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97 results for “Phase contrast”
Phase contrast microscopy sequence of U373 cells.
<p>The .zip file contains .CSV file for frame</p> <p>each line contains: #cell, #frame, x position, y position</p> <p>Cell cultures in Falcon plastic dishes maintained at 37 C° were observed and recorded automatically every 4 min. The images were acquired under a phase-contrast microscope (Olympus, model IX50, magnification ratio 10:1), with a<br> charge-coupled device video camera (Hitachi Denshi, modelKP-M1E/K-S10) coupled with an acquisition board (32-bits<br> Matrix Vision PC-GRAB-GI frame grabber).</p> <p>The image dimensions are 700 500 pixels (with a pixel resolution of 0.92µm at the magnification ratio abovementioned) discretized on 256 gray levels (8 bits/pixel). The images are compressed using jpeg compression.</p>
Phase-contrast MRI data of 18 Chiari-I malformation patients and 21 controls
<p>We collected phase-contrast MRI data (5-mm midline sagittal section, rostro-caudal direction) and obtained average velocity in certain Regions-0f-Interest (ROIs). The measured ROIs are cerebellar tonsil, ventral spinal subarachnoid space, dorsal spinal subarachnoid space, upper portion of syrinxes, upper cervical cord, and medulla. Data obtained from 18 Chiari-I patients (preoperative and postoperative) and 21 controls are included. Because the preoperative MRI data was missing in one patient, the preoperative studies included 17 MRI sessions. The unit of the data is cm/sec. The data are synchronized with pre-processing so that the rise of the caudal CSF movement be placed at the center of the cardiac cycle.</p>
Figure 16. Hemiphlebia mirabilis Selys, 1869 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 16. Hemiphlebia mirabilis Selys, 1869, anterior view of head.
Figure 15. Hemiphlebia mirabilis Selys, 1869 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 15. Hemiphlebia mirabilis Selys, 1869, dorsal view of head.
Figure 8 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 8. Electrohemiphlebia barucheli gen. et sp. nov., holotype ARC 372.1, left view of thorax.
Figure 5 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 5. Electrohemiphlebia barucheli gen. et sp. nov., holotype ARC 372.1, dorsal view of head.
Figure 17. Hemiphlebia mirabilis Selys, 1869 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 17. Hemiphlebia mirabilis Selys, 1869, ventral view of fore tibia.
Phase-contrast time-lapses of seven bacterial species growing in microfluidic mother machine traps
<p>This dataset and software accompany the article "Rapid label-free identification of seven bacterial species using microfluidics, single-cell time-lapse phase-contrast microscopy, and deep learning-based image and video classification" for reproducing the results.</p> <p>In the study, deep-learning models are trained to classify phase-contrast videos (time-lapses) of bacteria growing in microfluidic chip traps. The dataset consists of lab isolates of the species Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Enterococcus faecalis, Proteus mirabilis, and Staphylococcus aureus. The video clips have around 30 frames each, captured during one hour of growth (2 minutes between each frame). The whole dataset consists of around 620,000 images from 19,500 traps.</p> <p>Additionally, the package contains software to re-run the experiments, generate output metrics, and build the graphs in the article.</p>
Improving magnetic STEM-differential phase contrast imaging using precession
<p>Scanning transmission electron microscopy datasets and processing files used in the journal publication "<strong>Improving Magnetic STEM-Differential Phase Contrast Imaging using Precession</strong>".</p> <p>DOI link to publication: <a href="https://doi.org/10.1093/micmic/ozad001">https://doi.org/10.1093/micmic/ozad001</a></p> <p> </p> <p><strong>Prerequisites</strong></p> <p>To run the scripts necessary to process the files, the open source packages JupyterLab, HyperSpy, pyXem, and fpd need to be installed. These notebooks were created with these package versions:</p> <ul> <li>hyperspy 1.6.4</li> <li>pyxem 0.13.3</li> <li>fpd 0.2.0</li> <li>jupyterlab 3.2.0</li> </ul> <p> </p> <p><strong>Data files and processing scripts</strong></p> <p>Data files are collected in .zip folders and have names that start with "d00..", while processing scripts are in the Jupyter Notebook .ipynb data format whose names start with "p00..". These files are divided into three main processing steps, outlined as follows:</p> <ol> <li><strong>Processing of raw data</strong>: Raw data files can be found in the d001_scans.zip folder. These are processed with the p002_get_dpc_raw.ipynb script which uses either the center of mass or phase correlation methods.</li> <li><strong>D-scan correction</strong>: The processed files from the previous step are saved in the d002_dpc_raw.zip folder. The p003_get_dpc_cor.ipynb script performs a d-scan correction on these files and saves the output in the d003_dpc_cor.zip folder. <ul> <li><strong>Virtual segmented detector algorithm</strong>: For comparison purposes to the other processing algorithms, a virtual segmented detector algorithm was developed and can be found in the p004_segmented_detector.ipynb script. This algorithm extracts a linear d-scan plane from already processed phase correlation files found in d002_dpc_raw.zip, subtracts it from the raw data files found in d001_scans.zip, and finally performs the processing algorithm.</li> </ul> </li> <li><strong>Plotting files</strong>:<strong> </strong>The p005_plot_dpc_images.ipynb script creates the figures as seen in the journal publication. The input files are those found in d003_dpc_cor.zip from the previous processing step.</li> </ol>
Phase contrast images of bacteria and ground truth segmentations
<p><strong>Name</strong>: Phase contrast images of bacteria </p> <p><strong>Data type</strong>: Paired microscopy images and corresponding labels/masks used for model training, organized as recommended by the <a href="https://imagej.net/plugins/denoiseg">DenoiSeg documentation</a>.</p> <p><strong>Microscopy data type</strong>: Light microscopy (Phase Contrast)</p> <p><strong>Manual annotations</strong>: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects were left unannotated.</p> <p><strong>Microscope</strong>: Zeiss Axio Imager M2 epi-fluorescence microscope with a Zeiss Plan-Apochromat; 100x/1.4 oil DIC objective</p> <p><strong>File format</strong>: .tif (float 32-bits for phase contrast and 16-bit for mask images)</p> <p><strong>Image size</strong>: 256x256 pixels (Pixel size: 64.5 nm)</p> <p> </p> <p>Content: </p> <p>train - raw (33 files) </p> <p> - masks (33 files)</p> <p>test - raw (11 files) </p> <p> - masks (11 files)</p> <p> </p> <p>All images available in the raw folders were normalized by dividing the original images with a gaussian blurred version or the original image (200 pixels). A groovy code working within ImageJ/Fiji corresponding to this operation is as follow:</p> <pre><code class="language-java">ImagePlus normalize(ImagePlus input_image) { flatfield = (new Duplicator()).run(input_image) (new GaussianBlur()).blur(flatfield.getProcessor(), 200) return ImageCalculator.run(input_image, flatfield, "Divide create 32-bit") } import ij.ImagePlus import ij.plugin.Duplicator import ij.plugin.ImageCalculator import ij.plugin.filter.GaussianBlur</code></pre> <p> </p>
Edge illumination X-ray phase contrast imaging with alternative gratings: dataset
<p>This dataset contains results from Edge illumunation X-ray phase contrast simulations with alternative gratings, as shown in 'Setup.png' The simulations are performed with the monte-carlo software Gate. Postprocessing is done in Matlab. Four different grating geometries were simulated: Conventional, sheared, curved and folded gratings. As phantom, a row of Aluminum cylinders is chosen.</p> <p>The simulation parameters can be found in the excel-file 'Simulation_parameters.xlsx'.</p> <p>The folder 'gate' contains the macros that where used for the monte carlo-simulation.</p> <p>The folder 'matlab' contains the results of post-processing in matlab for each grating geometry. They can be opened with the file 'results_script.m</p> <p>The folder 'results' contains images of the results for each geometry, including, flatfield, projection, threefold contrast and fitting parameters.</p> <table> <tbody> <tr> <td>This research was supported by EU Interreg Flanders - Netherlands Smart*Light (0386), Fonds wetenschappelijk onderzoek (G090020N, G094320N), and Agentschap Innoveren \& Ondernemen (Vlaio) (HBC.2020.2159). Nathanaël Six and Ben Huyge have a PhD fellowship of the FWO (11D8319N, 1S46122N).</td> </tr> </tbody> </table> <p> </p> <p></p>
Phase-contrast MRI data of 18 Chiari-I malformation patients and 21 controls
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Phase contrast images of Hierodula membranacea spermatocytes in metaphase I
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Data from: Label-free imaging of intracellular structures in living mammalian cells via external apodization phase-contrast microscopy
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3d Virtual Patho-Histology of Lung Tissue from Covid19 Patients based on Phase Contrast X-ray Tomography
<p>X-ray phase contrast tomographys datasets of lung tissue from 6 Covid-19 patients (paraffin embedded unstained tissue), and one control tissue (healthy lung, in fixative solution). The data has been recorded as part of the Covid-19 Call for Proposals at DESY April 2020 at the GINIX instrument, beamline P10 of the PETRAIII storage ring at 8keV. Three different datasets are included in a single tar archive for each sample: (i) overview scan (up to 4mm cross section ) from stitching different tomograms, (ii) 1mm cylindrical punch, and (iii) cone-beam zoom tomogram recorded with waveguide illumination. </p> <p>voxel sizes: (i) 1.3µm, (ii) 650nm, (iii) 167nm. </p> <p>3 datasets per sample, raw format, voxel numbers given in Filename, single (32bit real)</p> <p> </p>
Phase contrast mammography phantom
<p>Mammography phantom for phase-contrast simulations. For details, see our GitHub page <a href="https://github.com/ilianhaggmark/phase-contrast-phantom">here.</a></p> <p>The phantom is stored as ten separate material files (format .mat). Each file contains a matrix (type single) which is 24640 by 60160 pixels.</p> <p>Material indices:</p> <p>000 - Air<br> 001 - Adipose tissue<br> 002 - Skin tissue<br> 029 - Glandular tissue<br> 088 - Connective tissue<br> 095 - Terminal duct<br> 125 - Duct<br> 151 - Blood<br> 200 - Mass<br> 250 - Calcium oxalate<br> </p> <p>Please cite: <strong>In Silico Phase-Contrast X-Ray Imaging of Anthropomorphic Voxel-Based Phantoms</strong>, Ilian Häggmark, Kian Shaker, and Hans M. Hertz, <a href="https://doi.org/10.1109/TMI.2020.3031318">IEEE Transactions on Medical Imaging 40(2) 539-548 (2021)</a>.</p>
Datasets for "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo X-ray phase contrast computed tomography"
<p>Phase-contrast CT of BDL rats liver-8 week</p>
Datasets for "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo X-ray phase contrast computed tomography"
<p>Phase-contrast CT of BDL rats liver-6 week</p>
Datasets for "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo X-ray phase contrast computed tomography"
<p>Phase-contrast CT of BDL rats liver-control group</p>
Datasets for "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo X-ray phase contrast computed tomography"
<p>Phase-contrast CT of BDL rats liver-4 week</p>
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International Brain Laboratory public data
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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.