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145 results for “Image processing”
Amyloid burden quantification depends on PET and MR image processing methodology
<p>The enclosed datasets refer to the work developed at the University Medical Center Groningen and consist of the minimally required PET image data to replicate the results of the study entitled "Amyloid burden quantification depends on PET and MR image processing methodology" which abstract can be found below:</p> <blockquote> <p>Quantification of amyloid load with positron emission tomography can be useful to assess Alzheimer’s Disease <em>in-vivo. </em>However, quantification can be affected by the image processing methodology applied. This study’s goal was to address how amyloid quantification is influenced by different semi-automatic image processing pipelines. Images were analysed in their <em>Native Space </em>and <em>Standard Space</em>; non-rigid spatial transformation methods based on maximum a posteriori approaches and tissue probability maps (TPM) for regularisation were explored. Furthermore, grey matter tissue segmentations were defined before and after spatial normalisation, and also using a population-based template. Five quantification metrics were analysed: two intensity-based, two volumetric-based, and one multi-parametric feature.</p> <p>Intensity-related metrics were not meaningfully affected by spatial normalisation and did not significantly depend on the grey matter segmentation method, with an impact similar to that expected from test-retest studies (≤10%). Yet, volumetric and multi-parametric features were sensitive to the image processing methodology, with an overall variability up to 45%. Therefore, the analysis should be carried out in <em>Native Space</em> avoiding non-rigid spatial transformations. For analyses in <em>Standard Space</em>, spatial normalisation regularised by TPM is preferred. Volumetric-based measurements should be done in <em>Native Space,</em> while intensity-based metrics are more robust against differences in image processing pipelines.</p> </blockquote>
Dataset for Black Tea Fermentation Detection based on Image Processing and Machine Learning Techniques
<p>This is a dataset on black tea fermentation. The dataset contains black tea fermentation conditions and images. The fermentation conditions captured are: temperature, humidty and time. The images belong to black tea as they underwent the fermentation process. The dataset was collected in Sisibo tea factory, Kenya in July and August 2020.</p>
Figure 4. - Brightfield image showing the posterior mesosoma and anterior metasoma of Pteroceraphronmirabilipennis Dessart 1981. Arrows point to bifurcated anteromedian process of the propodeum-metanotum complex.
Figure 4. - Brightfield image showing the posterior mesosoma and anterior metasoma of Pteroceraphronmirabilipennis Dessart 1981. Arrows point to bifurcated anteromedian process of the propodeum-metanotum complex.
Impact of Image Processing Settings for Radiomic Features in Alzheimer's Disease Using 18F-FDG and 11C-PIB PET Scans
<p>Radiomics is an established method for calculating features for computer-aided diagnosis and has been vastly applied to oncological studies. This study aimed to assess the impact of image processing in radiomic features in neuroimaging. Fifteen Alzheimer's disease subjects and 18 healthy individuals underwent [18F]-2-fluoro-2-deoxy-D-glucose (FDG) and 11C-labelled Pittsburgh Compound B (PIB) PET scans. T1-MRI scans were used for cerebellar and grey matter (GM), and white matter (WM) tissue delineation. PET images were registered to MRI (MR space) and transformed to MNI space. All images were normalized to cerebellar uptake (SUVR). All possible combinations of the following settings were considered to extract feature values: (1)tracer: FDG or PIB; (2)space: MR or MNI space; (3)discretization: fixed bin number (BN) of 64, fixed bin sizes (BS) of 0.05 or 0.25; and (4)volume of interest (VOI): GM, WM, or BRAIN (GM+WM). Features that correlated (>0.9) to traditional metrics (average VOI SUVR and volume) in any configuration were removed. Correlation of feature values between configurations, redundancy, and harmonization of feature values were tested. Image processing settings highly affect radiomic feature values and should be carefully taken into consideration during study design and should be properly reported.</p><p> </p><p>The enclosed datasets refer to the work developed at the University Medical Center Groningen and consists of extracted feature values used in the publication.</p>
STED images of foot processes
<p>The dataset is made of STED images of mouse and human foot processes. </p><p>It is made of 2 parts. </p><ol><li>288 images of mouse samples</li><li>151 binarised images of human samples, 142 original images. The numbers are different as the few original ones had much bigger size than others, so they were split into more pictures of normal size.</li></ol><p>Folder structure:</p><ul><li>zenodo-mouse-data-renal - mouse images:<ul><li>original:<ul><li>mut - samples with FSGS</li><li>ctrl - control samples</li><li>NTN</li><li>2021_PodR231Q_NTS</li><li>Adriamycin_Kuehne</li></ul></li><li>binarized:<ul><li>train - train set</li><li>test</li></ul></li></ul></li><li>zenodo-human-data-hoyer - images of human samples with foot processes:<ul><li>original</li><li>binarized:<ul><li>train</li><li>test</li></ul></li></ul></li></ul>
Integrative processing in artificial and biological vision predicts the perceived beauty of natural images
<p><em>Data, code, and materials for Nara & Kaiser (2023). </em></p> <p><em>Preprint: </em><a href="https://www.biorxiv.org/content/10.1101/2023.05.05.539579v1">https://www.biorxiv.org/content/10.1101/2023.05.05.539579v1</a></p> <p>Paper: <a href="https://doi.org/10.1126/sciadv.adi9294">https://doi.org/10.1126/sciadv.adi9294</a></p> <p>In this version (v2) of the repository, we:</p> <ul> <li>fixed an error in the fMRI data, where only the data from one participant, instead of all participants was uploaded previously - now all data are available,</li> <li>added brain masks (extracted by SPM) for each participant, and</li> <li>added realignment parameter text files (created by SPM) to the functional data for each participant.</li> </ul>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
In situ plankton footage for testing image processing routines
<p>This dataset contains 12 video files collected from the deep-focus plankton imager (DPI) version of the in situ ictyoplankton imaging system (ISIIS). These files are intended to be used for testing, validating, and comparing computational pipelines. These videos were collected in the Northern Gulf of Alaska as part of an NSF-sponsored field campaign (NGA LTER).</p>
Data sets used in "Neural network processing of holographic images"
<p>Included are the training, validation, and testing data sets for synthetic holograms (netCDF), the HOLODEC data set containing the RF07 examples (netCDF), and the two splits of manually labeled HOLODEC image tiles (numpy arrays). The source code for using the data sets can be found at https://github.com/NCAR/holodec-ml </p>
Prismatic Wave Imaging Reveals Details of Highly Steep Structures: Processed 1994 BP benchmark model
<p>This zip file contains our processed 1994 BP model, some of the synthetic seismic data, and the Matlab drawing code.</p> <p>Each part has its own folder and README file, check these files for more information</p>
Annotated and processed 3D confocal microscopy images of dorsal aorta in wild-type and Endoglin-deficient zebrafish embryos at 48 hpf and 72 hpf
<p>This repository contains the original 3D confocal microscopy images that were used for the analysis of vessel geometry and endothelial cell morphology in the dorsal aorta of wild-type and Endoglin-deficient zebrafish embryos at 48 hours post fertilization (hpf) and 72 hpf in the article <a href="https://www.biorxiv.org/content/10.1101/2024.02.19.580931">Novel mathematical approach to accurately quantify 3D endothelial cell morphology and vessel geometry based on fluorescently marked endothelial cell contours: Application to the dorsal aorta of wild-type and Endoglin-deficient zebrafish embryos</a>. In this article, we developed a novel mathematical approach that allows to consistently estimate 3D vessel geometry and endothelial cell surface morphology using only information from endothelial cell contours. For the article's analysis, endothelial cell contours were manually annotated on Pecam1-EGFP-labeled cell junctions. Furthermore, dorsal aorta cross-sections were outlined on Dextran Texas Red-perfused vessel lumens. Further details are provided in the article's Materials and methods section.</p> <p>This repository contains 14 images of 7 wild-type embryos, each imaged at 48hpf and 72hpf. Furthermore, 12 images of 6 Endoglin-deficient embryos, each imaged at 48hpf and 72hpf are included. These combined files (called "analysis data" in the article) are stored in "eng_wt_data.zip". Secondly, images of 2 wild-types at 72hpf with repeated cell contour annotation and outlined vessel lumens (called "validation data" in the article) are located in "wt_angiogram_data.zip". The provided files are stored in Imaris format and can be inspected using the free <a href="https://imaris.oxinst.com/imaris-viewer">Imaris Viewer software</a>.</p> <p>To allow inspection of the endothelial cell contours that we manually annotated for the article's analysis and compare them against the intermediate results of our novel mathematical approach, i.e., contour enrichments by neighboring cells, contour smoothing splines and their projections onto the estimated vessel surfaces, we imported these contours into the Imaris files. Note that the contours' coordinates in these files are slightly less precise than in our article's analysis and thus are intended for visual inspection. To exactly reproduce the results in our article, refer to the files in <a href="https://doi.org/10.5281/zenodo.10549101">our other Zenodo repository</a>.</p>
Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
<p>Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.</p> <p>The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user's system or to familiarize oneself with the pipeline.</p>
AA5086 tensile tests with Portevin-Le Chatelier (PLC) effect, complete raw dataset: DIC images, raw output and elements of post-processing
<p><em>This dataset is associated with a paper published in Materials Science and Engineering: A.<br> DOI: </em><a href="https://doi.org/10.1016/j.msea.2019.01.009">10.1016/j.msea.2019.01.009</a></p> <p>Hardening and 2D digital image correlation data obtained on AA5086 sheets with Portevin-Le Chatelier (PLC) effect and Piobert-Lüder (PL) bands.</p> <ul> <li>rolled sheets</li> <li>28 tensile tests in rolling direction, room temperature</li> <li>constant imposed velocity with nominal strain rate from 1E-4 to 1E-1 Hz</li> <li>2 geometries: <ul> <li>(mac) large, Lw=60mm</li> <li>(mic or mes) small, Lw=4.2mm</li> </ul> </li> </ul> <p><strong>File contents</strong></p> <ul> <li>summary of experiments <ul> <li>libreoffice database format: experiments-summary.ods</li> <li>specifications, details, measurements and post treated quantities of each test</li> </ul> </li> <li>specimens blueprints (.pdf)</li> <li>video record of a test (.mp4)</li> <li>raw DIC images archives <ul> <li>format: <nominalStrainRate><geometry><index>.zip</li> <li>from 300 to 2300 images per test</li> </ul> </li> <li>time series <ul> <li>archived in timeSeries.zip</li> <li>format: <nominalStrainRate><geometry><index>_timeSeries.dat</li> <li>tabulation separated values, one line per picture (previous item)</li> <li>index, force, cross-head displacement, Stress, axial strain</li> </ul> </li> <li>bands kinetics post-treated quantities <ul> <li>archived in bandKinetics.zip, 2 TSV data files (one per geometry)</li> <li>stress at nucleation, strain magnitude jump and instantaneous working area relative elongation rate.</li> </ul> </li> </ul>
Chromocenter image processing and data for "Volume buffering in multi-component phase separation"
<p>Contains image processing code and a csv of the image processing results for the images of chromocenters in mammalian cells for the paper "Volume buffering in multi-component phase separation".</p>
Hyperspectral Placenta Dataset: Hyperspectral Image Acquisition, Annotations, and Processing of Biological Tissues in Microsurgical Training
<p>The dataset consists of 101 hyperspectral images of four fresh human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset can be used to develop machine learning algorithms for the automated classification of biological structures, particularly the classification of superficial and deep vessels and transparent tissue layers.</p>
Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
Open the record for dataset details and reuse information.
Companion for "Measuring Phenology Uncertainty with Large Scale Image Processing"
<p>This is the software and dataset companion for the paper entitled "Measuring Phenology Uncertainty with Large Scale Image Processing". Further instructions can be found in the README.org file.</p>
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
High Throughput Multispectral Image Processing with applications in Food Science
<p>Raw image samples for the PLoS ONE paper entitled "High Throughput Multispectral Image Processing with applications in Food Science".</p> <p>Segmented images for the PLoS ONE paper entitled "High Throughput Multispectral Image Processing with applications in Food Science".</p>
Dataset of light microscopy and image processing for ruthenium red staining - Rhamnogalacturonan-II dimerization deficiency impairs the coordination between growth and adhesion maintenance in plants
<p>This contains additional data relative to version 1, corresponding to a new versio of the manuscript. </p> <p>This dataset contains darkfield light microscopy images from ruthenium red stained <em>Arabidopsis thaliana </em>dark grown hypocotyls of various wildtype and mutant plants, along with the prossessing and quantified data (including segmented masks, corrected masks, raw quantification and processed quantification) reported in the study "Rhamnogalacturonan-II dimerization deficiency impairs the coordination between growth and adhesion maintenance in plants" (<a href="https://www.biorxiv.org/content/10.1101/2024.11.26.625362v1">https://www.biorxiv.org/content/10.1101/2024.11.26.625362v1</a>). Data was acquired following the method described in the publication. Processing of the raw data was perfomed using the RRQuant workflow (<a href="https://doi.org/10.5281/zenodo.14173186">10.5281/zenodo.14173186</a>).</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.