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162
datasets available to search
ShareScore release 0.9.0
Dataset results
162 results for “Quantitative Imaging”
Quantitative Non-Invasive Brain Imaging Using QUTE-CE MRI
ClinicalTrials.gov study NCT03266848. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Quantitative-imaging in Cardiac Transthyretin Amyloidosis
ClinicalTrials.gov study NCT05776212. IPD Sharing: YES. Countries: 1. Publications: 1.
Quantitative MRI Imaging in Diffuse Liver Diseases
ClinicalTrials.gov study NCT04626492. IPD Sharing: NO. Countries: 1. Publications: 15.
Quantitative Evaluation of Osteopathic Treatment in Temporomandibular Joint Disorders Using Magnetic Resonance Imaging
ClinicalTrials.gov study NCT04468464. IPD Sharing: NO. Countries: 1. Publications: 2.
Assessing and Improving Quantitative Magnetic Resonance Imaging Metrics in Human Subjects
ClinicalTrials.gov study NCT05604534. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
QUANTIFY: Quantitative Understanding of Advanced Novel Techniques for Imaging Fasciitis and Yielding Biomarkers
ClinicalTrials.gov study NCT06803056. IPD Sharing: YES. Countries: 1. Publications: 0.
Quantitative GLP-1 Receptor Imaging Correlated to ex Vivo Distribution of In-111-exendin
ClinicalTrials.gov study NCT03889496. IPD Sharing: NO. Countries: 1. Publications: 1.
Rapid Breath-hold Quantitative Macromolecular Proton Fraction Imaging for Liver Fibrosis
ClinicalTrials.gov study NCT04429100. IPD Sharing: NO. Countries: 1. Publications: 24.
Quantitative Magnetic Resonance Imaging to Aid Clinical Decision Making in Autoimmune Hepatitis.
ClinicalTrials.gov study NCT03979053. IPD Sharing: NO. Countries: 1. Publications: 3.
Evaluation of Neuropathic Pain in the Context of Clinical Symptoms, Quantitative Sensory Tests and Imaging With FMRI
ClinicalTrials.gov study NCT03890315. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Quantitative Fluorescence Molecular Imaging of Ustekinumab-800CW to Elucidate the Drug Distribution Throughout Inflamed Tissue in Crohn's Disease and Psoriasis.
ClinicalTrials.gov study NCT05725876. IPD Sharing: NO. Countries: 1. Publications: 0.
Study on the Correlation Between the Quantitative Parameters of Mr Mean Cell Size Imaging and the Histopathological Characteristics of Breast Cancer
ClinicalTrials.gov study NCT05373628. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Development of Quantitative Ultrasound Imaging Software Platform
ClinicalTrials.gov study NCT05836246. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Quantitative Imaging Biomarkers for Sarcoma
ClinicalTrials.gov study NCT02579980. IPD Sharing: UNDECIDED. Countries: 1. Publications: 38.
HepQuant: Study to Assess the Role of Blood-based Biomarkers and Quantitative MR Imaging for Patients Receiving Radiation Therapy for Liver Cancer
ClinicalTrials.gov study NCT06899152. IPD Sharing: NO. Countries: 1. Publications: 25.
Data from: Ranking quantitative resistance to Septoria tritici blotch in elite wheat cultivars using automated image analysis
Open the record for dataset details and reuse information.
Data from: Radar wind profilers and avian migration - a qualitative and quantitative assessment verified by thermal imaging and moon watching
Open the record for dataset details and reuse information.
Data from: Robust extraction of quantitative structural information from high-variance histological images of livers from necropsied Soay sheep
Quantitative information is essential to the empirical analysis of biological systems. In many such systems, spatial relations between anatomical structures is of interest, making imaging a valuable data acquisition tool. However, image data can be difficult to analyse quantitatively. Many image processing algorithms are highly sensitive to variations in the image, limiting their current application to fields where sample and image quality may be very high. Here, we develop robust image processing algorithms for extracting structural information from a dataset of high-variance histological images of inflamed liver tissue obtained during necropsies of wild Soay sheep. We demonstrate that features of the data can be measured in a fully automated manner, providing quantitative information which can be readily used in statistical analysis. We show that these methods provide measures that correlate well with a manual, expert operator-led analysis of the same images, that they provide advantages in terms of sampling a wider range of information and that information can be extracted far more quickly than in manual analysis.
Non-human primate white matter development during the first year of life as assessed by diffusion tensor imaging and quantitative relaxometry
<p>In this project, we use diffusion tensor imaging (DTI) and quantitative relaxometry (QR) metrics to assess longitudinal changes in white matter (WM) throughout the postnatal rhesus macaque brain across the first year of life. DTI and QR metrics are measured at 3, 7, 13, 25, and 53 weeks of age in each of 35 non-human primates (NHPs) in this sample, for a total 175 datapoints (i.e., scans). Leveraging a previously published NHP WM atlas (Adluru et al., 2012; Zakszewski et al., 2014), we assess WM microstructure in 76 WM regions across the brain. DTI metrics include fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); the QR metric assessed is the longitudinal relaxation rate (qR<sub>1</sub>). These longitudinal data enable the direct comparison of DTI and QR metrics in very early life on a within-subject level, as well as robust, multimodal modeling of WM growth in infancy, providing insights into early postnatal neurodevelopment that may have important implications neuropsychiatric and neurodevelopmental disorders in childhood.</p>
SQUID- Stereo Quantitative Underwater Image Dataset
<p>Underwater Single Image Color Restoration</p> <p>SQUID- Stereo Quantitative Underwater Image Dataset</p> <p><a href="http://www.eng.tau.ac.il/~berman">Dana Berman</a>, Deborah Levy, <a href="http://www.eng.tau.ac.il/~avidan/">Shai Avidan, </a><a href="https://www.viseaon.haifa.ac.il/">Tali Treibitz</a></p> <p> </p> <p>Abstract</p> <p>Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We collected a dataset of images taken in different locations with varying water properties, showing color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of our method.</p> <p> </p> <p>Publication</p> <p>The paper is availbale on <a href="https://arxiv.org/abs/1811.01343">arXiv</a>.</p> <p>If you use this dataset please cite it as SQUID [ref].<br> [Ref] Berman, Dana, Deborah Levy, Shai Avidan, and Tali Treibitz. "Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).<br> <br> Bibtex entry:<br> @article{berman2020underwater,<br> title={Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset},<br> author={Berman, Dana and Levy, Deborah and Avidan, Shai and Treibitz, Tali},<br> journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},<br> year={2020}<br> }</p> <p>Source Code</p> <p>The code is availbale on GitHub: <a href="https://github.com/danaberman/underwater-hl">https://github.com/danaberman/underwater-hl</a>.</p> <p>Dataset</p> <p>The dataset includes RAW images, TIF files, camera clibration files, and distance maps.<br> The database contains 57 stereo pairs from four different sites in Israel, two in the Red Sea (representing tropical water) and two in the Mediterranean Sea (temperate water). In the Red Sea the sites were a coral reef ('Katzaa') which is 10-15 meters deep (15 pairs) and a shipwreck ('Satil'), 20-30 meters deep (8 pairs). In the Mediterranean Sea both sites were rocky reef environments, separated by 30km, Nachsholim at 3-6 meters depth (13 pairs), and Mikhmoret at 10-12 meters depth (21 pairs).<br> <br> For convenience it is divided to the 4 dive sites.<br> <br> <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/files/README.md">README file</a>.<br> If you use this data, please <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/underwater_haze_lines_and_dataset.bib">cite the paper</a>.<br> To evaluate your own results, please use this <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/files/underwater-dataset-evaluation-undisorted-params.zip">evaluation code</a>.</p> <p><br> </p> <p>References</p> <p><strong>[Drews et al. 2013]</strong> P. Drews, E. Nascimento, F. Moraes, S. Botelho, and M. Campos. Transmission estimation in underwater single images. In <em>Proc. IEEE ICCV Underwater Vision Workshop, pages 825–830,</em> 2013.<br> <strong>[Peng et al. 2015]</strong> Y.-T. Peng, X. Zhao, and P. C. Cosman. Single underwater image enhancement using depth estimation based on blurriness. In <em>Proc. IEEE ICIP</em>, 2015.<br> <strong>[Ancuti et al. 2016]</strong> C. Ancuti, C. O. Ancuti, C. De Vleeschouwer, R. Garcia, and A. C. Bovik. Multi-scale underwater descattering. In <em>Proc. ICPR</em>, 2016.<br> <strong>[Ancuti et al. 2017]</strong> C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, L. Neumann, and R. Garcia. Color transfer for underwater dehazing and depth estimation. In <em>Proc. IEEE ICIP</em>, 2017<em>(All color transfers were done with a single image)</em>.<br> <strong>[Emberton et al. 2017]</strong>S. Emberton, L. Chittka, and A. Cavallaro, Underwater image and video dehazing with pure haze region segmentation, <em>Computer Vision and Image Understanding</em>, 2017.<br> <strong>[Ancuti et al. 2018]</strong> . O. Ancuti, C. Ancuti, C. De Vleeschouwer, and P. Bekaert. Color balance and fusion for underwater image enhancement. IEEE Transactions on Image Processing, 27(1):379–393, 2018.</p>
ScienceDex guides
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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.