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1,087 results for “Chest”
PTX-498: A multi-center pneumothorax segmentation chest X-ray image dataset
<p>Pneumothorax is a common medical emergency defined as the abnormal collection of air in the pleural space between the lung and chest wall. Its typical symptoms include chest pain and dyspnea, leading to oxygen deficiency or even life-threatening in severe cases. Therefore, an efficient and automatic pneumothorax diagnosis algorithm would be useful in many clinical scenarios. Recently, deep learning methods have achieved impressive progress in medical image segmentation tasks. However, a large-scale dataset is one of the critical components for the success of deep learning. On the other hand, there are few public chest X-ray images with pneumothorax.</p> <p>To stimulate the researchers' interest in the pneumothorax diagnosis algorithm, <strong>we released a new data set PTX-498 here. It contains 498 chest X-ray images of pneumothorax collected from three hospitals, and each image contains pixel-level annotations.</strong> All images were resized to 1024×1024. The raw image intensity was clipped according to the window width and level inside the dicom tag and then normalized to 0 to 255. The contours of the pneumothorax area were labelled by two senior radiologists using ITK-SNAP. The dataset was anonymized and every record related to patients' privacy was removed. Only the image data and the corresponding labels were included in PTX-498.</p> <p><strong>Please use the latest v2-fix version which removes duplicate images and uses the window width and level from the original dicom tag for normalization.</strong></p> <p><strong>Citation: If you are interested in this dataset and applying it in your research, please cite the following article.</strong><br> Paper link: https://doi.org/10.1016/j.neucom.2021.05.029<br> Cite this article as Yunpeng Wang, Kang Wang, Xueqing Peng, Lili Shi, Jing Sun, Shibao Zheng, Fei Shan, Weiya Shi, Lei Liu*. DeepSDM: Boundary-aware pneumothorax segmentation in chest X-ray images [J]. Neurocomputing, 2021, 454: 201-211.</p> <div> <div class="gtx-trans-icon"> </div> </div>
Bare Chested Men - German Press Coverage Corpus
<p>A list of articles published in German gaming magazines in the 1980s and 1990s about the following games:</p><ul><li><a href="https://www.mobygames.com/game/1033/death-sword/">Barbarian I</a> (1987)</li><li><a href="https://www.mobygames.com/game/12167/axe-of-rage/">Barbarian II</a> (1988)</li><li><a href="https://research.swissdigitization.ch/?p=613">DragonSlayer</a> (1989, unreleased)</li><li><a href="https://www.mobygames.com/game/54344/torvak-the-warrior/">Torvak the Warrior</a> (1990)</li><li><a href="https://www.mobygames.com/game/6182/conan-the-cimmerian/">Conan the Cimmerian</a> (1991)</li><li><a href="https://www.mobygames.com/game/1618/commando/">Commando</a> (1985)</li><li><a href="https://www.mobygames.com/game/6739/ikari-warriors/">Ikari Warriors</a> (1986)</li><li><a href="https://www.mobygames.com/game/23105/leatherneck/">Leatherneck</a> (1988)</li><li><a href="https://www.mobygames.com/game/16149/dogs-of-war/">Dogs of War</a> (1989)</li></ul>
From Generalist to Specialist: Incorporating Domain-Knowledge into Flamingo for Chest X-Ray Report Generation
<p>This subset of the MIMIC-CXR split file contains the study identifiers and paths to the chest X-ray images used for training, validation and testing of all models presented in the paper: "From Generalist to Specialist: Incorporating Domain-Knowledge into Flamingo for Chest X-Ray Report Generation".</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>
Multienergy Fan Beam Computed Tomography Dataset of a Bird Chest Imaged with 3 Different X-ray Spectra
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection data of a biological imaging phantom (a bird chest) imaged in an X-ray microtomography scanner, using three different X-ray spectra. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters, as well as photographs and example reconstructions. The dataset is designed for use in algorithm development for multienergy computed tomography.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is the chest of a common quail (<em>Coturnix coturnix</em>) bird obtained frozen from a local supermarket. The chest section of the frozen bird was removed using a handsaw, and left to melt and settle in a sample holder before imaging.</p> <p><em>Scanner</em></p> <p>The measurement data were acquired using an X-ray microtomography scanner in the University of Helsinki Micro-CT Laboratory. The scanner uses cone beam geometry and it is equipped with an end-window tube with a tungsten target.</p> <p><em>Scan Settings</em></p> <p>The dataset consists of three consecutive scans made using identical geometry but different X-ray spectra and detector exposure times. For each scan, 720 X-ray projections were acquired using an angle increment of 0.5 degrees. Multiple frames were averaged for each projection in order to increase signal-to-noise ratio. The scan geometry and the energy-specific settings are summarized in the following two tables.</p> <p><strong>Table 1.</strong> Imaging geometry used for collecting the data.</p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Focus-center distance</td> <td>252 mm</td> </tr> <tr> <td>Focus-detector distance</td> <td>420 mm</td> </tr> <tr> <td>Geometric magnification</td> <td>5/2</td> </tr> <tr> <td>Detector pixel size</td> <td>0.200 mm</td> </tr> <tr> <td>Effective pixel size</td> <td>0.120 mm</td> </tr> <tr> <td>Projection size</td> <td>552 x 576 pixels</td> </tr> <tr> <td>Angular range</td> <td>360'</td> </tr> <tr> <td>#projections</td> <td>720</td> </tr> </tbody> </table> <p><strong>Table 2.</strong> Energy-specific settings used for collecting the data.</p> <table> <tbody> <tr> <td>Energy label</td> <td><em>U</em> (kV)</td> <td>Filtration</td> <td><em>I</em> (μA)</td> <td>Exposure time (ms)</td> <td>Frame averaging</td> </tr> <tr> <td><em>E1</em></td> <td>50</td> <td>None</td> <td>300</td> <td>125</td> <td>4</td> </tr> <tr> <td><em>E2</em></td> <td>80</td> <td>1 mm Al</td> <td>180</td> <td>125</td> <td>4</td> </tr> <tr> <td><em>E3</em></td> <td>120</td> <td>0.5 mm Cu</td> <td>120</td> <td>250</td> <td>4</td> </tr> </tbody> </table> <p><em>Data Post-Processing</em></p> <p>Before the scans were made, a dark current image and flat-field image were acquired for each scan setting. During the scans, dark current subtraction and flat-field correction were automatically applied to the X-ray projections by the measurement software.</p> <p><em>Data Contents</em></p> <p>This dataset contains the following files:</p> <ul> <li>The raw projection data (.tif format) for each scan and a metadata file (.txt format) describing the measurement setup, with formatting that is both human-readable and machine-readable.</li> <li>Pre-created 2D sinograms for each energy level. The sinograms have been created from the central plane of the cone beam, which reduces to fan beam geometry. The sinograms are stored in Matlab's .mat file format in data structures which also contain metadata on the measurement.</li> <li>Photographs taken during the measurement process.</li> <li>Example filtered backprojection (FBP) reconstructions of the central plane of the phantom for each energy. The reconstructions were computed using the Phoenix datos|x CT software provided with the microtomography scanner</li> </ul> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland (<a href="https://www.helsinki.fi/en/researchgroups/inverse-problems">https://www.helsinki.fi/en/researchgroups/inverse-problems</a>) in collaboration with the Computational Physics and Inverse Problems research group at the University of Eastern Finland, Finland (<a href="https://sites.uef.fi/inverse">https://sites.uef.fi/inverse</a>) and the X-ray Laboratory at the Department of Physics at the University of Helsinki, Finland (<a href="https://www.helsinki.fi/en/researchgroups/x-ray-laboratory">https://www.helsinki.fi/en/researchgroups/x-ray-laboratory</a>).</p> <p> </p> <p><strong>Previous Use</strong></p> <p>This dataset has been used in the following publications:</p> <p>Jussi Toivanen, Alexander Meaney, Samuli Siltanen, Ville Kolehmainen. Joint reconstruction in low dose multi-energy CT. <em>Inverse Problems and Imaging</em>, 2020, 14(4): 607-629. doi: <a href="https://doi.org/10.3934/ipi.2020028" target="_blank" rel="noopener">10.3934/ipi.2020028</a>.</p> <p>E. Cueva, A. Meaney, S. Siltanen, M. J. Ehrhardt. Synergistic multi-spectral CT reconstruction with directional total variation. <em>Philos Trans A Math Phys Eng Sci</em>. 2021 Aug 23;379(2204):20200198. doi: <a href="https://doi.org/10.1098/rsta.2020.0198">10.1098/rsta.2020.0198</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected by the Inverse Problems research group, and available at <a href="https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox">https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox</a>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We wish to thank laboratory engineer Heikki Suhonen for his guidance and assistance in conducting the measurements.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe. in Revision of common snipe, Gallinago gallinago in morphometric analysis and building the standard reference haematological values for further studies
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe.
Miniature Chest
<p>Miniature Chest and its creator, Mr. Mentis.<br>The Miniature Chest is an exhibit at the Silversmithing Museum.</p>
Bare Chested Men - Image Corpus
<p>A collection of in-game screenshots as well as paratextual image-material (covers) for the following video games:</p> <ul> <li><a href="https://www.mobygames.com/game/1033/death-sword/">Barbarian I</a> (1987)</li> <li><a href="https://www.mobygames.com/game/12167/axe-of-rage/">Barbarian II</a> (1988)</li> <li><a href="https://research.swissdigitization.ch/?p=613">DragonSlayer</a> (1989, unreleased)</li> <li><a href="https://www.mobygames.com/game/54344/torvak-the-warrior/">Torvak the Warrior</a> (1990)</li> <li><a href="https://www.mobygames.com/game/6182/conan-the-cimmerian/">Conan the Cimmerian</a> (1991)</li> <li><a href="https://www.mobygames.com/game/1618/commando/">Commando</a> (1985)</li> <li><a href="https://www.mobygames.com/game/6739/ikari-warriors/">Ikari Warriors</a> (1986)</li> <li><a href="https://www.mobygames.com/game/23105/leatherneck/">Leatherneck</a> (1988)</li> <li><a href="https://www.mobygames.com/game/16149/dogs-of-war/">Dogs of War</a> (1989)</li> </ul> <p>Copyright for the images are with their respective owners.</p>
Shared Decision Making in the Emergency Department: Chest Pain Choice Trial
ClinicalTrials.gov study NCT01969240. IPD Sharing: YES. Countries: 1. Publications: 2.
Medieval Chest
A low-poly treasure chest I made for a class assignment. It was fun to make this prop, and challenging in a way, since I had to keep it below 500 tris. Source: Objaverse 1.0 / Sketchfab
Treasure Chest Challenge Skectfab
#treasure_chest made with #blender 2.91 by Kalule Enoch for the #treasure_chest_challenge on #sketchfab its a #fancy and #magical treasure chest thats always been kept in a #magical #palace where only people with the right hearts can reach the chest therefore remains new and never runs out of treasures Source: Objaverse 1.0 / Sketchfab
Coffin Chest Fragment - AA2190.A.17
Measurements: L: 94.4cm x W: 32.5cm x D: 4.6cm Egyptian Coffin fragment from the 25th Dynasty. Originally owned by the Stanford family. Then acquired by the Canadian Conservation Institute (CCI). The fragments are currently in possession of the Queen's University Art Conservation Department. Source: Objaverse 1.0 / Sketchfab
Old Leather Chest
Pirate / Medieval type chest for setting the scene on a ship or in a castle. Source: Objaverse 1.0 / Sketchfab
Treasure's chest
Great for using on maps I redid it according to another that I saw ............................................................................................,, Source: Objaverse 1.0 / Sketchfab
Chinese Camphor Chest - Photogrammetry Scan
Photogrammetry model of an old camphor chest I bought from a driftstore some time ago. I had to decimate the model quite a bit, so it isn't the fanciest it could be. Made with 3DF Zephyr Lite and Blender. Source: Objaverse 1.0 / Sketchfab
Wood Chest Photoscan PBR
Full 360 model of a old wood chest made from around 15,000,000 poly high res mesh baked down to 65,000. Includes 4k PBR color, roughness and normal maps. Source: Objaverse 1.0 / Sketchfab
Sandstone Chest
* Nome do aluno: Víctor Costa Pôças * Disciplina: Modelagem de objetos * Curso: Curso superior de tecnologia em Jogos Digitais da Puc Minas * Nome do modelo: Baú * Descrição: Modelo feito para composição de cenário, possui 32 vertices, 21 faces e 42 tris. * Contato: victu.draw@gmail.com * This model was made in: June 2018 Source: Objaverse 1.0 / Sketchfab
[XYZ School] CW draft. Excavator chest.
Excavator chest. Coursework. The draft is made according to the concept of Lisa Kasyanova.  Source: Objaverse 1.0 / Sketchfab
Medieval Era Chest
Medieval Style Wooden Chest Texture remake of Original model by AIV https://sketchfab.com/3d-models/medieval-chest-037b03a3e0274279be4b93b7c7cedf01 Source: Objaverse 1.0 / Sketchfab
dataset- Tuberculosis detection using Squid Game Optimization with Deep Learning Model on Chest X-Ray Images
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