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Dataset results
240 results for “Tissue imaging”
Data from: Deep-tissue optical imaging of near cellular-sized features
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Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues - MERSCOPE Data
GEO Series GSE308147. Homo sapiens; synthetic construct. 6 samples. Type: Other.
Multiplexed imaging of nucleome architectures in single cells of mammalian tissue
GEO Series GSE148072. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Fibrotic Remodelling of Epicardial Adipose Tissue in Patients with Atrial Fibrillation: Comparative Analyses of Histological Findings and Computed Tomographic Imaging
GEO Series GSE154436. Homo sapiens. 6 samples. Type: Expression profiling by array.
Spatial Immune Associations of Immunotherapy Response in Non-Small Cell Lung Cancer by Multiplexed Tissue Imaging
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Near Infrared Spectroscopy Imaging of Change in Tissue Oxygenation to Assess Peripheral Artery Disease
ClinicalTrials.gov study NCT07397494. IPD Sharing: NO. Countries: 0. Publications: 0.
3D Structural Imaging of Human Breast Tissues
ClinicalTrials.gov study NCT06454214. IPD Sharing: NO. Countries: 0. Publications: 0.
NIFR Image-guided Surgery for Malignant Soft Tissue Tumor With Low-dose SWIG Technique
ClinicalTrials.gov study NCT06610071. IPD Sharing: YES. Countries: 0. Publications: 0.
Diagnostic Efficacy of FFOCT Imaging for Tissue Sample Obtained by EUSFNB
ClinicalTrials.gov study NCT04153318. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Tissue Doppler Imaging (TDI) Versus Electrocardiography (ECG) Interventricular Pacing Delay Optimization in Cardiac Resynchronization Therapy (CRT)
ClinicalTrials.gov study NCT01179997. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.
MyData: A Comprehensive Database of Mycetoma Tissue Microscopic Images
<p>This dataset provides the first comprehensive collection of histopathological images for the study and automated diagnosis of mycetoma, a neglected tropical disease. The dataset includes 864 microscopic images from 142 patients, annotated with binary masks for grain detection and segmentation. The images represent both eumycetoma (fungal, FM) and actinomycetoma (bacterial, BM) cases, offering a valuable resource for developing and evaluating AI models for disease classification and segmentation tasks. This dataset supports the advancement of digital pathology solutions.</p> <p><strong>Keywords: </strong>Mycetoma, histopathology diagnosis, Microscopic images, Image Analysis, Classification, Segmentation.</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 "Diffusion weighted magnetic resonance imaging for kidney cyst volume quantification and non-cystic tissue characterization in ADPKD"
<p>Kidney volumes, demographic features, and median DWI-based parameters related to the individual patients and healthy volunteers included in the study</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 article "Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction"
<p><strong>SCORING SEGMENTATIONS</strong></p> <ul> <li>Qualitative segmentation scores by two raters (rater1; rater2).</li> <li>Scale: excellent (4); good (3); doubtful (2) and failed (1).</li> </ul> <p> </p> <p><strong>DATABASES</strong></p> <ul> <li>IXI database, Imperial College of London (<a href="https://brain-development.org/ixi-dataset/">https://brain-development.org/ixi-dataset/</a>)</li> <li>Autism Brain Imaging Data Exchange (ABIDE) database (<a href="http://fcon_1000.projects.nitrc.org">http://fcon_1000.projects.nitrc.org</a>)</li> <li>SchizConnect database (<a href="http://schizconnect.org">http://schizconnect.org</a>)</li> </ul> <p> </p> <p><strong>SEGMENTATION METHODS</strong></p> <ul> <li>MR-TIM (Taberna et al., 2021), green rows</li> <li>WTS (Liu et al., 2017), red rows</li> </ul> <p> </p> <p><strong>TABLES</strong></p> <p><strong>IXI_young </strong></p> <ul> <li>20 MRI from the IXI database, participants 20–35 years old;</li> <li>MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G)</li> </ul> <p><strong>IXI_older</strong></p> <ul> <li>20 MRI from the IXI database, participants 60–75 years old;</li> <li>MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G)</li> </ul> <p><strong>ABIDE</strong></p> <ul> <li>10 MRI from the ABIDE database, participants 18-25 years old;</li> <li>MR scanner: Philips Achieva 3.0T</li> </ul> <p><strong>SchizConnect</strong></p> <ul> <li>10 MRI from the SchizConnect database, participants 19-66 years old;</li> <li>MR scanner: Siemens Trio Tim 3.0T</li> </ul> <p> </p> <p><strong>REFERENCES</strong></p> <p>Liu, Q., Farahibozorg, S., Porcaro, C., Wenderoth, N., & Mantini, D. (2017). Detecting large-scale networks in the human brain using high-density electroencephalography. Hum Brain Mapp, 38(9), 4631-4643. doi:10.1002/hbm.23688</p> <p>Taberna, G. A., Samogin, J., & Mantini, D. (2021). Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction. Neuroinformatics. doi:10.1007/s12021-020-09504-5</p>
Data set from the article Petrini M, Alì M, Cannaò PM, Zambelli D, Cozzi A, Codari M, Malavazos AE, Secchi F, Sardanelli F. Epicardial adipose tissue volume in patients with coronary artery disease or non-ischaemic dilated cardiomyopathy: evaluation with cardiac magnetic resonance imaging. Clin Radiol. 2019 Jan;74(1):81.e1-81.e7. doi: 10.1016/j.crad.2018.09.006. Epub 2018 Oct 15. PMID: 30336943.
<p>Data set from the article Petrini M, Alì M, Cannaò PM, Zambelli D, Cozzi A, Codari M, Malavazos AE, Secchi F, Sardanelli F. Epicardial adipose tissue volume in patients with coronary artery disease or non-ischaemic dilated cardiomyopathy: evaluation with cardiac magnetic resonance imaging. Clin Radiol. 2019 Jan;74(1):81.e1-81.e7. doi: 10.1016/j.crad.2018.09.006. Epub 2018 Oct 15. PMID: 30336943.</p> <p> </p> <p>This is the abstract:</p> <p><strong>Aim: </strong> To compare the amount of epicardial adipose tissue (EAT) in patients with coronary artery disease (CAD) or non-ischaemic dilated cardiomyopathy (NIDCM) with that in patients with negative cardiac magnetic resonance imaging (CMR).</p> <p><strong>Materials and methods: </strong> One hundred and fifty patients (median age 57 years, interquartile range [IQR] 46-66 years) who underwent CMR were evaluated retrospectively: 50 with CAD, 50 with NIDCM, and 50 with negative CMR. For each patient, the EAT mass index (EATMI) to body surface area, end-diastolic volume index (EDVI), end-systolic volume index (ESVI), stroke volume (SV), ejection fraction (EF) for both ventricles, and left ventricle (LV) mass index were estimated. Intra and inter-reader reproducibility was tested in a random subset of 30 patients, 10 for each group. Mann-Whitney U test, Kruskal-Wallis test, Spearman's correlation, and Bland-Altman statistics were used.</p> <p><strong>Results: </strong> The EATMI in CAD patients (median 15.7 g/m<sup>2</sup>, IQR 8.3-25.7) or in NIDCM patients (15.9 g/m<sup>2</sup>, 11.5-18.1) was significantly higher than that in negative CMR patients (9.1 g/m<sup>2</sup>, 6-12; p<0.001 both). No significant difference was found between CAD and NIDCM patients (p=1.000). A correlation between EATMI and LV mass index was found in NIDCM patients (r=0.455, p=0.002). Intra- and inter-reader reproducibility were up to 80% and 72%, respectively.</p> <p><strong>Conclusion: </strong> Patients with NIDCM or CAD exhibited an increased EATMI in comparison to negative CMR patients. CMR can be used to estimate EAT with good reproducibility.</p>
Auguste and Ferro et al. Fixed Tissue Raw Images
<p>Within each compressed ZIP file are the raw fixed tissue images used for the majority of data presented in figures 1-3. The image nomenclature is in a blinded state and a key to the blinding can be found within each dataset. </p> <p>Also, find attached the in-house ImageJ macro used throughout the paper. </p>
Prognostic Relevance of Left Ventricular Thrombus Motility: Assessment by Pulsed Wave Tissue Doppler Imaging
<p>Raw data.</p> <p>Abstract<br> Pulsed wave tissue Doppler imaging (PW-TDI) easily detects motion of cardiac structures. Hence, PW-TDI could be of value for<br> assessing potentially cardioembolic masses. We sought to evaluate the prognostic value of left ventricular (LV) thrombus mobility<br> assessed by PW-TDI. In 83 consecutive patients with echocardiographically detected LV thrombi, PW-TDI echocardiographic<br> study was performed. At 1-year follow-up, the composite of major adverse cardiovascular events (MACE) defined as all-cause<br> mortality plus hospitalizations for stroke/systemic embolism was evaluated. Seventy-two patients (77.1 + 13.1 year/old,<br> 32 males) were studied. All thrombi were located at the LV apex. At 1-year follow-up, 17 cardioembolic events occurred. By<br> univariable Cox analysis, variables associated with MACE were heart rate (hazard ratio: 1.02, 95% CI: 1.00-1.05; P ¼ .03), thrombi<br> with mobile free edge (hazard ratio: 3.25, 95% CI: 1.25-8.44; P ¼ .01), hypoechoic thrombi (hazard ratio: 2.86, 95% CI: 1.10-7.42;<br> P ¼ .03), and mass peak antegrade velocity (Va) 10 cm/s (hazard ratio: 8.79, 95% CI: 2.00-38.5; P ¼ .004). By multivariable<br> analysis, thrombi with mobile free edge (hazard ratio: 3.54, 95% CI: 1.23-10.2; P ¼ .02), and mass peak Va 10 cm/s (hazard ratio:<br> 7.97, 95% CI: 1.60-39.6; P ¼ .01) retained statistical significance. Mass peak Va 10 cm/s predicted the composite end point with<br> 94% sensitivity and 85% specificity (area under the curve ¼ 0.86). In conclusion, PW-TDI allows objective prognostication of LV<br> thrombi embolic risk.</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.