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102
datasets available to search
ShareScore release 0.9.0
Dataset results
102 results for “spatial imaging”
Sex-biased architecture guides T cell development through spatially defined niches - CODEX images
Open the record for dataset details and reuse information.
CASSINI ORBITER SATURN UVIS SPATIAL SPECTRAL IMAGE CUBE V1.4
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER SATURN UVIS SPATIAL SPECTRAL IMAGE CUBE 1.0
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER SATURN UVIS SPATIAL SPECTRAL IMAGE CUBE V1.3
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER SATURN UVIS SPATIAL SPECTRAL IMAGE CUBE 1.1
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER SATURN UVIS SPATIAL SPECTRAL IMAGE CUBE 1.2
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER N/A UVIS SPATIAL SPECTRAL IMAGE CUBE 1.0
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER JUPITER UVIS SPATIAL SPECTRAL IMAGE CUBE 1.0
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus
GEO Series GSE308167. Homo sapiens; Mus musculus. 10 samples. Type: Other.
High resolution spatial trancrioptomics combined with deep learning-based image segmentation enables single-cell spatial profiling of archival FFPE kidney tissue from patients with idiopathic nephroti
GEO Series GSE300895. Homo sapiens. 3 samples. Type: Other.
Spatial Images 2022
<p>H&E Images for spatial transcriptomics project</p>
Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis [Visium]
GEO Series GSE276934. Homo sapiens. 1 samples. Type: Other.
Smaller is better? Unduly nice accuracy assessments in image classification due to spatial autocorrelation in identification of small sized objects
<p>Deriving the thematic accuracy of models is a fundamental part of image classification analyses. However, due to high spatial autocorrelation in remotely sensed imagery, accuracy assessments can be biased, which leads to relevant overestimation of accuracies. </p>
Mapping of cell types in the tumor microenvironment from tissue images via deep learning trained by spatial transcriptomics of lung adenocarcinoma
<p>Spatial transcriptomic data (10X Visium Platform) of lung adenocarcinoma that comprised thousands of spots with gene expression data and spatially registered H&E-stained tissue images from 22 samples.</p>
Dataset related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"
<p>This record contains raw data related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"</p> <p>Abstract</p> <p>Background</p> <p>Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).</p> <p>Methods</p> <p>In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi<sup>42</sup>(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability.</p> <p>Results</p> <p>In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF − 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s).</p> <p>Conclusions</p> <p>Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.</p>
CASSINI ORBITER SATURN UVIS SPATIAL SPECTRAL IMAGE CUBE 1.2
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER SATURN UVIS SPATIAL SPECTRAL IMAGE CUBE 1.0
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER JUPITER UVIS SPATIAL SPECTRAL IMAGE CUBE 1.0
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER SATURN UVIS SPATIAL SPECTRAL IMAGE CUBE V1.3
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
CASSINI ORBITER SATURN UVIS SPATIAL SPECTRAL IMAGE CUBE V1.4
Spectrographic observations of Jupiter, Saturnian rings, satellites, atmospheres and the interplanetary medium in the far and extreme ultraviolet.
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.