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97 results for “Phase contrast”
Dataset for "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?"
<p>This dataset contains images that were used in the analysis of the manuscript "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?", that was published in Biomedical Physics and Engineering Express in 2018. Images are uploaded in .tif format. Three different synchrotron-based imaging techniques were compared on the same cardiovascular samples: grating interferometry (GI) and absorption-based phase propagation with and without phase retrieval according to Paganins method. An excel file is provided in which the nomenclature of the files is explained.</p>
Armoricaphyton chateaupannense - Propagation Phase Contrast X-Ray Synchrotron Microtomography Dataset
<p>Includes:</p> <p>1) Propagation phase contrast X-ray synchrotron microtomography (PPC-SRμCT) dataset to study the three-dimensional structure of the permineralized wood from <em>Armoricaphyton chateaupannense</em>, using the ID19 beamline of the European Synchrotron Radiation Facility (ESRF), Grenoble, France.</p> <p><strong>Dataset Information (also see scan_log.xml):</strong></p> <ul> <li>Number of image in dataset: 2159 images</li> <li>Images prefix: plante_</li> <li>Image x/y size: 3763 x 2048 px</li> <li>Image type: 16-bit TIFFs (with Pack Bits compression)</li> <li>Image size on disk: 14.8 MB each</li> <li>Scan date: 31-Oct-2008</li> <li>Scan energy: 30keV </li> <li>Voxels size: 0.551 um</li> <li>Filters: Al_1_mm Al_0.5_mm Diam_U</li> <li>Projection number: 4000</li> <li>Projection rotation: 360 degs</li> <li>Magnification: x20</li> <li>Source-sample distance: 145000</li> <li>Scan type: continuous</li> </ul> <p>Note: these images have been cropped from their original scan output size.</p> <p>2) Two supplemental videos of the 3D model.</p>
Reproduction package for the paper "High-contrast observations of brown dwarf companion HR 2562 B with the vector Apodizing Phase Plate coronagraph"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stab1893">"High-contrast observations of brown dwarf companion HR 2562 B with the vector Apodizing Phase Plate coronagraph" by Sutlieff et al. (2021)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Experimental data for: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"
<p>This is the raw experimental data for the paper: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"</p> <p>Now also including code to recreate figures, and data from alignment and multi-slice ptychography reconstructions.</p> <p>The data is in zarr format and can be read with the zarr python library. It also contains metadata in a dictionary. </p>
Cellpose model for Digital Phase Contrast images
<p><strong>Name: </strong>Cellpose model for Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose model, trained via transfer learning from ‘cyto’ model.</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast) and Manual annotations (<em>10.5281/zenodo.5996883</em>)</p> <p><strong>Training Procedure: </strong>Model was trained using a Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> </p> <p>python -m cellpose --train --dir <em>TRAINING/DATASET/PATH/</em>train --test_dir <em>TRAINING/DATASET/PATH/</em>test --pretrained_model cyto --chan 0 --chan2 0</p> <p>The model file (MODEL NAME) in this repository is the result of this training.</p> <p><strong>Prediction Procedure: </strong>Using this model, a label image can be obtained from new unseen images in a given folder with</p> <p>python -m cellpose --dir <em>NEW/DATASET/PATH</em> --pretrained_model <em>FULL_MODEL_PATH</em> --chan 0 --chan2 0 --save_tif --no_npy</p>
Digital Phase Contrast on Primary Dermal Human Fibroblasts cells
<p><strong>Name</strong>: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells </p> <p><strong>Data type</strong>: Paired microscopy images (Digital Phase Contrast, <em>square rooted</em>) and corresponding labels/masks used for cellpose training (the corresponding Brightfield images are also present), organized as recommended by <a href="https://cellpose.readthedocs.io/en/latest/train.html">cellpose documentation</a>.</p> <p><strong>Microscopy data type</strong>: Light microscopy (Digital Phase Contrast and Brighfield )</p> <p><strong>Manual annotations</strong>: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects (Dust, fused cells) were left unannotated, so that the cellpose model (10.5281/zenodo.6023317) may mimic the same user bias during prediction. This was particularly necessary due to the accumulation of floating debris in the center of the well.</p> <p><strong>Microscope</strong>: Perkin Elmer Operetta microscope with a 10x 0.35 NA objective</p> <p><strong>Cell type</strong>: Primary Dermal Human Fibroblasts cells</p> <p><strong>File format</strong>: .tif (16-bit for DPC and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p>NOTE : This dataset was used to train cellpose model ( 10.5281/zenodo.6023317 )</p> <p> </p>
Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images
<p><strong>Name: </strong>Cellpose models for Brightfield and Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional <strong>Training Dataset . Includes</strong> corresponding csv files with 'Quality Control' metrics(§) (model.zip).</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>)</p> <p><strong>Training Procedure: </strong>The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> . Training was done using a <a href="https://datascience.ch/renku/">Renku </a>environment (<a href="https://github.com/BIOP/renku-templates/tree/main/VNC-Napari-Fiji-Omero-CUDA11.4-cellpose-omnipose">renku template</a>).</p> <p> </p> <p><strong>Command Line Execution for the different trained models</strong></p> <p><strong>nuclei_from_bf: </strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_bf</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_dpc:</strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_dpc</strong>:</p> <pre><code>cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><em><strong>NOTE </strong></em>(§): We provide a notebook for Quality Control, which is an adaptation of the <a href="https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb">"Cellpose (2D and 3D)" notebook from ZeroCostDL4Mic</a> .</p> <p><em><strong>NOTE</strong></em>: This dataset used a training dataset from the Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>) generated from the “HeLa “Kyoto” cells under the scope” dataset Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6139958">https://doi.org/10.5281/zenodo.6139958</a>) in order to automatically generate the label images.</p> <p><strong><em>NOTE</em></strong>:<strong> </strong>Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.</p> <p> </p>
Phase Contrast Time-Lapse and F-actin Imaging of Mechanically Compressed or Irradiated Pseudostratified Human Bronchial Epithelial Cells
<p><strong>Overview</strong></p> <p>This dataset includes phase contrast time-lapse imaging of <em>in vitro</em> pseudostratified airway epithelial cells to visualize their collective cellular migration after exposure to mechanical compression (mimicking bronchoconstriction) or irradiation. Additionally, the cells were fixed and stained for F-actin to visualize the apical cell boundaries, basal cell boundaries, and basal cell stress fibers.</p> <p><strong>Cell Culture and Treatment</strong></p> <p>Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface (ALI) culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to either mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction or irradiation (1Gy of ionizing radiation using a RS 2000 Biological Research Irradiator (RadSource) on ALI days 7, 10, and 14).</p> <p><strong>Phase Contrast Time-Lapse Imaging</strong></p> <p>At 24 or 72 hours after final treatment, cells were imaged to visualize collective cellular migration. For each independent experimental replicate (2 transwells per treatment per timepoint), six fields of view per well were imaged every 6 minutes over 1.5 hours. The imaging chamber was supplied with 37°C, 5% CO2, humidified air on a Zeiss Axio Observer Z1 to collect phase contrast images. <em>The image resolution is 0.586 µm/pixel.</em></p> <p><strong>Immunofluorescence Imaging</strong></p> <p>Cells were fixed (4% PFA for 30 minutes) at 24 or 72 hours after final treatment (and after phase contrast time-lapse imaging). Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack. <em>The image resolution is 0.293 µm/pixel.</em></p> <p><strong>Dataset</strong></p> <p>Phase contrast time-lapse movies are provided as *.avi files. Immunofluorescence images are provided as *.tif files. For an individual transwell, the imaging dataset includes:</p> <ul> <li>6 phase contrast time-lapse movies</li> <li>5 immunofluorescence images of apical cell boundaries</li> <li>5 immunofluorescence images of basal cell boundaries</li> <li>5 immunofluorescence images of basal cell stress fibers</li> </ul> <p>Phase contrast time-lapse filenames contain</p> <ul> <li>Donor: U13</li> <li>Timepoint: 24 or 72 hours</li> <li>Treatment & Well: control (C), mechanical compression (P), or irradiation (R); well 1 or 2</li> <li>Field of View: (1) – (6)</li> </ul> <p>Immunofluorescence image filenames contain:</p> <ul> <li>Donor: <strong>U13</strong></li> <li>Timepoint: <strong>24</strong> or <strong>72</strong> hours</li> <li>Treatment & Well: control (<strong>C</strong>), mechanical compression (<strong>P</strong>), or irradiation (<strong>R</strong>); well <strong>1</strong> or <strong>2</strong></li> <li>Field of View: <strong>1-5</strong></li> <li>Region of Interest: apical cell boundaries (<strong>ACB</strong>), basal cell boundaries (<strong>BCB</strong>), or basal stress fibers (<strong>SF</strong>)</li> </ul> <p>Phase contrast time-lapse and immunofluorescence from the same transwell will all start with the same “Donor_Timepoint_Treatment/Well...” (i.e. U13_24_C1…). <strong>Note that the images from phase contrast and immunofluorescence are not necessarily from matched locations within the transwell and are at different spatial scales.</strong></p> <p>Immunofluorescence images from the same z-stack field of view will start with the same “Donor_Timepoint_Treatment/Well_FieldofView…” (i.e. U13_24_C1_1…).</p>
Supporting data: phase-contrast virtual chest radiography
<p>This dataset contains supporting data for the publication below:</p> <ul> <li>Ilian Häggmark, Kian Shaker, Sven Nyrén, Bariq Al-Amiry, Ehsan Abadi, William P. Segars, Ehsan Samei, and Hans M. Hertz, "Phase-contrast virtual chest radiography<em>"</em>, <em>Proceedings of the National Academy of Sciences </em><strong>120</strong><em> </em>(1), e2210214120 (2023). <a href="https://doi.org/10.1073/pnas.2210214120">https://doi.org/10.1073/pnas.2210214120</a></li> </ul> <p>If you use this dataset for your work, <strong>please cite this publication.</strong></p> <p>-------------------------------------</p> <p><strong>1. Virtual patient (2D) </strong></p> <p>An upsampled and projected virtual patient derived from the XCAT model (see paper for more details). The projected thickness (unit: [m]) of 28 separate materials are stored in the mat-file <em>'virtual_patient.mat'</em>. The accompanying text file <em>'virtual_patient_materials.txt' </em>lists all 28 materials (3rd dimension in the virtual patient .mat file) .</p> <p>Data information:</p> <ul> <li>File type: .mat</li> <li>Size: 39200x52200x28 (3D matrix, single, 32-bit)</li> <li>Pixel size: 7.69x7.69 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>2. Full-chest virtual radiographs</strong></p> <p>Three full-size virtual chest radiographs of the virtual patient (above) simulated with different settings:</p> <ul> <li>Conventional (<em>z</em> = 0 m, 120 kVp tungsten spectrum)</li> <li>Control (<em>z</em> = 0 m, 60 keV monochromatic)</li> <li>Phase contrast (<em>z</em> = 12 m, 60 keV monochromatic)</li> </ul> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 8000x6000 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>3. Zoom in on chest radiographs at different propagation distances</strong></p> <p>This is the underlying data for <strong>Figure 2</strong> in the paper.</p> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 600x600 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>4. Observing airway wall thickening</strong></p> <p>This is the underlying data for<strong> Figure 5</strong> in the paper.</p> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 750x750 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul>
Figure 13 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 13. Electrohemiphlebia barucheli gen. et sp. nov., holotype ARC 372.1, apical part of hindwing.
Figure 20. Wing reconstructions. A–B in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 20. Wing reconstructions. A–B. Electrohemiphlebia barucheli gen. et sp. nov., holotype ARC 372.1. A, forewing. B, hindwing. C, Jordanhemiphlebia electronica Kaddumi gen. et sp. nov., holotype. Scale bars = 2 mm. N, nodus; Ax1, Ax2, primary antenodal crossveins; RA, radius anterior; RP, radius posterior; IR, intercalary radial veins; MA, median anterior; MP, median posterior; CuA, cubitus anterior; Pt, pterostigma.
Figure 11 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 11. Electrohemiphlebia barucheli gen. et sp. nov., holotype ARC 372.1, nodal region of hindwing.
Figure 6 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 6. Electrohemiphlebia barucheli gen. et sp. nov., holotype ARC 372.1, detail of fore legs white arrow for tibia).
Figure 1 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 1. Electrohemiphlebia barucheli gen. et sp. nov., holotype ARC 372.1, habitus left lateral view.
Figure 3 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 3. Electrohemiphlebia barucheli gen. nov., sp. nov., holotype ARC 372.1, habitus frontal view.
Figure 19 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 19. Jordanhemiphlebia electronica Kaddumi gen. et sp. nov., holotype, basal half of wing. Scale bar = 1 mm.
Figure 2 in Phase contrast X-ray synchrotron microtomography and the oldest damselflies in amber (Odonata: Zygoptera: Hemiphlebiidae)
Figure 2. Electrohemiphlebia barucheli gen. et sp. nov., holotype ARC 372.1, habitus right lateral view.
Phase-contrast CT of BDL rats liver-2 week
<p>Datasets of paper "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo PCCT"</p>
Phase-contrast time-lapse recordings of four bacterial species growing in microfluidic microtraps
<p>This dataset and software accompany the article <a href="https://doi.org/10.1371/journal.pcbi.1011181">"Label-free deep learning based species classification of bacteria imaged by phase-contrast microscopy"</a> for reproducing the results. The study applies deep-learning based image and video models for species classification of the bacteria <em>Enterococcus faecalis</em>, <em>Escherichia coli</em>, <em>Klebsiella pneumoniae</em>, and <em>Pseudomonas aeruginosa</em> growing in microfluidic chip traps.</p>
Phase contrast images of Hierodula membranacea spermatocytes in metaphase I
<p>These are phase contrast images of <em>Hierodula membranacea</em> primary spermatocytes. For each image, 608 px=10 µm.</p>
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