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3,036 results for “ct”
CT-Scan der sog. Wartburg-Quinterne (ROI2: Bassseite der Decke)
<p><span>Instrument: </span><span>Quinterne <br></span><span>Herkunftsland: Deutschland? <br></span><span>Instrumentenzettel: </span><span>„Hans Ot [zu?] / Nuremberg“ <br></span><span>Datierung: 15. Jahrhundert? <br></span><span>Aufbewahrungsort: Eisenach, Wartburg <br></span><span>Inventarnummer: KH50</span></p> <p><span>Maße: <br></span><span>Gesamtlänge: 689 mm <br></span><span>Breite: 229 mm</span></p> <p><span>Material: <br></span><span>Korpus: Ahorn <br></span><span>Decke: Fichte <br></span><span>Rosette: Pergament <br></span><span>Griffbrett: Birnbaum/Mooreiche <br></span><span>Anhängestifte: Messing <br></span><span>Wirbel: Ahorn <br></span><span>Untersattel/Steg: Mahagoni mit Elfenbeineinlage</span></p> <p><span>Mikro-CT-Scan: Fraunhofer-Entwicklungszentrum Röntgentechnik in Fürth (Fraunhofer-IIS/EZRT), Leitender Wissenschaftler: Dr. Theobald Fuchs, </span><span>Produktionsmonitoring:</span><span> </span><span>Gabriele Scholz</span></p> <p><span> </span><span>Foto des Untersuchungs-Aufbaus: </span><span>Fraunhofer-IIS/EZRT</span></p> <p><span>Technische Angaben zu den CT-Scans: <br></span><span>Röntgenröhre: Yxlon FXE-225.99 (Verwendung des Transmissionskopfs) <br></span><span>Detektor: Thales Pixium RF4343 <br></span><span>Größe: 42,39 cm x 42,39 cm <br></span><span>Pixelanzahl: 2864 x 2864 <br></span><span>Pixelpitch: 148 μm <br></span><span>Aufnahmeparameter: 150 kV, 270 μA <br></span><span>Aufnahmeverfahren für den Korpus: Helix-CT</span></p> <p><span>Bis zum Erscheinen des Artikels zur Neuuntersuchung der Wartburg-Quinterne von Thilo Hirsch und weiteren Co-Autor*innen ist das Material nicht zugänglich (voraussichtlich bis Mitte 2024). Danach muss zuerst das Formular «request access» ausgefüllt und abgeschickt werden. Daraufhin bekommen Sie die Aufforderung, eine Genehmigung der Wartburg-Stiftung </span><span>zur Verwendung der Daten einzuholen. Sobald diese vorliegt, wird der Download-Link verschickt.</span></p>
Dataset for "Validation of SSDE calculation in a modern CT scanner and correlation with effective dose"
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A study of generalization and compatibility performance of 3D U‑Net segmentation on multiple heterogeneous liver CT datasets
<p>Background: Most existing algorithms have been focused on the segmentation from several public Liver CT<br> datasets scanned regularly (no pneumoperitoneum and horizontal supine position). This study primarily segmented<br> datasets with unconventional liver shapes and intensities deduced by contrast phases, irregular scanning conditions,<br> different scanning objects of pigs and patients with large pathological tumors, which formed the multiple heterogeneity of datasets used in this study.<br> Methods: The multiple heterogeneous datasets used in this paper includes: (1) One public contrast-enhanced CT<br> dataset and one public non-contrast CT dataset; (2) A contrast-enhanced dataset that has abnormal liver shape with<br> very long left liver lobes and large-sized liver tumors with abnormal presets deduced by microvascular invasion; (3)<br> One artificial pneumoperitoneum dataset under the pneumoperitoneum and three scanning profiles (horizontal/<br> left/right recumbent position); (4) Two porcine datasets of Bama type and domestic type that contains pneumoperitoneum cases but with large anatomy discrepancy with humans. The study aimed to investigate the segmentation performances of 3D U-Net in: (1) generalization ability between multiple heterogeneous datasets by cross-testing experiments; (2) the compatibility when hybrid training all datasets in different sampling and encoder layer sharing schema. We further investigated the compatibility of encoder level by setting separate level for each dataset (i.e., dataset-wise convolutions) while sharing the decoder.<br> Results: Model trained on different datasets has different segmentation performance. The prediction accuracy<br> between LiTS dataset and Zhujiang dataset was about 0.955 and 0.958 which shows their good generalization ability<br> due to that they were all contrast-enhanced clinical patient datasets scanned regularly. For the datasets scanned<br> under pneumoperitoneum, their corresponding datasets scanned without pneumoperitoneum showed good generalization ability. Dataset-wise convolution module in high-level can improve the dataset unbalance problem. The experimental results will facilitate researchers making solutions when segmenting those special datasets.</p> <p>Conclusions: (1) Regularly scanned datasets is well generalized to irregularly ones. (2) The hybrid training is beneficial but the dataset imbalance problem always exits due to the multi-domain homogeneity. The higher levels<br> encoded more domain specific information than lower levels and thus were less compatible in terms of our datasets.</p>
Head-Neck-PET-CT combined GTVs 2D images
<p>This dataset contains 2D images of the combined Gross Tumour Volumes (GTVs) of a Head and Neck cancer public dataset created from four different institutions in Canada. 298 images were created by using the CT and the combined GTV masks extracted from the RTSTRUCTs associated with the patient study. The dataset was orginally collected from https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-PET-CT into an Orthanc server. Patient studies were then processed into 3d numpy arrays. The central slices representing the slice with the highest number of contoured pixels were selected and extracted into 2d numpy arrays. </p>
CT-FAN-22 corpus: A Multilingual dataset for Fake News Detection
<p><strong>Data Access: </strong>The data in the research collection provided may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use it only for research purposes. Due to these restrictions, the collection is not open data. Please download the Agreement at <a href="https://drive.google.com/file/d/1QU-rw4D26r3F04FB63hTvToxOvDKaKdv/view?usp=sharing">Data Sharing Agreement</a> and send the signed form to <a href="mailto:fakenewstask@gmail.com">fakenewstask@gmail.com</a> .</p> <p><strong>Citation</strong></p> <p>Please cite our work as</p> <pre>@article{shahi2021overview, title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection}, author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas}, journal={Working Notes of CLEF}, year={2021} }</pre> <p><strong>Problem Definition:</strong> Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in <strong>English and German.</strong></p> <p><strong>Subtask 3:</strong> <strong>Multi-class fake news detection of news articles (English)</strong> Sub-task A would detect fake news designed as a four-class classification problem. The training data will be released in batches and roughly about 900 articles with the respective label. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. Our definitions for the categories are as follows:</p> <ul> <li> <p>False - The main claim made in an article is untrue.</p> </li> <li> <p>Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.</p> </li> <li> <p>True - This rating indicates that the primary elements of the main claim are demonstrably true.</p> </li> <li> <p>Other- An article that cannot be categorised as true, false, or partially false due to lack of evidence about its claims. This category includes articles in dispute and unproven articles.</p> </li> </ul> <p><strong>Input Data</strong></p> <p>The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:</p> <p><strong>Task 3</strong></p> <ul> <li>ID- Unique identifier of the news article</li> <li>Title- Title of the news article</li> <li>text- Text mentioned inside the news article</li> <li>our rating - class of the news article as false, partially false, true, other</li> </ul> <p><strong>Output data format</strong></p> <p><strong>Task 3</strong></p> <ul> <li>public_id- Unique identifier of the news article</li> <li>predicted_rating- predicted class</li> </ul> <p>Sample File</p> <pre><code>public_id, predicted_rating 1, false 2, true</code></pre> <p>Sample file</p> <pre><code>public_id, predicted_domain 1, health 2, crime</code></pre> <p><strong>Additional data for Training</strong></p> <p>To train your model, the participant can use additional data with a similar format; some datasets are available over the web. We don't provide the background truth for those datasets. For testing, we will not use any articles from other datasets. Some of the possible sources:</p> <ul> <li><a href="https://www.kaggle.com/liberoliber/onion-notonion-datasets">Fakenews Classification Datasets</a></li> <li><a href="https://www.kaggle.com/c/fakenewskdd2020/overview">Fake News Detection Challenge KDD 2020</a></li> <li><a href="https://www.kaggle.com/mdepak/fakenewsnet?select=PolitiFact_real_news_content.csv">FakeNewsNet</a></li> </ul> <p><strong>IMPORTANT! </strong></p> <ol> <li>We have used the data from 2010 to 2021, and the content of fake news is mixed up with several topics like election, COVID-19 etc.</li> </ol> <p><strong>Evaluation Metrics</strong></p> <p>This task is evaluated as a classification task. We will use the F1-macro measure for the ranking of teams. There is a limit of 5 runs (total and not per day), and only one person from a team is allowed to submit runs.</p> <p><strong>Submission Link: </strong><a href="https://codalab.org/">Coming soon</a></p> <p><strong>Related Work</strong></p> <ul> <li>Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. <em>Working Notes of CLEF</em>.</li> <li>Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, A., Míguez, R., Shaar, S., ... & Mandl, T. (2021, March). The CLEF-2021 CheckThat! lab on detecting check-worthy claims, previously fact-checked claims, and fake news. In <em>European Conference on Information Retrieval</em> (pp. 639-649). Springer, Cham.</li> <li>Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, A., Míguez, R., Shaar, S., ... & Kartal, Y. S. (2021, September). Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News. In <em>International Conference of the Cross-Language Evaluation Forum for European Languages</em> (pp. 264-291). Springer, Cham.</li> <li>Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.<a href="https://arxiv.org/pdf/2010.00502.pdf">https://arxiv.org/pdf/2010.00502.pdf</a></li> <li>G. K. Shahi and D. Nandini, “FakeCovid – a multilingualcross-domain fact check news dataset for covid-19,” inWorkshop Proceedings of the 14th International AAAIConference on Web and Social Media, 2020. <a href="http://workshop-proceedings.icwsm.org/abstract?id=2020_14">http://workshop-proceedings.icwsm.org/abstract?id=2020_14</a></li> <li>Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. <em>Online Social Networks and Media</em>, <em>22</em>, 100104. doi: <a href="https://dx.doi.org/10.1016%2Fj.osnem.2020.100104">10.1016/j.osnem.2020.100104</a></li> </ul>
An unenhanced abdominal CT dataset for kidney and kidney stone segmentation
<p>260 unenhanced abdominal CT scans from 2018 to 2020 were collected from the First Affiliated Hospital of Guangzhou Medical University. After data desensitization and data cleaning, the clean NIfTI image of abdominal CT scans was annotated based on clinical requirements and image segmentation needs. Two labels (“kidney” and “kidney stone”) were considered for masking and labeling. The 3D Slicer tool was used to mask the CT scans. The data annotations process comprised three stages. Data annotation was performed under the guidance of a professional urologist. </p> <p>Each CT scan contains an original raw image and a masked image for kidney and kidney stone. Please note that the whole dataset is split into 5 parts for easy download.</p> <p> </p>
Original images of VOI(1) and VOI(2), micro-CT scans of human meniscal tissue
<p>This link contains two micro-CT datasets, VOI(1) and VOI(2) of human meniscal tissue. <br>Image methodology discussed in the paper: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actbio.2023.12.042" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.actbio.2023.12.042</span></span></a></p> <p><span><span>The statistical image analysis of these two datasets and the pore network modeling codes are available in: https://github.com/olgaBARRERA/Biologic-porous-media-characterisation/tree/main</span></span></p>
Dataset related to article "Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"
<p>This record contains raw data related to article “Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"</p> <p>Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.</p>
Dataset related to article "Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study"
<p>This record contains raw data related to article “ Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study"</p> <p>Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the "first wave" of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51-69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1-4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.</p>
The Effect of a Digital Photograph of the Patient in a CT Scan Examination on the Quality of Diagnosis by the Radiologist
ClinicalTrials.gov study NCT00437788. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Evaluation of Thoracic Tumors With 18F-FMT and 18F-FDG PET-CT A Clinicopathological Study
ClinicalTrials.gov study NCT00698373. IPD Sharing: Not stated. Countries: 0. Publications: 0.
CT Network error reports
Open the record for dataset details and reuse information.
Profiling T cell repertoires and RNA sequencing from mouse CT-26 tumor model by TRJL
GEO Series GSE247573. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
Comparison of hatched blastocyst derived from IVV, PA,and CT
GEO Series GSE48291. Sus scrofa. 9 samples. Type: Expression profiling by array.
Transcriptome analysis for CT mRNA treated BT-549 cells and negative control cells
GEO Series GSE202194. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis for CT mRNA treated MDA-MB-468 cells and negative control cells
GEO Series GSE202180. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 08
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 02
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 03
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</p>
UK Industrial X-ray CT User-group Benchmark Data: 3D Al target 07
<p>X-ray tomography (CT) image data of a aluminium target as part of the 'UK Industrial X-ray CT User-group Benchmark Study'.<br> <br> The 3D image was generated with an X-ray tomography scan performed by Dr T Lowe at the Manchester X-ray Imaging Facility.<br> <br> The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (.raw) is in binary format and has the following characteristics: 1596 x 1596 x 950; 16-bit Signed; little-endian byte order.</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.