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469
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ShareScore release 0.9.0
Dataset results
469 results for “Image analysis”
Analysis of BPD in Premature Infants With Typical Imaging Changes
ClinicalTrials.gov study NCT04163822. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Analysis, camera, and image files for: ecoEye, embedded vision camera for biodiversity monitoring
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BEHAV3D: A 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response
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Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
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Fluorescent (C)LSM image sequences of Dictyostelium discoideum (Ax2 - LifeAct mRFP) for cell track and cell contour analysis
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Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
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Supporting information for phylogenetic analysis and imaging of Agriodontosaurus
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In vivo timelapse imaging and analysis of Golgi satellite organelle distribution and movement in the neural progenitor cells of the brain
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Active and low-cost hyperspectral imaging for spectral analysis in low lighting environment
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3D micro-CT image of cichlid fish samples for genetic analysis
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Python and Jupyter Notebook for Medical Image Analysis - OpenMRBenelux 2020
<p>Dataset for the workshop "Python and Jupyter Notebook for Medical Image Analysis" at OpenMRBenelux - January 22, 2020 - Nijmegen (The Netherlands)</p>
Image Databases for Facial Analysis Coded for Race and Gender Features
<p> </p> <p>This document consists of the corpus of image databases examined for race and gender information as published in:</p> <p><em>Morgan Klaus Scheuerman, Kandrea Wade, Caitlin Lustig, and Jed R. Brubaker. 2020. How We’ve Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis. Proc. ACM Hum.-Comput. CSCW. </em></p> <p>This code book includes:</p> <p>1. Whether race/gender is present implicitly (as descriptive, but not annotated) or explicitly (annotated/labeled). </p> <p>2. What categories or descriptions of race/gender are used.</p> <p>3. Whether those categories/descriptions use underlying source material to justify or motivate their descriptions of race/gender.</p> <p>4. Whether explicitly annotated databases describe the process of annotating race/gender.</p>
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
<p><strong>Content</strong></p> <p>This repository contains pre-trained computer vision models, data labels, and images used in the pre-print publication "A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains":</p> <ol> <li><em>ADPdevkit</em>: a folder containing the 50 validation ("tuning") set and 50 evaluation ("segtest") set of images from the Atlas of Digital Pathology database formatted in the VOC2012 style--the full database of 17,668 images is available for download from the original website</li> <li><em>VOCdevkit</em>: a folder containing the relevant files for the PASCAL VOC2012 Segmentation dataset, with both the trainaug and test sets</li> <li><em>DGdevkit</em>: a folder containing the 803 test images of the DeepGlobe Land Cover challenge dataset formatted in the VOC2012 style</li> <li><em>cues</em>: a folder containing the pre-generated weak cues for ADP, VOC2012, and DeepGlobe datasets, as required for the SEC and DSRG methods</li> <li><em>models_cnn</em>: a folder containing the pre-trained CNN models</li> <li><em>models_wsss</em>: a folder containing the pre-trained SEC, DSRG, and IRNet models, along with dense CRF settings</li> </ol> <p><strong>More information</strong></p> <p>For more information, please refer to the following article. <strong>Please cite this article when using the data set.</strong></p> <p>@misc{chan2019comprehensive,<br> title={A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains},<br> author={Lyndon Chan and Mahdi S. Hosseini and Konstantinos N. Plataniotis},<br> year={2019},<br> eprint={1912.11186},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> }</p> <p>For the full code released on GitHub, please visit the repository at: <a href="https://github.com/lyndonchan/wsss-analysis">https://github.com/lyndonchan/wsss-analysis</a></p> <p><strong>Contact</strong></p> <p>For questions, please contact:<br> Lyndon Chan<br> lyndon.chan@mail.utoronto.ca<br> http://orcid.org/0000-0002-1185-7961</p>
Diagnostic Accuracy of Adrenal Imaging for Subtype Diagnosis in Primary Aldosteronism: Systematic Review and Meta-Analysis
<p>Objectives: Accurate subtype classification in primary aldosteronism (PA) is critical in assessing the optimal treatment options. This study aimed to evaluate the diagnostic accuracy of adrenal imaging for unilateral PA classification.</p> <p>Methods: Systematic searches of PubMed, EMBASE, and the Cochrane databases were performed from January 1, 2000, to February 1, 2020, for all studies that used computed tomography (CT) or magnetic resonance imaging (MRI) in determining unilateral PA and validated the results against invasive adrenal vein sampling (AVS). Summary diagnostic accuracies were assessed using a bivariate random-effects model. Subgroup analyses, meta-regression and sensitivity analysis were performed to explore the possible sources of heterogeneity.</p> <p>Result: A total of 25 studies, involving a total of 4669 subjects, were identified. The overall analysis revealed a pooled sensitivity of 68% (95% confidence interval [CI]: 61 to 74) and specificity of 57% (95% CI: 50 to 65) for CT/MRI in identifying unilateral PA. Sensitivity was higher in the contrast-enhanced (CT) group versus the traditional CT group [77% (95% CI: 66 to 85) vs. 58% (95% CI: 50 to 66)]. Subgroup analysis stratified by screening test for PA showed that the sensitivity of the aldosterone-to-renin ratio (ARR) group was higher than that of the non-ARR group [78% (95% CI: 69 to 84) vs. 66% (95% CI: 58 to 72)]. The diagnostic accuracy of PA patients aged ≤40 years was reported in 4 studies, and the overall sensitivity was 71%, with 79% specificity. Meta-regression revealed a significant impact of sample size on sensitivity and of age and study quality on specificity.</p> <p>Conclusion: CT/MRI is not a reliable alternative to invasive AVS without excellent sensitivity or specificity for correctly identifying unilateral PA. Even in young patients (≤40 years), 21% of patients would have undergone unnecessary adrenalectomy based on imaging results alone.</p>
Data from: LiDAR and RGB-image analysis to predict hairy vetch biomass in breeding nurseries
Hairy vetch is a fall seeded annual legume that can be used as a forage and cover crop. As a cover crop, it can provide numerous ecosystem services, such as soil erosion reduction, carbon sequestration, and pollinator habitat, but also agronomic services such as weed suppression and N fixation via soil rhizobium species. To improve cover crop function, traits such as biomass production are especially relevant, making it a first priority trait for cover crop breeders. However, direct phenotypic methods for biomass production are destructive. Breeders have thus relied on subjective, visual scoring methods for biomass, which are generally correlative, but are not quantitative or absolute. In this study, we evaluated two low-cost remote sensing tools, LiDAR and RGB-image analysis, for their effectiveness at predicting biomass in vivo. We evaluated these tools in two common forage breeding scenarios, spaced-plant and sward-plot nurseries, at three Minnesota locations following the winter of 2016/2017. Ground cover, determined from RGB image binarization using the Canopeo application, had a significant and linear relationship with above-ground biomass in spaced-plants (R2=0.93), and sward-plots (R2=0.89). Once the image area became saturated with vegetative pixels, a near-exponential relationship with biomass would occur. Because of the low-growth habit of hairy vetch, RGB image analysis was more appropriate at lower plant densities, such as spaced-plant nurseries. LiDAR measures of sward-plot height were also linearly and strongly related to dry-matter biomass in sward-plots (R2=0.80). The dimensionality of LiDAR sensing gave it greater predictive ability at higher plant densities, where RGB analysis could not detect vertical increases in biomass production. Lastly, we combined RGB and LiDAR data to predict sward-plot biomass in a multiple mixed-effect regression model. By doing so, we were able to explain more biomass variation than with use of either phenotypic tool as a single predictor (R2=0.94).
Scripts and raw data for comparing 2D vs 3D image analysis of zebrafish embryo microscopic data
<p>Raw data and MatLab scripts used for image analysis of RNA polymerase II with serine 5 phosphorylation in the C-terminal domain (CTD) of the subunit 1 (Pol II Ser5P) in a fixed zebrafish embryo, comparing a 2D vs 3D approach to segment out the Pol II Ser5P clusters. Pol II Ser5P was labeled by immunofluorescence, microscopy images were acquired by instant-SIM microscopy and analyzed using MatLab scripts and the bioformats importer.</p>
Automated analysis of scanning electron microscopic images for assessment of hair surface damage
<p>Mechanical damage of hair can serve as an indicator of health status and its assessment relies on the measurement of morphological features via microscopic analysis, yet few studies have categorized the extent of damage sustained, and instead, have depended on qualitative profiling based on the presence or absence of specific features. We describe the development and application of a novel quantitative measure for scoring hair surface damage in scanning electron microscopic (SEM) images without predefined features, and automation of image analysis for characterization of morphological hair damage after exposure to an explosive blast. Application of an automated normalization procedure for SEM images revealed features indicative of contact with materials in an explosive device and characteristic of heat damage, though many were similar to features from physical and chemical weathering. Assessment of hair damage with tailing factor, a measure of asymmetry in pixel brightness histograms and proxy for surface roughness, yielded 81% classification accuracy to an existing damage classification system, indicating good agreement between the two metrics. Further ability of tailing factor to score features of hair damage reflecting explosion conditions demonstrates the broad applicability of the metric to assess damage to hairs containing a diverse set of morphological features. </p>
Context Model of Aurora Image Analysis
<p>Context model for an experiment implemented in Python that classifies Aurora images. Auhtor of the script is Daisuke Kitao.</p>
Per-chalcopyrite particle morphological analysis data obtaining from three time-lapse micro-CT images and the PhreeqcRM simulation data
<p>The file "overall_particles.xlsx" includes all the image-based quantifications of all chalcopyrite particles extracted from three time-lapse micro-CT images. These image-based quantifications include specific surface area, volume, liberation ratio, mass before leaching, in the middle of leaching, and after leaching.</p> <p> </p> <p>The file "PhreeqcRM simulation.xlsx" includes the PhreeqcRM simulation results using experimental and image-based data.</p> <p> </p> <p>The file "<a href="https://zenodo.org/api/records/15239075/draft/files/Laplace_Solver-master-main.zip/content" target="_blank" rel="noopener noreferrer">Laplace_Solver-master-main.zip</a>" includes the diffusion simulation source code.</p>
Comparative analysis of autofocus criteria in reconstructed images of individual plankton for digital in line holography - Supplementary Material
<p>Supplementary Material belonging to the manuscript entitled "Comparative analysis of autofocus criteria in reconstructed images of individual plankton for digital in line holography", by Moreno et al., submitted to the journal OPTIK.</p>
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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.