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Dataset results
240 results for “Tissue imaging”
Example image and Model for virtual histological staining of unlabeled autopsy tissue
<p>Example images can be found at "Example_images.zip". Here's what you will find:</p><ul><li><strong>Input</strong>: Autofluorescence images of unstained autopsy tissue sections; each FOV corresponds to a .mat file, with the 1st channel representing the DAPI channel and the 2nd channel representing the TxRed channel.</li><li><strong>Output</strong>: Virtually stained H&E images generated by the network model.</li><li><strong>Ground Truth</strong>: Histochemically stained H&E images for reference/comparison.</li></ul><p>10 individual FOVs are provided as example images. Each FOV measures 2048 x 2048 pixels, corresponding to roughly 333 x 333 µm². The 10 FOVs are labeled from "001" to "010".</p><ul><li>"001"-"005": These correspond to tissue regions suffering from severe autolysis, rendering staining artifacts in the histochemically stained images (ground truth).</li><li>"006"-"010": These correspond to well-preserved tissue regions with high histochemical staining quality.</li></ul><p>How to use the model file and the corresponding codes can be found at: https://github.com/liyuzhu1998/Autopsy-Virtual-Staining/tree/main</p>
Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy"
<p>Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy", <em>Nat Methods</em> (2024), https://doi.org/10.1038/s41592-024-02275-w.</p>
PathoEye: a deep learning framework for histopathological image analysis of skin tissue
<p>This dataset comprises two parts: one consisting of skin pathology slices near the epidermal layer of elderly individuals under sunlight exposure conditions, and the other comprising skin pathology slices near the epidermal layer of young individuals under the same conditions. Each type of image comes in two sizes: one is 128×128 pixels, and the other is 512×512 pixels.</p>
Datasets for "Automated Detection of Portal Fields and Central Veins in Whole-Slide Images of Liver Tissue"
<p>Datasets and results for the manuscript “Automated Detection of Portal Fields and Central Veins in Whole-Slide<br> Images of Liver Tissue” (Journal of Pathology Informatics 13 (2022) 100001, https://doi.org/10.1016/j.jpi.2022.100001)</p>
Imaging mass cytometry analysis of brain tissues with CNS immune-related adverse events during anti-PD-1 cancer immunotherapy
<p><span>Dataset accompanying the manuscript "Anti-PD-1 cancer immunotherapy induces CNS immune-related adverse events by Spleen tyrosine kinase activation in microglia".</span></p> <p><span>The Metadata.xls file includes the metadata, the raw image data is saved as .txt file, the segmented cellular expression data is available as csv files. </span></p>
Transwell-Based Microfluidic Platform for High-Resolution Imaging of Airway Tissues
<div> <div> <div> <div> <p>This dataset contains original data collected during our study on the development and characterization of a transwell-based microfluidic platform designed for high-resolution imaging of airway tissues. It includes quantitative measurements of various features of live airway epithelium tissues, such as Trans-Epithelial Electrical Resistance (TEER), Cilia Beating Frequency (CBF), LDH Release (a cytotoxicity assay), tissue thickness, and cell number. Additionally, the dataset provides results from computational simulations modeling the shear stress in the microchannel generated by perfusion.</p> </div> </div> </div> </div>
Test Dataset for 3D semantic image segmentation of the Breast, Fibrograndular Tissue, and Breast Carcinoma
Open the record for dataset details and reuse information.
Accompanying dataset for: "IBEX: An iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues"
<p>These datasets were acquired using either the manual or automated IBEX multiplex imaging protocols and accompany the manuscript “IBEX: An iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues”, A. Radtke <em>et al.</em>, 2021, Nature Protocols.</p> <p>All image data are stored using the <a href="https://imaris.oxinst.com/support/imaris-file-format">Imaris file format</a>. To view these multi-channel images, you can either use one of these <strong>free</strong> viewers, <a href="https://imaris.oxinst.com/imaris-viewer">Imaris viewer</a>, <a href="https://imagej.net/Fiji">Fiji</a>.</p> <p>Each experiment has an associated imaging meta-data file in xlsx format and the resulting image in Imaris format.</p> <p><strong>Human Jejunum (Automated)</strong></p> <p>Dataset is a 24 parameter IBEX experiment performed on a human jejunum section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Thunder 3D Cell Culture microscope with a high precision (Quantum) stage, a high quantum efficiency sCMOS camera (DFC9000 GTC), and a 40x (1.3) NA oil objective. The light source was an LED8 with 8 individual LED lines for excitation with millisecond triggering. All images were captured at a 16-bit depth with the following pixel dimensions: x (0.160 μm), y (0.160 μm), and z (1 μm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.7.1.21655).</p> <p><strong>Human Kidney (Automated)</strong></p> <p>Dataset is a 16 parameter IBEX experiment performed on a human kidney Formalin-Fixed Paraffin-Embedded (FFPE) section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Thunder 3D Cell Culture microscope with a high precision (Quantum) stage, a high quantum efficiency sCMOS camera (DFC9000 GTC), and a 40x (1.3) NA oil objective. The light source was an LED8 with 8 individual LED lines for excitation with millisecond triggering. All images were captured at a 16-bit depth with the following pixel dimensions: x (0.160 μm), y (0.160 μm), and z (1 μm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.7.1.21655).</p> <p><strong>Human Lymph Node (Automated)</strong></p> <p>Dataset is a 25 parameter IBEX experiment performed on a human lymph node section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Thunder 3D Cell Culture microscope with a high precision (Quantum) stage, a high quantum efficiency sCMOS camera (DFC9000 GTC), and a 40x (1.3) NA oil objective. The light source was an LED8 with 8 individual LED lines for excitation with millisecond triggering. All images were captured at a 16-bit depth with the following pixel dimensions: x (0.160 μm), y (0.160 μm), and z (1 μm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.7.1.21655).</p> <p><strong>Human Skin (Automated)</strong></p> <p>Dataset is an 18 parameter IBEX experiment performed on a human skin section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Thunder 3D Cell Culture microscope with a high precision (Quantum) stage, a high quantum efficiency sCMOS camera (DFC9000 GTC), and a 40x (1.3) NA oil objective. The light source was an LED8 with 8 individual LED lines for excitation with millisecond triggering. All images were captured at a 16-bit depth with the following pixel dimensions: x (0.160 μm), y (0.160 μm), and z (1 μm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.7.1.21655).</p> <p><strong>Human Liver (Manual)</strong></p> <p>Dataset is a 22 parameter IBEX experiment performed on a human liver section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Human Lymph Node (Manual)</strong></p> <p>Dataset is a 38 parameter IBEX experiment performed on a human lymph node section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Human Spleen (Manual)</strong></p> <p>Dataset is a 25 parameter IBEX experiment performed on a human spleen section labeled with the nuclear marker Hoechst and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p>
Imaging Mass Cytometry for high-dimensional tissue profiling in the eye
<p>Imaging mass cytometry data (folders containing single tiffs + cell masks) generated for the analysis of healthy conjunctiva and conjunctival melanoma.</p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 1 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the first part of 14 parts of the full dataset (1/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 10ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>Within this part, we also include the segmentation labels for each tissue.</p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 9 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the ninth part of 14 parts of the full dataset (9/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 15ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Galaxy Training Data for "End-to-End Tissue Microarray Image Analysis with Galaxy-ME"
<p>This dataset provides the inputs used in the Galaxy Training Network (GTN) training 'End-to-End Tissue Microarray Image Analysis with Galaxy-ME'. The tutorial demonstrates how to use the Galaxy-ME tool suite for primary image processing, data analysis, and interactive visualization of multiple tissue imaging datasets. Original data was published by <a href="https://pubmed.ncbi.nlm.nih.gov/34824477/">Schapiro <em>et al</em></a>.</p>
Data from: 3-D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System (MANTIS)
<p><span class="pre-line-wrapping ng-binding">Routine clinical assays, such as conventional immunohistochemistry, often fail to resolve the regional heterogeneity of complex inflammatory skin conditions. Here we introduce MANTIS (Multiplexed Annotated Tissue Imaging System), a flexible analytic pipeline compatible with routine practice, specifically designed for spatially-resolved immune phenotyping of the skin in experimental or clinical samples. Based on phenotype attribution matrices coupled to alpha-shape algorithms, MANTIS projects a representative digital immune landscape, while enabling automated detection of major inflammatory clusters and concomitant single-cell data quantification of biomarkers. We observed that severe pathological lesions from systemic lupus erythematosus, Kawasaki syndrome, or COVID-19-associated skin manifestations share common quantitative immune features, while displaying a non-random distribution of cells with the formation of disease-specific dermal immune structures. Given its accuracy and flexibility, MANTIS is designed to solve the spatial organization of complex immune environments to better apprehend the pathophysiology of skin manifestations.</span></p>
Hyperspectral Placenta Dataset: Hyperspectral Image Acquisition, Annotations, and Processing of Biological Tissues in Microsurgical Training
<p>The dataset consists of 101 hyperspectral images of four fresh human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset can be used to develop machine learning algorithms for the automated classification of biological structures, particularly the classification of superficial and deep vessels and transparent tissue layers.</p>
Image-Guided Radiation Therapy in Treating Patients With Primary Soft Tissue Sarcoma of the Shoulder, Arm, Hip, or Leg
ClinicalTrials.gov study NCT00589121. IPD Sharing: Not stated. Countries: 2. Publications: 2.
VIO Imaging for Skin Tissue Assessment (VISTA)
ClinicalTrials.gov study NCT05619471. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Nucleus feature profiles from GTEx histological images across 12 tissues
Open the record for dataset details and reuse information.
Whole mount 3D imaged adipose tissue demonstrating sympathetic neurons and blood vessels during cold exposure and thermoneutrality
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Positive-going hybrid indicators for voltage imaging in excitable cells and tissues
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Data from: 3-D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System (MANTIS)
Open the record for dataset details and reuse information.
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.