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Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other)
<p><strong>Description</strong></p> <p>1018 images and 1018 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other</p> <p>These images and labels have been made using the Doodleverse software package, Doodler*. 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>Some (473) 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 4 classes.</p> <p>Imagery comes from the following 10 sand beach sites:</p> <ol> <li>Duck, NC, Hatteras NC, USA</li> <li>Santa Cruz CA, USA</li> <li>Galveston TX, USA</li> <li>Truc Vert,France</li> <li>Sunset State Beach CA, USA</li> <li>Torrey Pines CA, USA</li> <li>Narrabeen, NSW, Australia</li> <li>Elwha WA, USA</li> <li>Ventura region, CA, USA</li> <li>Klamath region, CA USA</li> </ol> <p>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, and Blue bands only</p> <p><strong>File descriptions</strong></p> <ol> <li>classes.txt, a file containing the class names</li> <li>images.zip, a zipped folder containing the 3-band images of varying sizes and extents</li> <li>labels.zip, a zipped folder containing the 1-band label images</li> <li>overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other)</li> <li>resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li>resized_labels.zip, label images resized to 512x512 pixels</li> </ol> <p><strong>References</strong></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.e2021EA002085<a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>. See <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler.</a></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>***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, <a href="https://doi.org/10.5066/P91NP87I">https://doi.org/10.5066/P91NP87I</a>. See <a href="https://coasttrain.github.io/CoastTrain/">https://coasttrain.github.io/CoastTrain/ </a>for more information</p> <p> </p>
Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.</strong></em></p> <p> </p> <p>These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels.</p> <p> </p> <p>Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a></p> <p>Classes: {0=water, 1=whitewater, 2=sediment, 3=other}</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, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a></p> <p> </p>
Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band MNDWI images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band MNDWI images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on 1-band MNDWI images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571</p> <p>Classes: {0=water, 1=whitewater, 2=sediment, 3=other}</p> <p>File descriptions</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><br> References</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>** 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> </p>
Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)
<p><em><strong>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)</strong></em></p> <p>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)</p> <p><strong>Description</strong></p> <p>4088 images and 4088 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB 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 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> <ul> <li>1018 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7335647</li> <li>Labels have been reclassified from 4 classes to 2 classes.</li> <li>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.</li> <li>These images and labels have been made using the Doodleverse software package, Doodler*.</li> </ul> <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> overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, bllue=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> </ul> <p><strong>References</strong></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, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647</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>
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 2 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 second part of 14 parts of the full dataset (2/14). 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) = 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>
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 3 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 third part of 14 parts of the full dataset (3/14). 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) = 20ms. 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>
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 12 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 twelfth part of 14 parts of the full dataset (12/14). 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) = 30ms. 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>
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 5 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 fifth part of 14 parts of the full dataset (5/14). 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) = 30ms. 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>
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 4 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 fourth part of 14 parts of the full dataset (4/14). 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) = 25ms. 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 of 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>
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 11 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 eleventh part of 14 parts of the full dataset (11/14). 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) = 25ms. 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>
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 10 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 tenth part of 14 parts of the full dataset (10/14). 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) = 20ms. 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>
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 14 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 fourteenth and final part of 14 parts of the full dataset (14/14). 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) = 40ms. 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>
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 6 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 sixth part of 14 parts of the full dataset (6/14). 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) = 35ms. 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>
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 8 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 eighth part of 14 parts of the full dataset (8/14). 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) = 10ms. 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>
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 13 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 thirteenth part of 14 parts of the full dataset (13/14). 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) = 35ms. 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>
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 7 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 seventh part of 14 parts of the full dataset (7/14). 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) = 40ms. 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>
Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) 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 3-band (RGB) images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on RGB (red, green, and blue) 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.7384242">https://doi.org/10.5281/zenodo.7384242</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, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384242">https://doi.org/10.5281/zenodo.7384242</a></p>
Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI 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 1-band NDWI images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on NDWI 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 Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band MNDWI 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 1-band MNDWI images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on MNDWI 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 FloodNet/10-class segmentation of RGB 768x512 UAV images
<p><em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for FloodNet/10-class segmentation of RGB 768x512 UAV images</strong></em></p> <p>These Residual-UNet model data are based on [FloodNet](https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021) images and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021</p> <p>Image size used by model: 768 x 512 x 3 pixels</p> <p><em>classes:</em><br> 1. Background<br> 2. Building-flooded<br> 3. Building-non-flooded<br> 4. Road-flooded<br> 5. Road-non-flooded<br> 6. Water<br> 7. Tree<br> 8. Vehicle<br> 9. Pool<br> 10. Grass</p> <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>images.zip and labels.zip contain the images and labels, respectively, used to train the model</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>** Rahnemoonfar, M., Chowdhury, T., Sarkar, A., Varshney, D., Yari, M. and Murphy, R.R., 2021. Floodnet: A high resolution aerial imagery dataset for post flood scene understanding. IEEE Access, 9, pp.89644-89654.</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.