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3,036 results for “ct”
FIGURE 1 in A review of the taxonomy and osteology of the Rhombophryne serratopalpebrosa species group (Anura: Microhylidae) from Madagascar, with comments on the value of volume rendering of micro-CT data to taxonomists
FIGURE 1. Four of the named species from the Rhombophryne serratopalpebrosa group. (a) R. ornata, (b) R. vaventy, (c) R. coronata, and (d) R. tany. No photos in life of R. serratopalpebrosa are available. Rhombophryne guentherpetersi is shown in Fig. 6.
FIGURE 5 in A review of the taxonomy and osteology of the Rhombophryne serratopalpebrosa species group (Anura: Microhylidae) from Madagascar, with comments on the value of volume rendering of micro-CT data to taxonomists
FIGURE 5. Comparative morphology of the heads, feet, and hands of members of the Rhombophryne serratopalpebrosa species group. All specimens are the holotypes of their respective species, except R. coronata, which is paratype ZSM 694/ 2001. Asterisks indicate mirrored images. Not to scale.
FIGURE 14 in A review of the taxonomy and osteology of the Rhombophryne serratopalpebrosa species group (Anura: Microhylidae) from Madagascar, with comments on the value of volume rendering of micro-CT data to taxonomists
FIGURE 14. The posteromedial hyoid processes and mandible of the R. serratopalpebrosa species group shown in dorsal view. Abbreviations: mmk = mentomeckelian bone, angspl = angulosplenial, angspl.cp = angulosplenial coronoid process, php.base = base of posteromedial hyoid process, php.mc = medial crista of posteromedial hyoid process.
FIGURE 18 in A review of the taxonomy and osteology of the Rhombophryne serratopalpebrosa species group (Anura: Microhylidae) from Madagascar, with comments on the value of volume rendering of micro-CT data to taxonomists
FIGURE 18. The pelvic girdle of the R. serratopalpebrosa species group in lateral and dorsal view. Darker colour indicates cartilage. Abbreviations: il.cr = iliac crest, il.sh = iliac shaft, il.dp = dorsal prominence of ilium, il.og = oblique groove of ilium.
FIGURE 10 in A review of the taxonomy and osteology of the Rhombophryne serratopalpebrosa species group (Anura: Microhylidae) from Madagascar, with comments on the value of volume rendering of micro-CT data to taxonomists
FIGURE 10. Osteology of Rhombophryne regalis sp. nov. (MRSN A4602). Full skeleton in (a) dorsal, (b) ventral, and (c) lateral view; skull in (d) dorsal, (e) ventral, and (f) lateral view. Abbreviations as in Fig. 7.
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>
Breast cancer: CT and Structures. Free-breathing vs DIBH technique
Open the record for dataset details and reuse information.
Lung250M-4B: A Combined 3D Dataset for CT- and Point Cloud-Based Intra-Patient Lung Registration
<p>Point Cloud Data from the Lung250M-4B dataset.</p><p>Visit https://github.com/multimodallearning/Lung250M-4B for image data and associated code.</p>
CT-LabSTAF continous data AMT29
<p>Raw CT-LabSTAF datafiles collected duing the AMT29 transect in 2019. See readme.doc for more information.</p>
Offset-mode CT scan of an apple using table-top PHYWE scanner
<p>This is an X-ray dataset from a table-top PHYWE instrument recorded at the CINEMAX Summer School on 3D Imaging, August 2021, Fuglsang Manor, Lolland, Denmark.</p><p>The scanner was operated in offset mode, ie the centre-of-rotation was deliberately moved to the side to enable a larger field of view to image an object larger than the instrument's normal field of view, using a 360 degree scan. The object scanned is an apple collected in the Fuglsang Manor garden.</p><p>The data contains, in the raw/ folder the raw projections and in the reconstruction/ folder the reconstructed slices computed by the instrument's own software. These reconstructed slices do not contain the full apple and also shows double illumination from the offset scan geometry.</p><p>The file settings.cto contain all required geometrical information to set up the geometry for reconstruction using other software.</p>
CT-LabSTAF continous FLC from DY149 in 2022
<p><strong>CT-LabSTAF</strong></p><p><strong>DY149 </strong></p><p><strong>10-31 March 2022 </strong></p><p><strong>North Atlantic</strong></p><p>LabSTAF deployments during DY149 primarily focused on deployments of the first in situ instrument and 14C-uptake comparisons. However, two instruments were run continuously connected to the underway water supply and only this data is included here.</p><p>Both instruments were run in Auto-FLC mode and PEP and DWM were automatically run and applied before each FLC. Different FLC protocols were run, the data is saved in accordingly named subfolders.</p><p>The folder <strong>raw_rsf</strong> contains one folder per instrument within each are subfolders with the raw rsf files.</p><p>The folder<strong> auxiliary</strong> contains a folder with the cruisetrack and underway data (temp, sal, etc). The cruise report will be uploaded when it becomes available.</p>
LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 3 of 3
<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 5 and reference. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference (this one) <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td> </td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing). </p>
LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 2 of 3
<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 3 and 4. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 (this one) <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td> </td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing). </p>
Harp seal maxilloturbinate CT scans
<p>This repository contains four .vol files corresponding to four harp seal (<em>Pagophilus groenlandicus</em>) skull specimen scans from the Oslo Natural History Museum collections. The scans are centred around the maxilloturbinate mass. Scans performed by Øyvind Hammer using a Nikon Metrology XT H 225 ST CT scanner.</p> <p><code>.vol</code> files found under <code>scans.zip</code> can be opened with Avizo or loaded directly from binary as a raw <code>float[depth × height × width]</code> array.</p> <table> <tbody> <tr> <td>Specimen</td> <td>Life stage</td> <td>CBL (mm)</td> <td>CBL* (/1)</td> <td>Collection location</td> <td>Collection year</td> <td>Collector</td> <td>Scan date</td> <td>Voxel scale (μm)</td> <td>Dimensions (vx³)</td> </tr> <tr> <td>7357</td> <td>Juvenile</td> <td>148</td> <td>0.71</td> <td>Vardø, Norway</td> <td>1897</td> <td> </td> <td>March 2023</td> <td>300</td> <td>1440 × 1216 × 991</td> </tr> <tr> <td>7498</td> <td>Juvenile</td> <td>163</td> <td>0.78</td> <td> </td> <td> </td> <td> </td> <td>March 2023</td> <td>300</td> <td>1440 × 1440 × 1440</td> </tr> <tr> <td>7360</td> <td>Juvenile</td> <td>201</td> <td>0.96</td> <td>Vardø, Norway</td> <td>1893</td> <td>Dahl</td> <td>April 2023</td> <td>300</td> <td>1440 × 1440 × 1440</td> </tr> <tr> <td>7495</td> <td>Adult</td> <td>209</td> <td>1.00</td> <td>Maniitsoq, Greenland</td> <td>1893</td> <td>Bistrup</td> <td>April 2022</td> <td>127</td> <td>1535 × 1855 × 1920</td> </tr> </tbody> </table> <p> </p>
Qualla Simple Stamped Bowl (2233a3084) (CT scan)
**Qualla Simple Stamped Bowl** Location: Coweeta Creek site (31Ma34), Macon County, North Carolina. Period: Mississippian, Qualla phase (AD 1400-1700). Material: ceramic. Dimensions: diameter, 10.0 cm; height, 15.0 cm. Notes: Catalog no. 2233a3084. This artifact model was created by Abigail Gancz and is derived from a computerized tomography (CT) scan of the object performed at UNC Hospitals in 2014. The model's texture is derived from digital photographs of the object taken in 1994 and only approximates the object's actual texture. Source: Objaverse 1.0 / Sketchfab
Stone Disk (2233a5430) (CT scan)
**Stone discoidal** Location: Coweeta Creek site (31Ma34), Macon County, North Carolina. Period: Mississippian, Qualla phase (AD 1400-1700). Material: metamorphic stone. Dimensions: none. Notes: Catalog no. 2233a5430. This artifact model was created by Abigail Gancz and is derived from a computerized tomography (CT) scan of the object performed at UNC Hospitals in 2014. The model's texture is derived from digital photographs of the object taken in 1994 and only approximates the object's actual texture. Source: Objaverse 1.0 / Sketchfab
Fraunhofer EZRT XXL-CT Instance Segmentation Me163
<p>The ’Me 163’ was a Second World War fighter airplane and a result of the German air force secret developments. One of these airplanes is currently owned and displayed in the historic aircraft exhibition of the ’Deutsches Museum’ in Munich, Germany. To gain insights with respect to its history, design and state of preservation, a complete CT scan was obtained using an industrial XXL-computer tomography scanner.<br><br>Using the CT data from the Me 163, all its details can visually be examined at various levels, ranging from the complete hull down to single sprockets and rivets. However, while a trained human observer can identify and interpret the volumetric data with all its parts and connections, a virtual dissection of the airplane and all its different parts would be quite desirable. Nevertheless, this means, that an instance segmentation of all components and objects of interest into disjoint entities from the CT data is necessary.</p> <p>As of currently, no adequate computer-assisted tools for automated or semi-automated segmentation<br>of such XXL-airplane data are available, in a first step, an interactive data annotation and object labeling process has been established. So far, seven sub-volumes from the Me 163 airplane have been annotated and labeled, whose results can potentially be used for various new applications in the field of digital heritage, non-destructive testing, or machine-learning. These annotated and labeled data sets are available here.</p>
CT Scans of Roots in Soil
Open the record for dataset details and reuse information.
CT dataset for "Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer" Part 2
<p>To develop and validate a radiomics-based method for lung cancer detection, chest CT images of patients with pulmonary nodules from clinical 5 centers were collected. We hope this large-scale dataset could facilitate both clinical research for automatic lung cancer detection and diagnoses, and engineering research for 3D detection, segmentation and classification. Due to size limit of zenodo.org, we split the whole CT images sets into 2 parts; This is the Part 2.</p> <p>This dataset is a research effort of thousands of hours by experienced thoracic surgeons, and radiologists. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p>
IODP Exp 341 - Site U1417 CT scans
<p>These Zip files contain DICOM files for IODP Exp 341 Site U1417 from u-channels, measured at the College of Veterinary Medicine, Oregon State University, and curated at the Marine and Geology Repository, Oregon State University. The data is part of Deepa Dwyer's PhD thesis project. </p>
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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.