Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
282
datasets available to search
ShareScore release 0.7.1
Dataset results
282 results for “image segments”
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-MNDA (myeloid 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>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD235a (red blood 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>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD45RB (leukocytes)
<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-αSMA (smooth muscle cells / cancer associated firbroblasts)
<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-panCK (epithelial 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>
Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p> <p>Classes: {0=other, 1=water}</p> <p><strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. <strong> '_model_history.npz'</strong> model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. <strong> '.png'</strong> model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p> </p> <p><strong>References</strong></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. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p>** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p>
Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images
<p><em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain 5-class segmentation of RGB 768x768 NAIP images</strong></em></p> <p>These Residual-UNet model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2</p> <p>Image size used by model: 768 x 768 x 3 pixels</p> <p><em>classes:</em></p> <ol> <li>water</li> <li>whitewater</li> <li>sediment</li> <li>other_bare_natural_terrain</li> <li>other_terrain</li> </ol> <p><em>File descriptions</em></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><em>References</em><br> *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>**Buscombe, D., Wernette, P., Fitzpatrick, S. <em>et al.</em> A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. <em>Sci Data</em> <strong>10</strong>, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2</p>
Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images
<p>Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</p> <p>These Residual-UNet models have been created using Segmentation Gym*</p> <p>Image size used by model: 1024 x 768 x 3 pixels</p> <p>classes:</p> <ol> <li>water</li> <li>other</li> </ol> <p><br> <strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><strong>References</strong><br> *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>
Dataset for "Segmentation of Lipid Droplets in Histological Images"
<p>Datasets for the publication "Segmentation of Lipid Droplets in Histological Images" (Medical Imaging with Deep Learning (MIDL 2023), short paper track. 2023. https://openreview.net/forum?id=nTnAm_El0RC)</p>
A Fundus Image Dataset for Domain Generalization in Joint Segmentation of Optic Disc and Optic Cup
<p>We provide a fundus image dataset for domain generalization, which includes 5 different medical centres.<br> This dataset is based on the REFUGE[1] dataset, Drishti-GS[2] dataset, ORIGA[3] dataset, and RIGA[4] dataset. We appreciate their efforts devoted by the authors of [1-4].</p> <table> <caption>Details of this dataset</caption> <tbody> <tr> <td>Domain</td> <td>Cases in Each Domain<br> (Training/Test)</td> </tr> <tr> <td>REFUGE</td> <td>320/80</td> </tr> <tr> <td>Drishti-GS</td> <td>50/51</td> </tr> <tr> <td>ORIGA</td> <td>500/150</td> </tr> <tr> <td>BinRushed (RIGA)</td> <td>156/39</td> </tr> <tr> <td>Magrabia (RIGA)</td> <td>76/19</td> </tr> </tbody> </table> <p>[1] Orlando J I, Fu H, Breda J B, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J]. Medical image analysis, 2020, 59: 101570.</p> <p>[2] Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-GS: Retinal image dataset for optic nerve head (onh) segmentation[C]//2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, 2014: 53-56.</p> <p>[3] Zhang Z, Yin F S, Liu J, et al. Origa-light: An online retinal fundus image database for glaucoma analysis and research[C]//2010 Annual international conference of the IEEE engineering in medicine and biology. IEEE, 2010: 3065-3068.</p> <p>[4] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. SPIE, 2018, 10579: 55-62.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code class="language-markdown">@article{chen2023treasure, title={Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation}, author={Chen, Ziyang and Pan, Yongsheng and Ye, Yiwen and Cui, Hengfei and Xia, Yong}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023}, year={2023} }</code></pre> <p> </p>
Supplementary Video 1: Training deep learning models for cell image segmentation with sparse annotations
<p>Supplementary Video 1: Training deep learning models for cell image segmentation with sparse annotations</p> <p><strong>Acknowledgements</strong></p> <p>I am grateful to Michalis Averof (IGFL, CNRS) in whose lab this work was initiated and carried out, and to Shuichi Onami (RIKEN, BDR) in whose lab part of this work was carried out.</p> <p>Applications used in this movie:</p> <p>StarDist: <a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a></p> <p>QuPath: <a href="https://qupath.github.io/">https://qupath.github.io/</a></p>
PROMISE12: Data from the MICCAI Grand Challenge: Prostate MR Image Segmentation 2012
<p>This repository contains all data associated with the 'Prostate MR Image Segmentation'-challenge 2012 on https://promise12.grand-challenge.org/. The goal of this challenge was to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate. </p> <p> </p>
On the risk of manual annotations in 3D confocal microscopy image segmentation
<p>This dataset contains different annotated masks of human induced pluripotent stem cell nuclei from the dataset published with https://doi.org/10.1038/s41586-022-05563-7 and DL models trained using these masks. Napari-GT and Slicer-GT were manually annotated using the Napari and 3D Slicer software considering only the DNA channel, while for bioGT the Lamin B1 channel was annotated using the seeded watershed algorithm to obtain a reproducible and biologically plausible nucleus annotation. This dataset is provided to reproduce the results in the manuscript "On the risk of manual annotations in 3D confocal microscopy image segmentation", more details can be found there.</p>
Real-time Neuron Segmentation for Voltage Imaging
Open the record for dataset details and reuse information.
Thoracoscopic Laser Speckle Contrast Imaging for Segment Resections
ClinicalTrials.gov study NCT05545085. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
Ultrahigh-resolution Optical Coherence Tomography Imaging of the Anterior Eye Segment Structures
ClinicalTrials.gov study NCT03461978. IPD Sharing: NO. Countries: 1. Publications: 3.
Optical Coherence Tomography Imaging of the Posterior Segment in High Myopia.
ClinicalTrials.gov study NCT00347451. IPD Sharing: Not stated. Countries: 1. Publications: 30.
The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging
ClinicalTrials.gov study NCT06864702. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
Anterior Segment and Corneal Parameters in Keratoconus and High Myopic Astigmatism Using Schiempflug Imaging
ClinicalTrials.gov study NCT06739018. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A Non-interventional Study to Assess the Influence of Automated Optical Coherence Tomography Image Enrichment With Segmentation Information on Disease Activity Assessment in Patients Treated With Lice
ClinicalTrials.gov study NCT04662944. IPD Sharing: NO. Countries: 5. Publications: 0.
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.