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368
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Dataset results
368 results for “imaging system”
Prediction of Risk of Vascular Structural Damage in Patients With Large Vessel Vasculitis (LVV) Based on PET/MRA Image Evaluation System
ClinicalTrials.gov study NCT06824714. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Developing Echocardiography Image Quality Management System Based on Deep Learning
ClinicalTrials.gov study NCT05633732. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Image dataset of disk diffusion assay scanned with the SIRscan system
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Examples of misclassified images of papilledema severity by the Deep Learning System
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Data from: Three-dimensional self-gated cardiac MR imaging for the evaluation of myocardial infarction in mouse model on a 3T clinical MR system
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Data from: A multimodal image guiding system for Navigated Ultrasound Bronchoscopy (EBUS): a human feasibility study
Background: Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) is the endoscopic method of choice for confirming lung cancer metastasis to mediastinal lymph nodes. Precision is crucial for correct staging and clinical decision-making. Navigation and multimodal imaging can potentially improve EBUS-TBNA efficiency. Aims: To demonstrate the feasibility of a multimodal image guiding system using electromagnetic navigation for ultrasound bronchoschopy in humans. Methods: Four patients referred for lung cancer diagnosis and staging with EBUS-TBNA were enrolled in the study. Target lymph nodes were predefined from the preoperative computed tomography (CT) images. A prototype convex probe ultrasound bronchoscope with an attached sensor for position tracking was used for EBUS-TBNA. Electromagnetic tracking of the ultrasound bronchoscope and ultrasound images allowed fusion of preoperative CT and intraoperative ultrasound in the navigation software. Navigated EBUS-TBNA was used to guide target lymph node localization and sampling. Navigation system accuracy was calculated, measured by the deviation between lymph node position in ultrasound and CT in three planes. Procedure time, diagnostic yield and adverse events were recorded. Results: Preoperative CT and real-time ultrasound images were successfully fused and displayed in the navigation software during the procedures. Overall navigation accuracy (11 measurements) was 10.0 ± 3.8 mm, maximum 17.6 mm, minimum 4.5 mm. An adequate sample was obtained in 6/6 (100%) of targeted lymph nodes. No adverse events were registered. Conclusions: Electromagnetic navigated EBUS-TBNA was feasible, safe and easy in this human pilot study. The clinical usefulness was clearly demonstrated. Fusion of real-time ultrasound, preoperative CT and electromagnetic navigational bronchoscopy provided a controlled guiding to level of target, intraoperative overview and procedure documentation.
GAN Generated Images for Facial Expression Recognition systems
<p>Most facial expression recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques has been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems. To advance in this respect, we evaluate the impact of the GAN techniques by creating a new DB containing the generated synthetic images. </p> <p>The face images contained in the KDEF DB serve as the basis for creating novel synthetic images by combining the facial features of two images (i.e., Candie Kung and Cristina Saralegui) selected from the YouTube-Faces DB. The novel images differ from each other, in particular concerning the eyes, the nose, and the mouth, whose characteristics are taken from the Candie and Cristina images.</p> <p>The total number of novel synthetic images generated with the GAN is 980 (70 individuals from KDEF DB x 7 emotions x 2 subjects from YouTube-Faces DB).</p> <p>The zip file "GAN_KDEF_Candie" contains the 490 images generated by combining the KDEF images with the Candie Kung image. The zip file "GAN_KDEF_Cristina" contains the 490 images generated by combining the KDEF images with the Cristina Saralegui image. The used image IDs are the same used for the KDEF DB. The synthetic generated images have a resolution of 562x762 pixels.</p> <p> </p> <p><strong>If you make use of this dataset, please consider citing the following publication:</strong></p> <p>Porcu, S., Floris, A., & Atzori, L. (2020). Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems. Electronics, 9, 1892, doi: 10.3390/electronics9111892, url: https://www.mdpi.com/2079-9292/9/11/1892.</p> <p>BibTex format:</p> <p>@article{porcu2020evaluation, title={Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems}, author={Porcu, Simone and Floris, Alessandro and Atzori, Luigi}, journal={Electronics}, volume={9}, pages={108781}, year={2020}, number = {11}, article-number = {1892}, publisher={MDPI}, doi={10.3390/electronics9111892} }</p> <p> </p>
Covert System For Detecting Nuclear Dirty Bombs In Public Venues Image Files
<p>These files are figure images for the CRABS report. <a href="https://doi.org/10.12688/nuclscitechnolopenres.17455.1" target="_blank" rel="noopener noreferrer">https://doi.org/10.12688/nuclscitechnolopenres.17455.1</a></p>
Magnetic resonance imaging of the octopus nervous system
<p>Anatomical magnetic-resonance imaging of an octopus vulgaris with 0.5mm slice thickness and 0.25 by 0.25 mm in-plane resolution.</p> <p>The companion data paper details the acquisition procedure.</p>
3D ground truth annotations of cleared whole mouse brain nuclei imaged with a mesoSPIM system
<p>Please see Achard et al. 2024 for Data Card and other information.</p>
Repository of fully reproducible 193+ power systems images created with PyPSA-Earth for all UN countries
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Data from: GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown roots systems
Root systems develop different root types that individually sense cues from their local environment and integrate this information with systemic signals. This complex multi-dimensional amalgam of inputs enables continuous adjustment of root growth rates, direction and metabolic activity that define a dynamic physical network. Current methods for analyzing root biology balance physiological relevance with imaging capability. To bridge this divide, we developed an integrated imaging system called Growth and Luminescence Observatory for Roots (GLO-Roots) that uses luminescence-based reporters to enable studies of root architecture and gene expression patterns in soil-grown, light-shielded roots. We have developed image analysis algorithms that allow the spatial integration of soil properties, gene expression and root system architecture traits. We propose GLO-Roots as a system that has great utility in presenting environmental stimuli to roots in ways that evoke natural adaptive responses and in providing tools for studying the multi-dimensional nature of such processes.
Data for Martian Gravity Waves Observed by the Thermal Emission Imaging System (THEMIS) during Northern Summer
<p>README Data for <br> "Martian Gravity Waves Observed by the Thermal Emission Imaging System (THEMIS) during Northern Summer"<br> J. Michael Battalio et al.,<br> November 2022<br> (https://battalio.com)<br> michael[at]battalio[dot]com</p> <p>In this dataset are:<br> GW_readme.txt (this file)</p> <p>numpy compressed files<br> GWDaytime.npz<br> GWNighttime.npz<br> GWSpectrumMY##.npz, where "##" run the eight Mars years from 26 to 33, inclusive.<br> GWSpectrumNightMY##.npz, where "##" run the eight Mars years from 26 to 33, inclusive.</p> <p>Python notebook file:<br> THEMIS_GW_LOAD.ipynb (minimal notebook to read *.npz files)</p> <p>--The following arrays are in GWDaytime.npz:<br> gwArr, gwPeaks, gwArrMax, gwPeaksMax, gwNames, <br> gwArrSym, gwPeaksSym, gwArrSymMax, gwPeaksSymMax, gwNamesSym</p> <p>--The following arrays are in GWNighttime.npz:<br> gwArrNight, gwArrNightSym, gwPeaksNight, gwPeaksNightSym, gwArrNightMax, gwArrNightSymMax, gwPeaksNightMax, gwPeaksNightSymMax, gwNamesNight, gwNamesNightSym</p> <p>The arrays have the following format: Each row in gwArr, gwPeaks, gwArrMax, gwPeaksMax corresponds to a THEMIS swath or sub swath for the name given at the same column index as the gwNames vector. Similarly for gwArrSym, gwPeaksSym, gwArrSymMax, gwPeaksSymMax to gwNamesSym and for the GWNighttime file.</p> <p>gwArr, gwArrMax, gwArrNight, gwArrNightMax, gwArrSym, gwArrSymMax, gwArrNightSym, gwPeaksNightSym array columns are:<br> Swath centroid Longitude, swath centroid Latitude, Spectrum maximum, baseline orientation bin of maximum, baseline length bin of maximum, empty, areocentric longitude, local time, mean of whole spectrum, standard deviation of whole spectrum, Mars year, median of whole spectrum</p> <p>gwPeaks, gwPeaksMax, gwPeaksSym, gwPeaksSymMax, gwPeaksNightMax, gwPeaksNightSymMax, gwPeaksNight, gwPeaksNightSym array dimensions are: Dim1 corresponds to each THEMIS swath given by gwNames, gwNamesSym, gwNamesNight, or gwNamesNightSym. Dim2 corresponds to each of the top-10 peaks in the spectrum (Note: Peak one is identical to the peak given in column 3 of the "Arr" variables.). Dim 3 provides the baseline orientation bin of the corresponding peak, baseline length bin of the corresponding peak, the value of the peak.</p> <p><br> --The following arrays are in each GGWSpectrumMY##.npz:<br> gwNamesMY##,gwNamesSymMY##,spectrumArrMY##,spectrumArrMaxMY##,spectrumArrSymMY##,spectrumArrSymMaxMY##</p> <p>spectrumArrMY##,spectrumArrMaxMY##,spectrumArrSymMY##,spectrumArrSymMaxMY## array dimensions are: Dim1 corresponds to each THEMIS swath given by gwNamesMY##, gwNamesSymMY##. Dim2 corresponds to the baseline orientation from 90° east to 90° west of a due north-south orientation. Dim 3 provides the baseline length from 0 to 500 km for full swaths or 0 to 60 km for symmetric swaths.</p> <p>--Similarly, the following arrays are each GGWSpectrumNightMY##.npz:<br> gwNamesNightMY##,gwNamesNightSymMY##,spectrumArrNightMY##,spectrumArrNightMaxMY##,spectrumArrNightSymMY##,spectrumArrNightSymMaxMY## The variable dimensions follow the same convention as the daytime files.</p>
Non-Invasive Non-Ionizing Polarized Imaging System to Assess Structure of Cervix
ClinicalTrials.gov study NCT00955864. IPD Sharing: Not stated. Countries: 1. Publications: 3.
A Feasibility Study of a Modular Video Augmentation System for Image-Guided Skull Base Surgery
ClinicalTrials.gov study NCT01591954. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Image Based Mobile System for Dietary Assessment and Coaching
ClinicalTrials.gov study NCT03897426. IPD Sharing: NO. Countries: 0. Publications: 1.
Imaging of Intravenous (IV) Combidex to Brain, Intra-cerebral Tumors and in Central Nervous System (CNS) Inflammation
ClinicalTrials.gov study NCT00659334. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Establishment and Evaluation of Multimodal Image Recognition System of Glioma Based on Deep Learning
ClinicalTrials.gov study NCT04407039. IPD Sharing: UNDECIDED. Countries: 0. Publications: 4.
Two-Part Study to Evaluate the Safety and Efficacy of Image Guided Surgery Using Indocyanine Green for Intramolecular Imaging of Nervous System Tumors Compared to Standard of Care, (TumorGlow)
ClinicalTrials.gov study NCT03262636. IPD Sharing: Not stated. Countries: 0. Publications: 2.
Hyperpolarized Carbon C 13 Pyruvate Magnetic Resonance Spectroscopic Imaging in Detecting Lactate and Bicarbonate in Participants With Central Nervous System Tumors
ClinicalTrials.gov study NCT03565367. IPD Sharing: NO. Countries: 1. Publications: 0.
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.