Details on the composition and manufacturing procedures are explained in the corresponding publication. The data is organized into folders with different temperature setpoints. For each temperature, we investigated 10 samples. The name of the samples corresponds to their creation date. The name of the image files corresponds to their acquisition time. The first image in each sample folder was taken at the very start of the heating period. Hence, the heating time can be calculated by subtracting the start time from the acquisition time.</p>
Supporting data for "SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis"
<p>In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays.</p> <p>To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results.</p> <p>SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at <a href="https://github.com/FunctionalUrology/SpheroScan">https://github.com/FunctionalUrology/SpheroScan</a>.</p> Enable Ginger<em>Cannot connect to Ginger</em> Check your internet connection<br> or reload the browserDisable in this text fieldRephraseRephrase current sentenceEdit in Ginger×
Trained CNN for analysis of melanoma whole slide images to automatically assess the infiltration of TILs
<p>The file is the trained convolutional neural network (CNN) developed in "Ugolini F et al.Tumor infiltrating lymphocytes recognition in primary melanoma by deep learning convolutional neural network" submitted to American Journal of Pathology (2023). The CNN is based on a a pre-trained Inception-ResNet-v2 to automatically to recognize areas in the tumor region containing TILs and area without TILs in histopathological digitalized slides of primary melanoma. The file is in a Matlab format (.mat).</p>
External test datasets for "SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis"
<p>In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays.</p> <p>To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results.</p> <p>SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at <a href="https://github.com/FunctionalUrology/SpheroScan">https://github.com/FunctionalUrology/SpheroScan</a>.</p>
Single-Cell Imaging Dataset: Hela FUCCI Cell Fluorescence Analysis
<p>Hela FUCCI Cell Dataset: Fluorescence Intensity and Segmentation</p> <p>The "Hela FUCCI Cell Dataset" is a comprehensive collection of fluorescence microscopy data capturing the fluorescence intensity of Hela FUCCI cells. The dataset encompasses a diverse range of cellular images acquired through fluorescence imaging techniques, offering valuable insights into the cellular behavior and fluorescent signal patterns.</p> <p>Contents:</p> <p>Fluorescence Intensity Data: The dataset includes fluorescence images of Hela FUCCI cells captured in both red and green channels. These images represent the intensity levels of cellular fluorescence signals.</p> <p>Purpose:<br> The dataset serves as a resource for researchers and scientists interested in cellular fluorescence analysis. It supports investigations into cellular dynamics, cell cycle studies, and fluorescence signal patterns. Researchers can utilize this dataset to develop and evaluate image processing, analysis, and machine learning techniques for cell detection and fluorescence quantification.</p> <p>Data Collection:<br> The data were collected using fluorescence microscopy techniques, capturing the distinct fluorescence signals emitted by Hela FUCCI cells. </p> <p>Usage:<br> Researchers can use this dataset to:</p> <p>Investigate fluorescence patterns and intensities of Hela FUCCI cells.<br> Develop and validate machine learning algorithms for cell segmentation and detection.<br> Explore cellular behaviors and dynamics under various experimental conditions.</p> <p>Citation:<br> If you use this dataset in your research, please cite the original source to acknowledge its contribution.</p> <p>Access and Availability:<br> The dataset is openly available through Zendo, accessible via the following link: https://zenodo.org/. Researchers are encouraged to explore, analyze, and contribute to the dataset's applications and advancements in cellular fluorescence analysis.</p>
CitrusUAT: A Dataset of Orange Citrus sinensis Leaves for Abnormality Detection Using Image Analysis Techniques
<p>This dataset provides a collection of color images taken from the orange leaves of <em>Citrus sinensis</em> (L.) Osbeck species with diseases, nutritional deficiencies, and pest symptoms, proper to develop abnormality detection algorithms based on digital image analysis techniques. The dataset comprises 953 color images divided into 12 classes of orange leaves: Healthy, Huanglongbing (HLB), Greasy spot, Iron deficiency, Magnesium deficiency, Manganese deficiency, Nitrogen deficiency, Zinc deficiency, Texas citrus mite, Red scale, Red scale sequelae, and Citrus leafminer. Each color image was segmented by a thresholding method to obtain a binary mask of the leaf region. Samples were analyzed by the quantitative real-time polymerase chain reaction (qPCR) diagnostic test to detect the <em>Ca.</em> L. asiaticus bacterium that causes HLB disease.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.