Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Dataset defining representative route network for GLOWOPT market segments

For calculating the GLOWOPT representative route network, a forecast model chain was used. The model was calibrated with 2019 flight movement data (unimpeded by COVID-19) and provided forecasted aircraft movements from the year 2019 (~2020) to 2050 in 5 years intervals. Two formats of datasets are generated with the results of the forecast model chain, a csv file format and 4-dimensional array supported with MATLAB (.mat). CSV Datasets For each forecasted year a csv file is generated with the information on the origin-destination (OD) airports IATA codes, region, latitude and longitude of OD pair, representative aircraft type along with the aircraft category , the average load factor and finally, the distance between the OD pair. The airports worldwide are sub-dived into nine regions namely Africa, Asia, Caribbean, Central America, Europe, Middle East, North America, Oceania and South America. There are total of seven datasets, one for each forecasted year i.e. for years 2019 (~2020), 2025, 2030, 2035, 2040, 2045 and 2050. Description of the data labels: Origin- Origin airport IATA code Origin_Region- Region of the Origin Airport Origin_Latitude- Latitude of the Origin Airport Origin_Longitude- Longitude of the Origin Airport Destination- Destination airport IATA code Destination_Region- Region of the Destination Airport Destination_Latitude- Latitude of the Destination Airport Destination_Longitude- Longitude of the Destination Airport AcType- Representative aircraft type Load_Factor- Average load factor per flight Yearly_Frequency- Total aircraft movements per annum RefACType- Aircraft Category based on number of seats (Category 6 represents aircraft with seats 252-301 and category 7 represents aircraft with seats greater than 302.) Distance- Great circle distance between Origin and Destination in Km.   MATLAB Datasets The dataset generated with MATLAB is a 4-dimensional array with the extension *.mat. The first dimension is the region of the origin airport and subsequently the second dimensions contains the region of the destination airport. The third and fourth dimension are the aircraft category based on seat numbers and the categorized great circle distances. The information received therein is a 1X1 cell with the IATA codes of the OD pairs, frequency and great circle distance in Km. The 4D array is categorised such that the user can select the route segment specific to a region or a combination of regions. The range categorisation in combination with an aircraft category additionally offers the user the possibility to select routes depending on their great circle distances. The ranges are categorised to represent very short range (0-2000 km), short range (2000-6000 km), medium range (6000-10000 km) and long range (10000 – 15000 km). Indexing based on the categorisation of the 4D array dataset - Refer to file 'Indexing_MAT_Dataset.PNG' For example: To derive the OD pairs and yearly frequency of aircraft movements for routes which originate from Europe and are destined to Asia, operated with category 6 aircraft type and are separated by distances between 10,000 to 15,000 km: In MATLAB (Indexing based on file 'Indexing_MAT_Dataset.PNG' ): Route_Network (5,2,1,4), Description on Index: 5 – Europe: Origin Region  2 – Asia: Destination Region 1– Category 6: Aircraft Type 4 – 10000-15000 km: Range

opencc-by-nc-sa-4.0Nov 2018View details →

PESO: Prostate Epithelium Segmentation on H&E-stained prostatectomy whole slide images

Large set of whole-slide-images (WSI) of prostatectomy specimens with various grades of prostate cancer (PCa). More information can be found in the corresponding paper: <a href="https://doi.org/10.1038/s41598-018-37257-4">https://doi.org/10.1038/s41598-018-37257-4</a> The WSIs in this dataset can be viewed using the open-source software <a href="https://github.com/computationalpathologygroup/ASAP">ASAP</a> or <a href="https://openslide.org/">Open Slide</a>. Due to the large size of the complete dataset, the data has been split up in to multiple archives. The data from the training set: <ul> <li>peso_training_masks.zip: Training masks (N=62) that have been used to train the main network of our paper. These masks are generated by a trained U-Net on the corresponding IHC slides.</li> <li>peso_training_masks_corrected.zip: A subset of the color deconvolution masks (N=25) on which manual annotations have been made. Within these regions, stain and other artifacts have been removed.</li> <li>peso_training_colordeconvolution.zip: Mask files (N=62) containing the P63&CK8/18 channel of the color deconvolution operation. These masks mark all regions that are stained by either P63 or CK8/18 in the IHC version of the slides.</li> <li>peso_training_wsi_{1-6}.zip: Zip files containing the whole slide images of the training set (N=62). Each archive contains 10 slides, excluding the last which contains 12. These images are exported at a pixel resolution of 0.48mu/pixels. </li> </ul> The data from the test set: <ul> <li>peso_testset_regions.zip: Collection of annotation XML files with outlines of the test regions. These can be used to view the test regions in more detail using ASAP.</li> <li>peso_testset_png.zip: Export of the test set regions in PNG format (2500x2500 pixels per region).</li> <li>peso_testset_png_padded.zip: Export of the test regions in PNG format padded with a 500 pixel wide border (3500x3500 pixels per region). Useful for segmenting pixels at the border of the regions.</li> <li>peso_testset_mapping.csv: A csv file mapping files from the test set (numbered 1-160) to regions in the xml files. The csv file also contains the label (benign or cancer) for each region.</li> <li>peso_testset_groundtruth_masks.zip: The ground truth (pixel) masks (N=40) of all regions in the test set. For each pixel in the test set regions, these masks contain the ground truth: 0 for unlabelled, 1 for background and 2 for epithelial tissue.</li> <li>peso_testset_wsi_{1-4}.zip: Zip files containing the whole slide images of the test set (N=40). Each archive contains 10 slides of the test set. These images are exported at a pixel resolution of 0.48mu/pixels. </li> </ul> This study was financed by a grant from the Dutch Cancer Society (KWF), grant number KUN 2015-7970. If you make use of this dataset please cite both the dataset itself and the corresponding paper: <a href="https://doi.org/10.1038/s41598-018-37257-4">https://doi.org/10.1038/s41598-018-37257-4</a> Update July 2021: We have added the ground truth masks for the test set.

opencc-by-4.0Aug 2021View details →

Data Set for 'Self-Supervised Machine Learning for Live Cell Imagery Segmentation'

Self-supervised machine learning code and data for segmenting live cell imagery (Matlab) Running the Code SSL_Demo_2.m : main program for self-supervised machine learning segmentation SSL_Declumping_2.m : main program for declumping application (applied to output of SSL_Demo_2.m) This Matlab code is designed to be used with time-resolved live cell microscopy images (tiffs) for the automated segmentation of cells from background. It is recommended you first run this code with its accompanying demo data (included in this package), keeping the current directory structure. Simply open SSL_Demo_2.m or SSL_Declumping_2.m in Matlab and hit Run. Code Methodology The principle of self-supervised machine learning is that you simply load your images and Run - no parameter tuning needed, no training imagery required. Run from start to finish, the SSL_Demo_2.m code uses consecutive pairs of images to generate training data of 'cells' and 'background' via dynamic feature vectors based on optical flow (unsupervised). These self-labeled pixels are then used to generate static feature vectors (entropy, gradient), which in turn are used to train a classifier model. The training data is updated every image in order to automatically adapt to temporal changes in cell morphologies or background illumination. The code was tested for high fidelity segmentation using five different modes of light microscopy: transmitted light, DIC, phase contrast, fluorescence and interference reflection microscopy. Six different cell lines were imaged to cover a range of morphologies and phenotypic dynamics using three cameras of differing resolutions. The associated manuscript for this work can be found here (although the latest version is under peer review as of this writing):  <a href="https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1">https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1</a> This code was tested on Matlab v2020a and v2021a using commercially available laptop computers running the Windows 10 operating system.

opencc-by-4.0Jul 2021View details →

HTRCatalogs: Dataset for historical catalogs HTR and Segmentation

This release contains 465 xml files, and their corresponding images from a large corpus of 19th, 20th and 21th exhibition catalogs, manuscripts'fair catalogs and directories. The new catalogs added here were created using the HTR and segmentation models accessible in the repository. It includes a csv file describing the xml files and various tools to create a training dataset: differents bash scripts, a python programm to divide the xml files into testing, training and evaluation dataset and several fixed tests. A xsl transformation sheet is also accessible to delete the Entry and EntryEnd zones from the xml files in order to have a SegmOnto-like dataset. The xml files has been corrected since the 4.0 release thanks to the addition of a github action (SegmOntoKraken).

opencc-by-4.0Sep 2021View details →

Historical City Maps Semantic Segmentation Dataset

This dataset includes a total of 635 annotated image patches from historical city maps. It is designed for the semantic segmentation of the maps into 5 semantic classes (building blocks, non-built, water, road network, background frame). 330 patches are taken from maps of the city of Paris, while the 305 others are taken from a balanced corpus of city maps from 90 countries all around the world. Please read the detailed informations about data collection methodology, associated metadata and annotation ontology in README.md hereunder :

Segmentation Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon

The zip file here contains 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. A total of 1,054 unique images were labeled. 946 images were annotated by a single labeler. 95 images were annotated by two labelers. 11 images were annotated by three labelers. 2 images were annotated by five labelers. All authors contributed to labeling, and all labeling was done with an open-source labeling tool (Buscombe et al., 2022). All pixels in each image are labeled with one of four classes: 0 (water), 1 (bare sand), 2 (vegetation - both sparse and dense), 4 (the built environment - buildings, roads, parking lots, boats, etc.) The csv file provided here is a list of each image file name (which includes the anonymized labeler ID), the name of the image without the labeler ID, the name of the corresponding NOAA jpg, the NOAA flight name, the storm name, the latitude and longitude of the image, and a column stating if the image has been labeled multiple times.  Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). The images included in this data release correspond to original NOAA images that have been resized and then split into quadrants (using ImageMagick). The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right. The resize command used was: #to resize and then quarter #Dir structure is: # --Desktop #    |- originals #    |- resized #    |- quarters `cd originals` `mogrify -resize 2000x2000 -path ../resized *.jpg` #then quarter them `cd ..` `cd resized` `mogrify -crop 2x2@ +repage -path ../quarters *.jpg` For full size images, please download the jpegs directly from NOAA.

Morphotectonic indices and earthquake dataset of Batui Thrust Segment, Banggai, Indonesia

To measure the tectonic activity of a region, morphotectonic, a quantitave measurement of landscapes are carried out. Combination between geological, geomorphology, and earthquake data approach are powerful source to determine process that make up mountainous landscape. The combined data are applied to Pagimana and adjacent area, Banggai, Indonesia by dividing into eight watersheds and aim the Batui Thurst as main objective. The data comprised of 8.5m vertical resolution Digital Elevation Model (DEM) and earthquakes catalog were downloaded from the open-source database and extracted using composited GIS software. We use seven morphometry indices consist of Mountain Front Sinousity (Smf), Drainage Basin: Asymmetry Factor (AF) and Transverse Topographic Symmetry (T),  Hypsometric Integral (HI), Channel Sinuosity (S), Ratio of Valley Floor Width to Valley Height (Vf), Stream Length-Gradient Index (SL), and Basin Elongation Ratio (Re) and embed them on eight watersheds which taken by remote sensing. Weighing indices and watersheds matrix also calculated to define correlation between each watershed on each index. Fieldwork also carried out to ensure the presence of Batui Thrust and other related structural geology which have been developed on the Pagimana. This dataset is potentially can be used for geologist, geophysicist and geomorpher to get new insight on the forming of East Arm of Sulawesi landscape and the Banggai-Sula Microcontinent development. Beside, the urban planner and risk assessor can be easily analyze the dataset to avoid and minimize effect of the potential earthquake hazard in the future.

Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts. These Residual-UNet model data are based on 1-band NDWI images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571 Classes: {0=water, 1=whitewater, 2=sediment, 3=other} File descriptions For each model, there are 5 files with the same root name: 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. 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. 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 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` 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` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *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 ** 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

Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.

These Residual-UNet model data are based on 5-band RGB+NIR+SWIR (red, green, blue, near-infrared, and short-wave infrared) images of coasts and associated labels.   Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7344571">https://doi.org/10.5281/zenodo.7344571 </a> Classes: {0=water, 1=whitewater, 2=sediment, 3=other} File descriptions For each model, there are 5 files with the same root name: 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. 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. 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 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` 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` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *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> ** 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. <a href="https://doi.org/10.5281/zenodo.7344571">https://doi.org/10.5281/zenodo.7344571</a>

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)

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) Description 579 images and 579 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 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**. 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 4 classes. The label images are a subset of the following data release**** <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a> Imagery comes from the following 10 sand beach sites: <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> 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, NIR, and SWIR bands only File descriptions <ol> <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>nir.zip, a zipped folder containing the corresponding near-infrared (NIR) imagery</li> <li>swir.zip, a zipped folder containing the corresponding shortwave-infrared (SWIR) imagery</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_nir.zip, NIR images resized to 512x512x3 pixels</li> <li>resized_swir.zip, SWIR images resized to 512x512x3 pixels</li> <li>resized_labels.zip, label images resized to 512x512 pixels</li> </ol> References *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> **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> ***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 **** 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>

Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other)

Description 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 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**. 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. Imagery comes from the following 10 sand beach sites: <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> 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 File descriptions <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> References *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> **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> ***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

opencc-by-4.0Jul 2022View details →

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.

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.   These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels.   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> Classes: {0=water, 1=whitewater, 2=sediment, 3=other} File descriptions For each model, there are 5 files with the same root name: 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. 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. 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 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` 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` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *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> ** 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>

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.

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. These Residual-UNet model data are based on 1-band MNDWI images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571 Classes: {0=water, 1=whitewater, 2=sediment, 3=other} File descriptions For each model, there are 5 files with the same root name: 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. 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. 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 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` 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` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *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 ** 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

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) Description 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 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**. Two data sources have been combined Dataset 1 <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> Dataset 2 <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> File descriptions <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> References *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. **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 ***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 ****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 *****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/ ******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.

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

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 each simulated scanning sequence, there are 500 brain volumes. 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> 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. 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. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

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

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 each simulated scanning sequence, there are 500 brain volumes. 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> 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. 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. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

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

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 each simulated scanning sequence, there are 500 brain volumes. 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> 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. 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. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

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

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 each simulated scanning sequence, there are 500 brain volumes. 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> 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. 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. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

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

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 each simulated scanning sequence, there are 500 brain volumes. 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> 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. 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. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

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

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 each simulated scanning sequence, there are 500 brain volumes. 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> 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. 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. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>