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Dataset results
282 results for “image segments”
DAVE - Images from Trave for recognition or segmentation
<p>The dataset contains images from a measurement run on the River Trave, with selected objects blurred to ensure privacy. Each image is accompanied by a JSON file that provides the coordinates of the blurred bounding box and the object category.</p> <p>Image data was recorded using a Hik Vision DS-2CD2T47G2-LSU/SL camera.</p> <p> </p> <div> <div> <div> <p>This publication is a result of the research of the Center of Excellence CoSA and funded by the Federal Ministry for Digital and Transport of the Federal Republic of Germany (Id 19F2225C, DAVE).</p> <p> </p> <p>Project website: https://www.th-luebeck.de/cosa/projekt/dave/</p> </div> </div> </div>
MedSegBench: A Comprehensive Benchmark for Medical Image Segmentation in Diverse Data Modalities
<p>We split the dataset into two parts due to the maximum uploaded file limit. You can find other data in Version 1.</p> <p>This dataset is an article study and is under evaluation.</p> <p><code>You can access the article on <a href="https://www.nature.com/articles/s41597-024-04159-2">Nature</a>.</code></p> <p><code>Trained model weights and detailed prediction results for each dataset (<a href="../records/13381081" target="_blank" rel="noopener">Zenodo</a>)</code></p>
Dataset with segmentations of 75 stained cell images over P3HBV polymer films
<div> <p>We segmented 75 images of stained cells, divided in 5 folders according the thickness of the <strong>Poly-3-hydroxyvalerate</strong> films.</p> <p>P3HBV polymer films of different thicknesses were prepared by casting solution technique.</p> <p>For this, P3HBV was dissolved in chloroform to obtain homogeneous solutions with concentration of <strong>1.0, 1.5, 2.0, 2.5 and 3.0%</strong>. The films were dried at room temperature for 48 hours in a dust-free environment, allowing the chloroform to evaporate completely and resulting in solid films. Upper surfaces of the films were used for cell cultivation.</p> <p>Linear culture of mice fibroblasts NIH 3T3 was used to obtain the visual data of cell adhesion on P3HBV film samples. To assess the cytocompatibility of PHA samples, cells were seeded onto sterile polymer films samples at a density of 2 × 104𝑐𝑒𝑙𝑙𝑠/𝑐𝑚2 and cultured for 72 hours. After the incubation, the samples were washed with phosphate-buffered saline and cells were fixed with 4% paraformaldehyde solution.</p> <p>Cell membranes were permeabilized with 0.2% Triton-X, and the cytoplasm was stained with fluorescein isothiocyanate, FITC (green) (Sigma-Aldrich, USA), for 1 hour in the dark at room temperature. The nuclei were visualized using 4’,6-diamidino-2-phenylindole, DAPI (blue) (Sigma-Aldrich, USA).</p> <p>Cells were visualized using a Leica DMI8 fluorescent microscope with corresponding LAS X software.</p> <p>Per each sample, we include: A raw image (.tif) with 2 subfolders containing the segmented masks of their cells and nuclei respectively.</p> </div>
A comprehensive dataset of magnetic resonance enterography images with bowel segment annotations
<p>Inflammatory bowel disease (IBD) is a recurrent bowel disease that usually requires magnetic resonance enterography (MRE) for diagnosis and monitoring. However, recognition of bowel segments from MRE images by a radiologist is challenging and time-consuming. Deep learning-based medical image segmentation has shown the potential to reduce manual effort and provide automated tools to assist in disease management; however, it requires a large-scale fine<span>-</span>annotated dataset for training. To address this gap, we collected MRE data, including HASTE(half-Fourier acquisition single-shot turbo spin-echo) sequences with coronal orientation, from 114 patients with IBD. The bowel images per patient were contoured and annotated into ten segments (stomach, duodenum, small intestine, appendix, cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum), with fine pixel-level annotations labeled by experienced radiologists. Furthermore, we <span>validated</span> the efficiency of several state-of-the-art segmentation methods using this dataset. This study established a high-quality, publicly available whole-bowel segment MR dataset with benchmark results and laid the groundwork for AI research on IBD.</p>
LGE CMR SAX Phantom Images and Segmentation Masks
<h2>Synthetic imaging data used for analysis of Radiomic feature comparability</h2> <p>Phantom (synthetic) images of a single short-axis cardiovascular MRI slice of the heart (showing only the left ventricular myocardium and bloodpool), and corresponding segmentation masks in nifti format for whole myocardium and 17-segment AHA model. </p> <p>The phantom exists at a range of sizes (myocardial diameter between 28 and 99mm) and a range of resolutions (between 0.6 and 2.2mm isotropic voxel-size), as well as at resolutions corresponding to voxel-densities between 28 and 76 voxels per myocardial diameter.</p> <p>LGE patterns have been added to the healthy myocardium: global mesocardial LGE, global subendocardial LGE, global subepicardial LGE, inferolateral transmural LGE, and a patchy pattern. In each case, extent of LGE is 30% of the entire myocardium.</p> <p> </p> <p>Corresponding code can be found at: https://github.com/annplaube/mykkeLGEradiomics</p> <p> </p> <p>Naming conventions:</p> <p>Phantom: phantom_{LGE_pattern}_d{size}_{resampling_mode}.nii</p> <p>Label: label_d{size}_{resampling_mode}.nii</p> <p>AHA Label: label_aha_d{size}_{resampling_mode}.nii</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>
NasalSeg Dataset for Nasal Cavity and Paranasal Sinuses Segmentation from CT Images
<p>NasalSeg is the first large-scale, open-access dataset for nasal cavity and paranasal sinus segmentation from 3D CT images. The dataset comprises 130 CT scans with pixel-wise annotation of five anatomical structures including the left nasal cavity, right nasal cavity, nasal pharynx, left maxillary sinus, and right maxillary sinu.</p>
The AstroPath Image Acquisition and Segmentation Workflow
<p>Multidimensional, spatially resolved analyses of cells from pathology slides are of great diagnostic and prognostic interest. New multispectral, multiplex immunofluorescence microscopy platforms have the potential to facilitate such analyses, and here, we further improve and standardize the image acquisition and cell classification workflow. Studies to date on this emerging technology have typically assessed ~10 operator-dependent high power fields (HPFs) per slide, which represents a fraction of the tissue available for study. Standard cell segmentation and classification algorithms often oversegment larger cells, when they are segmented at the same time as smaller cells. Here we describe our AstroPath imaging platform, which addresses each of these considerations. In our study, slides from formalin-fixed paraffin embedded tissue specimens were stained with an optimized 6-plex multiplex immunofluorescence (mIF) assay. The slides were then scanned at 35 unique wavelengths using a multispectral microscope (Vectra 3.0 or Vectra Polaris) with 20% overlap of HPFs in an operator-independent fashion. An average of 1300 HPFs per slide was required to image the entire tissue, and each microscope scanned between 2 to 3 slides per day with this approach. After the images were captured and organized, overlaps were used to measure, quantify and correct systematics in the imagery (see Eminizer abstract). The central parts of the images were used to create a set of seamless “primary” tiles, similar to the strategy of the Sloan Digital Sky Survey, for a statistically fair pixel coverage of the whole tissue area (see Roskes abstract). Images were then linearly unmixed from the 35 wavelengths to 8 component layers (DAPI, tissue auto-fluorescence, and the 6 added fluorescent dyes) using inForm Cell Analysis©. We then employed a bespoke method for ‘multi-pass’ classification of cells wherein each marker was segmented and classified separately from the other markers, then merged into a single plane using a unique set of rules and predefined cell hierarchy. We showed that our segmentation and classification method reduced error in over-counting larger cells, e.g. tumor cells, by 25% and increased the specificity and sensitivity in each classification algorithm. Due to the amount of data, each algorithm was run automatically through one of 20 virtual machines housed on a set of servers in the Physics and Astronomy Department. Following the methodology developed during the SDSS project, image data was stored in a well-defined file system structure that facilitated further automatic processing and ingestion into a SQL Server database. Raw data for each slide was 200-300 GBs, which is on par with a full scale (30x) human genome. In summary, we have developed a unique facility and workflow that generates whole slide multispectral imagery with high-fidelity, single cell resolution. Our facility houses five multispectral microscopes (2 Vectra 3.0 and 3 Vectra Polaris) allowing us to collect a petabyte of raw data per year, on scale of the largest sky survey.</p>
Flywing (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Flywing (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Flywing (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)
<p><em><strong>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)</strong></em></p> <p>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)</p> <p><strong>Description</strong></p> <p>3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only</p> <p>These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Two data sources have been combined</p> <p><strong>Dataset 1</strong></p> <p>* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571<br> * Labels have been reclassified from 4 classes to 2 classes.<br> * Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.<br> * These images and labels have been made using the Doodleverse software package, Doodler*.</p> <p><strong>Dataset 2</strong></p> <ul> <li>3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******</li> <li>A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water</li> </ul> <p><strong>File descriptions</strong></p> <ul> <li> classes.txt, a file containing the class names</li> <li> images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li> labels.zip, a zipped folder containing the 1-band label images</li> <li> nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images</li> <li> swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images</li> <li> overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)</li> <li> resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li> resized_labels.zip, label images resized to 512x512x1 pixels</li> <li> resized_nir.zip, NIR images resized to 512x512x1 pixels</li> <li> resized_swir.zip, SWIR images resized to 512x512x1 pixels</li> </ul> <p>References</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.</p> <p>**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information</p> <p>****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p>*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/</p> <p>******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD3/CD20 (lymphocytes)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-ERG (endothelial cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</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.