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Dataset results
40 results for “multi-modal images”
Multi-modal image analysis for large scale cancer tissue studies within IMMUcan: multiplex immunofluorescence images
<p>In cancer research, multiplexed imaging has enabled the in-depth characterization of the tumor microenvironment (TME) and how it relates to patient prognosis. However, standardized, multi-modal data from large numbers of patients to identify robust biomarkers is missing. To provide such data across five cancer indications, the IMMUcan consortium performs broad molecular and cellular spatial profiling of thousands of cancer samples. Two reproducible and scalable workflows have been developed for whole slide multiplexed immunofluorescence (mIF) and imaging mass cytometry (IMC) to overcome challenges of reproducibility and scalability. For mIF we developed IFQuant, a web-based tool optimized for user-friendliness and reproducibility. This Zenodo record contains the mIF images and IFQuant settings to reproduce the results presented in the referenced publication. The companion IMC dataset is available as a joint Zenodo record.</p>
Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography
<p>Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe<sub>3</sub>O<sub>4</sub> metamaterial, Co<sub>3</sub>O<sub>4</sub> - Mn<sub>3</sub>O<sub>4</sub> core-shell nanocrystals, and ZnS-Cu<sub>0.64</sub>S<sub>0.36</sub> nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.</p>
A Multi-modal, Physician-centered Intervention to Improve Guideline-concordant Prostate Cancer Imaging
ClinicalTrials.gov study NCT03445559. IPD Sharing: NO. Countries: 1. Publications: 19.
Multi-modality medical image dataset for medical image processing in Python lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="../records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a></li> <li>Data for the anonymization exercises: ultrasound (<em>identifiable_us.jpg</em>) dowloaded from <a href="https://www.flickr.com/photos/jcarter/2461223727">here</a>, and DICOM data (<em>our_sample_dicom.dcm</em>) shared for this course specifically by a colleague</li> <li>Histopathology data: histopathology slide images from <a href="https://openslide.org/">openslide</a> library samples in the freely distributable test data </li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Data for 'Multi-modal microscopy imaging with the OpenFlexure Delta Stage'
<p>Image data for 'Multi-modal microscopy imaging with the OpenFlexure Delta Stage'</p>
STS-Tooth: A multi-modal dental dataset for semi-supervised deep learning image segmentation
<p>In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions. However, the scarcity of publicly available PXI and CBCT datasets hampers their use in tooth segmentation tasks. Therefore, this paper presents a multimodal dataset for semi-supervised deep learning in dental PXI and CBCT, named STS-2D-Tooth and STS-3D-Tooth. STS-2D-Tooth includes 4,000 images and 900 masks, categorized by age into children and adults. Moreover, we have collected CBCTs providing more detailed and three-dimensional information, resulting in the STS-3D-Tooth dataset comprising 148,400 unlabeled scans and 8,800 masks. To our knowledge, this is the first multimodal dataset combining dental PXI and CBCT, and it is the largest tooth segmentation dataset, a significant step forward for the advancement of tooth segmentation.</p>
LCPUFA Supplementation: A Multi-Modality Imaging Study
ClinicalTrials.gov study NCT02076048. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Carotid Artery Multi-modality Imaging Prognostic (CAMP) Study
ClinicalTrials.gov study NCT04679727. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Evaluation of Kidney Function by Multi-modal Magnetic Resonance Imaging and Spectroscopy
ClinicalTrials.gov study NCT00575432. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
Multi-modality Imaging in Acute Myocardial Infarction
ClinicalTrials.gov study NCT02926755. IPD Sharing: NO. Countries: 1. Publications: 1.
Multi-modality Prostate Cancer Image Guided Interventions
ClinicalTrials.gov study NCT04009174. IPD Sharing: YES. Countries: 1. Publications: 2.
Multi-modality Imaging and Collection of Biospecimen Samples in Understanding Bone Marrow Changes in Patients With Acute Myeloid Leukemia Undergoing TBI and Chemotherapy
ClinicalTrials.gov study NCT03422731. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Clinical Feasibilityof a Multi-modal Photoacoustic/Ultrasonic Imaging System in Rheumatoid Arthritis
ClinicalTrials.gov study NCT04297475. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Adaptive Dose-Escalated Multi-modality Image-guided RadiothErapy
ClinicalTrials.gov study NCT03376386. IPD Sharing: NO. Countries: 1. Publications: 2.
Multi-modality Imaging (PCa) Using Sodium MRI and PSMA PET in Men Pre-prostatectomy
ClinicalTrials.gov study NCT04053842. IPD Sharing: NO. Countries: 1. Publications: 25.
Multi-modality Imaging in Peritoneal Carcinomatosis of Colorectal Origin
ClinicalTrials.gov study NCT03699332. IPD Sharing: NO. Countries: 1. Publications: 1.
Multi-modality Imaging & Immunophenotyping of COVID-19 Related Myocardial Injury
ClinicalTrials.gov study NCT04412369. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Multi-modality Imaging of Ischemia With 18F-FDG PET and CTA
ClinicalTrials.gov study NCT01520025. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Sex-driven modifiers of Alzheimer's risk: a multi-modality brain imaging study
<p><span><b>Objective:</b> To investigate sex differences in late-onset Alzheimer's disease (AD) risks by means of multi-modality brain biomarkers <span>[β-amyloid load via </span><sup>11</sup>C-PiB<span> PET, and neurodegeneration via </span><sup>18</sup>F-FDG <span>PET and structural MRI]</span>.</span></p> <p><span><b>Methods:</b> We examined 121 cognitively normal participants [85 women and 36 men] ages 40-65, with clinical, laboratory, neuropsychological, lifestyle exams, and MRI, FDG- and PiB-PET exams. Several clinical (e.g. age, education, APOE status, family history), medical (e.g., depression, diabetes, hyperlipidemia), hormonal (e.g. thyroid disease, menopause), and lifestyle AD risk factors (e.g., smoking, diet, exercise, intellectual activity) were assessed. Statistical parametric mapping and LASSO regressions were used to compare AD-biomarkers between men and women, and to identify the risk factors associated with sex-related differences. </span></p> <p><b><span>Results:</span></b> Groups were comparable on clinical and cognitive measures. Adjusting for each modality-specific confounders, the female group showed higher PiB <span>β-amyloid </span>deposition, lower FDG glucose metabolism, and lower MRI gray and white matter volumes compared to the male group (p<0.05, FWE-corrected for multiple comparisons). The male group did not show biomarker abnormalities compared to the female group. Results were independent of age and remained significant using age-matched groups. Second to female sex, menopausal status was the predictor most consistently and strongly associated with the observed brain biomarker differences, followed by hormone therapy, hysterectomy status, and thyroid disease.</p> <p><span><b><span>Conclusion:</span></b><span> Hormonal risk factors, in particular menopause, predict AD-endophenotype in middle-aged women. These findings suggest that the window of opportunity for AD-preventative interventions in women is early in the endocrine aging process.</span></span></p>
M3: A Multi-Image Multi-Modal Entity Alignment Dataset
<p><span>M3, an MMEA benchmark equipped with multiple images retrieved from respective search engines, which better mirrors real-life challenges. We utilize the widely used DBP15K dataset as the foundational dataset, which includes three cross-ingual datasets: Chinese-English (ZH-EN), Japanese-English (JA-EN), and French-English (FR-EN).</span></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.