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2,474 results for “Segmentation”
Artefact segmentation in digital pathology whole-slide images
<p>Dataset with examples of Artefacts in Digital Pathology.</p> <p>The dataset contains 22 Whole-Slide Images, with H&E or IHC staining, showing various types and levels of defect to the slides. Annotations were made by a biomedical engineer based on examples given by an expert.</p> <p>The dataset is split in different folders:</p> <ul> <li>train <ul> <li>18 whole-slide images (extracted at 1.25x & 2.5x magnification)</li> <li>All from the same Block (colorectal cancer tissue)</li> <li>1/2 with H&E & 1/2 with anti-pan-cytokeratin IHC staining.</li> </ul> </li> <li>validation <ul> <li>3 whole-slide images (1.25x + 2.5x mag)</li> <li>2 from the same Block as the training set (1 IHC, 1 H&E)</li> <li>1 from another Block (IHC anti-pan-cytokerating, gastroesophageal junction lesion)</li> </ul> </li> <li>validation_tiles <ul> <li>patches of varying sizes taken from the 3 validation whole-slide images @1.25x magnification.</li> <li>7 patches from each slide.</li> </ul> </li> <li>test <ul> <li>1 whole-slide image (1.25x + 2.5x mag)</li> <li>From another block: IHC staining (anti-NR2F2), mouth cancer</li> </ul> </li> </ul> <p>For the train, validation and test whole-slide images, each slide has:<br> - The RGB images @1.25x & 2.5x mag<br> - The corresponding background/tissue masks<br> - The corresponding annotation masks containing examples of artefacts (note that a majority of artefacts are not annotated. In total, 918 artefacts are in the train set)</p> <p>For the validation tiles, the following table gives the "patch-level" supervision:</p> <p>tile# Artefact(s)<br> 00 None/Few<br> 01 Tear&Fold<br> 02 Ink<br> 03 None/Few<br> 04 None/Few<br> 05 Tear&Fold<br> 06 Tear&Fold + Blur<br> 07 Knife damage<br> 08 Knife damage<br> 09 Ink<br> 10 None/Few<br> 11 Tear&Fold<br> 12 Tear&Fold<br> 13 None/Few<br> 14 None/Few<br> 15 Knife damage<br> 16 Tear&Fold<br> 17 None/Few<br> 18 None/Few<br> 19 Blur<br> 20 Knife damage</p>
Dataset for Overscan Detection in Digitized Analog Films by Precise Sprocket Hole Segmentation
<p>This repo includes the self-generated dataset as well as the pre-trained models .</p> <p>ISVC 2020 - 15th International Symposium on Visual Computing</p> <p>Paper: Overscan Detection in Digitized Analog Filmsby Precise Sprocket Hole Segmentation</p> <p> </p> <p>Acknowledgement:</p> <p>Visual History of the Holocaust: Rethinking Curation in the Digital Age. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Grant Agreement 822670.</p> <p>https://www.vhh-project.eu</p> <p> </p>
2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.
<p>This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at <a href="https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1">https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1</a>. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at <a href="https://github.com/DiamondLightSource/placental-segmentation-2dunet">https://github.com/DiamondLightSource/placental-segmentation-2dunet</a> and is published at <a href="https://doi.org/10.5281/zenodo.4252562">https://doi.org/10.5281/zenodo.4252562</a> </p>
watercourse_100mseg: the Flemish watercourses represented by 100-meter line segments and corresponding downstream endpoints
<p>The data source <code>watercourse_100mseg</code> is derived from the raw data source '<a href="https://doi.org/10.5281/zenodo.4420904">watercourses</a>'. It represents all officially known watercourses of the Flemish Region as line segments of <strong>100 m</strong> (or < 100 m, for the most upstream segment of a watercourse). The data source can be used as a base layer of statistical <strong>population units</strong> (line segments) and corresponding anchor points, in the design of monitoring and research of watercourses.</p> <p>The data source is a GeoPackage with <strong>two spatial layers</strong>:</p> <ul> <li> <p><code>watercourse_100mseg_lines</code>: the line segments;</p> </li> <li> <p><code>watercourse_100mseg_points</code>: the corresponding downstream endpoints ('downstream' as defined in <code>watercourses</code>).</p> </li> </ul> <p>The coordinate reference system is 'Belge 72 / Belgian Lambert 72' (EPSG-code <a href="https://epsg.io/31370">31370</a>). Both layers have the same number of rows, and they share the same <strong>attributes</strong>:</p> <ul> <li><code>rank</code>: a unique, incremental number for each segment/endpoint. It just reflects the downstream-to-upstream order of segments within each original line.</li> <li><code>vhag_code</code>: the VHAG code from the raw <code>watercourses</code> data source. It distinguishes the different watercourses, so it is common to all segments/points that belong to the same watercourse.</li> </ul> <p>This version was derived from version '<code>watercourses_20200807</code>' (<a href="https://doi.org/10.5281/zenodo.4420905">Zenodo DOI</a>) as follows:</p> <ol> <li> <p>each line ('watercourse') of <code>watercourses</code> is split into segments of 100 m, where the remaining segment of < 100 m (per original line) is situated most upstream. For this step, the direction of the lines has been reverted (in <code>watercourses</code> the direction is from upstream to downstream). A unique rank number is assigned to each segment, as well as the VHAG code from the corresponding line in <code>watercourses</code>.</p> </li> <li> <p>the downstream endpoint of each segment is located, and assigned the same attributes (<code>rank</code> and <code>vhag_code</code>).</p> </li> </ol> <p>See R and GRASS code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/6b1d8f7/src/generate_watercourse_100mseg">'n2khab-preprocessing' at commit 6b1d8f7</a> for the creation from the <code>watercourses</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p>
Datasets and Models for Historical Newspaper Article Segmentation
<p>This record contains the datasets and models used and produced for the work reported in the paper "<em>Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers</em>" (<a href="https://infoscience.epfl.ch/record/282863?ln=en">link</a>).</p> <p>Please cite this paper if you are using the models/datasets or find it relevant to your research:</p> <pre><code>@article{barman_combining_2020, title = {{Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers}}, author = {Raphaël Barman and Maud Ehrmann and Simon Clematide and Sofia Ares Oliveira and Frédéric Kaplan}, journal= {Journal of Data Mining \& Digital Humanities}, volume= {HistoInformatics} DOI = {10.5281/zenodo.4065271}, year = {2021}, url = {https://jdmdh.episciences.org/7097}, }</code></pre> <p><br> <strong>Please note that this record contains data under different licenses.</strong><br> <br> <strong>1. DATA</strong></p> <ul> <li><strong>Annotations (json files)</strong>: JSON files contains image annotations, with one file per newspaper containing region annotations (label and coordinates) in VIA format. The following licenses apply: <ul> <li> luxwort.json: those annotations are under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">CC0 1.0 license</a>. Please refer to the right statement specified for each image in the file.</li> <li>GDL.json, IMP.json and JDG.json: those annotations are under a <a href="https://creativecommons.org/licenses/by-sa/4.0/legalcode">CC BY-SA 4.0 license</a>.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Image files: </strong>The archive images.zip contains the Swiss titles image files (GDL, IMP, JDG) used for the experiments described in the paper. Those images are under copyright (property of the journal <em>Le Temps </em>and of <em>ArcInfo</em>) and can be used <em>for academic research or educational purposes only</em>. Redistribution, publication or commercial use are not permitted. These terms of use are similar to the following right statement: <a href="http://rightsstatements.org/vocab/InC-EDU/1.0/">http://rightsstatements.org/vocab/InC-EDU/1.0/</a></li> </ul> <p> </p> <p><strong>2. MODELS</strong></p> <p>Some of the best models are released under a <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> license (they are also available as assets of the current Github <a href="https://github.com/dhlab-epfl/dhSegment-text/releases/tag/0.1">release</a>).</p> <ul> <li><strong>JDG_flair-FT</strong>: this model was trained on JDG using french Flair and FastText embeddings. It is able to predict the four classes presented in the paper (<code>Serial</code>, <code>Weather</code>, <code>Death notice</code> and <code>Stocks</code>).</li> <li><strong>Luxwort_obituary_flair-bpemb</strong>: this model was trained on Luxwort using multilingual Flair and Byte-pair embeddings. It is able to predict the <code>Death notice</code> class.</li> <li><strong>Luxwort_obituary_flair-FT_indomain</strong>: this model was trained on Luxwort using in-domain Flair and FastText embeddings (trained on Luxwort data). It is also able to predict the <code>Death notice</code> class.</li> </ul> <p>Those models can be used to predict probabilities on new images using the same code as in the original <a href="https://github.com/dhlab-epfl/dhSegment">dhSegment</a> repository. One needs to adjust three parameters to the <code>predict</code> function: 1) <code>embeddings_path</code> (the path to the embeddings list), 2) <code>embeddings_map_path</code>(the path to the compressed embedding map), and 3) <code>embeddings_dim</code> (the size of the embeddings).</p> <p>Please refer to the paper for further information or contact us.</p> <p> </p> <p><strong>3. CODE: </strong></p> <p><a href="https://github.com/dhlab-epfl/dhSegment-text">https://github.com/dhlab-epfl/dhSegment-text</a></p> <p><br> <strong>4. ACKNOWLEDGEMENTS</strong><br> We warmly thank the journal <a href="https://letemps.ch">Le Temps</a> (owner of <em>La Gazette de Lausanne</em> and the <em>Journal de Genève</em>) and the group <a href="https://www.arcinfo.ch/">ArcInfo</a> (owner of <em>L'Impartial</em>) for accepting to share the related datasets for academic purposes. We also thank the <a href="https://bnl.public.lu/fr.html">National Library of Luxembourg</a> for its support with all steps related to the <em>Luxemburger Wort</em> annotation release.<br> This work was realized in the context of the <a href="https://impresso-project.ch"><em>impresso</em> - Media Monitoring of the Past</a> project and supported by the Swiss National Science Foundation under grant CR- SII5_173719.<br> <br> <strong>5. CONTACT</strong><br> Maud Ehrmann (EPFL-DHLAB)<br> Simon Clematide (UZH)</p>
Cu dataset – A copper ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 121 pairs of correlated images. Each pair contains one image of a copper ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from a copper ore from Yauri Cusco (Peru) with a complex mineralogy, mainly composed of sulfides, oxides, silicates, and native copper. It was classified by size. The fraction +74-100 μm was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 121 fields were imaged on a reflected light microscope with a 20× (NA 0.40) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 1017×753 pixels with a resolution of 0.53 µm/pixel. As matter of fact, some images (the images No. 2, 3, 24, 25, 46, 47, 69, 91, and 113) have slightly smaller sizes because they were cropped during the registration procedure to correct co-localization errors of the order of a few pixels. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model (Chen et al., 2018) that reached mean values of 90.56% and 92.12% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5020566</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p>
Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis
<p>Underlying dataset and analysis tests of the results described in the article "Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis".</p>
Deep learning segmentation projects of FIB-SEM dataset of U2-OS cell
<p>This submission includes ground truth datasets that were used to segment the nuclear envelope (NE), mitochondria, endoplasmic reticulum (ER) and Golgi from a human bone osteosarcoma epithelial cell (U2-OS) imaged using focused-ion beam scanning electron microscopy (FIB-SEM).</p><p>The full FIB-SEM dataset is deposited to EMPIAR (<a href="https://www.ebi.ac.uk/empiar">https://www.ebi.ac.uk/empiar</a>, EMPIAR-11746). </p>
Example image for color based segmentation
<p>With the bright orange pumpkins on a flat background, it is a suitable example for color based segmentation.</p>
R Code and Re-analyzed Datasets for: Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses
<p>This submission includes all the scripts and data analyzed in the manuscript "Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses". This manuscript is a technical note on how genome formula data can be analyzed. There are no new experimental data in the manuscript, as published datasets are re-analyzed. Here we reproduce those datasets as formatted for our analysis, for the convenience of the reader. Please consult the README.txt file first.</p> <p>The corresponding paper was published in Viruses <em>16</em>(2): 270. (<a href="https://doi.org/10.3390/v16020270">https://doi.org/10.3390/v16020270</a>).</p> <p>This is the second version of the code, corresponding to the final version of the paper. The intial restricted version for review had a DOI 10.5281/zenodo.10355273.</p> <p> </p>
MRI Neonatal Lung Segmentation and 3D Morphologic Features
<p>We developed an ensemble of deep convolutional neural networks (2D-UNets) to perform automated neonatal lung segmentation from MRI sequences. A three-dimensional reconstruction is used to calculate MRI features for lung volume, shape, pixel intensity, and surface.</p> <p>In addition, ML Models for severity prediction of Bronchopulmonary Dysplasia (BPD) are implemented as an applied example of the use of MRI lung volumetric features for disease prognosis.</p> <p>This dataset comprises:</p> <ul> <li>Three pretrained 2D-UNet Models for Neonatal MRI Lung Segmentation.</li> <li>Resulting performances and features per MRI-sequence.</li> </ul> <p>See Publication:</p> <p>Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease (2023)</p> <p><a href="https://doi.org/10.1148/ryai.220239">https://doi.org/10.1148/ryai.220239</a></p>
Semantic segmentation model of construction waste landfill based on high-resolution satellite images
<p>CWLD_model project shows scripts and instructions on how to use this dataset (<a href="../records/10686118">https://zenodo.org/records/10686118</a>) to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
SEMFIRE forest dataset for semantic segmentation and data augmentation
<p><strong>SEMFIRE Datasets (Forest environment dataset)</strong></p> <p>These datasets are used for semantic segmentation and data augmentation and contain various forestry scenes. They were collected as part of the research work conducted by the Institute of Systems and Robotics, University of Coimbra <a href="https://isr.uc.pt/index.php/people?task=showprojects.show&idProject=203">team</a> within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. <a href="http://semfire.ingeniarius.pt/">CENTRO-01-0247-FEDER-032691</a>) research project coordinated by <a href="https://ingeniarius.pt/">Ingeniarius Ltd.</a></p> <p>The semantic segmentation algorithms attempt to identify various semantic classes (e.g. background, live flammable materials, trunks, canopies etc.) in the images of the datasets.</p> <p>The datasets include diverse image types, e.g. original camera images and their labeled images. In total the SEMFIRE datasets include about 1700 image pairs. Each dataset includes corresponding .bag files.</p> <p>To launch those .bag files on your ROS environment, use the instructions on the following Github <a href="https://github.com/Forestry-Robotics-UC/fruc_rosbags">repository</a></p> <p>Description of<strong> </strong>each <strong>dataset:</strong></p> <ol> <li><strong>2019_2020_quinta_do_bolao_coimbra:</strong> Robot moving on a path through a forest environment</li> <li><strong>2020_ctcv_parking_lot_coimbra:</strong> Robot moving in a circle in a parking lot for testings</li> <li><strong>2020_sete_fontes_forest: </strong>A set of forest images acquired by hand-held apparatus</li> </ol> <p>Each <strong>dataset</strong> consists of following <strong>directories:</strong></p> <ol> <li><strong>images directory: </strong>diverse image types, e.g. original camera images and their labeled images</li> <li><strong>rosbags directory: </strong>.bag files, which correspond to the image directory</li> </ol> <p>Each <strong>images directory </strong>consists of following <strong>directories:</strong></p> <ul> <li><strong>img:</strong> original camera images</li> <li><strong>lbl:</strong> single channel images (ground truth) with corresponding labels for each image in<strong> img</strong></li> <li><strong>lbl_colored: </strong>camera <strong> </strong>images in <strong>lbl</strong> colorized according to different semantic classes (for more details see the datasets descriptions)</li> <li><strong>lbl_overlaid: </strong>camera images in <strong>img </strong>overlaid with corresponding labels (colored)</li> </ul> <p>Each <strong>rosbags directory </strong>contains .bag files with the following <strong>topics:</strong></p> <ul> <li><strong>2019_2020_quinta_do_bolao_coimbra_rosbags: </strong> <ul> <li>/back_lslidar_packet</li> <li>/dalsa_camera_720p/compressed</li> <li>/flir_ax8/compressed</li> <li>/front_lslidar_packet</li> <li>/gps_fix</li> <li>/gps_time</li> <li>/gps_vel</li> <li>/imu/data</li> <li>/realsense/aligned_depth_to_color/image_raw</li> <li>/realsense/color/camera_info</li> <li>/realsense/color/image_raw/compressed</li> <li>/realsense/depth/camera_info</li> <li>/realsense/depth/image_rect_raw/compressed</li> <li>/realsense/extrinsics/depth_to_color</li> </ul> </li> <li><strong>2020_ctcv_parking_lot_coimbra_rosbags:</strong> <ul> <li>/dalsa_camera_720p/compressed</li> <li>/gps_fix</li> <li>/gps_ime</li> <li>/fused_point_cloud</li> <li>/imu/data</li> <li>/imu/mag</li> <li>/imu/rpy</li> </ul> </li> <li><strong>2020_sete_fontes_forest_rosbags: </strong> <ul> <li>/realsense/camera_info</li> <li>/realsense/depth_compressed/compressedDepth</li> <li>/realsense/nir/left/compressed</li> <li>/realsense/nir/right/compressed</li> <li>/realsense/rgb/compressed</li> </ul> </li> </ul> <p>All datasets include a detailed description as a text file. In addition, they include a rosbag_info.txt file with a description for each ROS inside the .bag files as well as a description for each ROS topic.</p> <p> </p> <p>The following table shows the statistical description of typical portuguese woodland configurations with structured plantations of <em>Pinus pinaster </em>(<em>Pp, </em>pine trees) and <em>Eucalyptus globulus </em>(<em>Eg, </em>eucalyptus).</p> <table> <tbody> <tr> <td> </td> <td><strong>"Low density" structured plantation</strong></td> <td><strong>"High density" structured plantation</strong></td> </tr> <tr> <td><strong>Tree density (assuming plantation in rows spaced 3m apart in all cases)</strong></td> <td> <p><em>Eg</em>: 900 trees/ha</p> <p><em>Pp</em>: 450 trees/ha</p> </td> <td> <p><em>Eg</em>: 1400 trees/ha</p> <p><em>Pp</em>: 1250 trees/ha</p> </td> </tr> <tr> <td> <p><strong>Average heights and corresponding ages of plantation trees</strong></p> </td> <td> <p><em>Eg</em>: 12m (6 years old)</p> <p><em>Pp</em>: 10m (15 years old)</p> </td> <td> <p><em>Eg</em>: 12m (6 years old)</p> <p><em>Pp</em>: 10m (15 years old)</p> </td> </tr> <tr> <td> <p><strong>Maximum heights and corresponding fully-matured ages of plantation trees</strong></p> </td> <td> <p><em>Eg</em>: 20m (11 years old)</p> <p><em>Pp</em>: 30m (40 years old)</p> </td> <td> <p><em>Eg</em>: 20m (11 years old)</p> <p><em>Pp</em>: 30m (40 years old)</p> </td> </tr> <tr> <td> <p><strong>Diameter at chest level (DCL – 1,3m) of plantation trees (average/maximum)</strong></p> </td> <td> <p><em>Eg</em>: 15cm/25cm</p> <p><em>Pp</em>: 20cm/50cm</p> </td> <td> <p><em>Eg</em>: 15cm/25cm</p> <p><em>Pp</em>: 20cm/50cm</p> </td> </tr> <tr> <td> <p><strong>Natural density of herbaceous plants</strong></p> </td> <td> <p>30% of woodland area</p> </td> <td> <p>30% of woodland area</p> </td> </tr> <tr> <td> <p><strong>Natural density of bush and shrubbery</strong></p> </td> <td> <p>30% of woodland area</p> </td> <td> <p>30% of woodland area</p> </td> </tr> <tr> <td> <p><strong>Natural density of arboreal plants (not part of plantation)</strong></p> </td> <td> <p>5% of woodland area</p> </td> <td> <p>5% of woodland area</p> </td> </tr> </tbody> </table> <ul> </ul>
Raw Metrics and Rankings for "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing"
<p>These datasets accompany the article published in <em>Remote Sensing </em>entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing".</p> <p>For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included: </p> <ul> <li><strong>Raw Metrics: </strong>the raw evaluations for each image returned by each of the 12 evaluation metrics. </li> <li><strong>Rankings:</strong> the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). </li> </ul> <p>The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.</p>
Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination
<p>Submissions for the Tractostorm 2 Project [1] from our collaborators (raters) are available for new analysis.<br> Contains regions of interest (ROIs) as well as resulting bundles. Segmentations were performed with MI-Brain [2] (<a href="https://github.com/imeka/mi-brain">MI-Brain</a>)</p> <p>Initial data is the same as in the initial <a href="https://zenodo.org/record/2547025#.YRV2S3VKiUk">Tractostorm Project</a> [3]<br> Contains the data as sent to collaborators and the written document containing the dissection protocol in detail.</p> <p>[1] Rheault, Francois, et al. "Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination." <em>Human Brain Mapping</em> (2022).<br> [2] Rheault, Francois, et al. "MI-Brain, a software to handle tractograms and perform interactive virtual dissection." <em>Proceedings of the ISMRM Diffusion study group workshop, Lisbon</em>. 2016.<br> [3] Rheault, Francois, et al. "Tractostorm: The what, why, and how of tractography dissection reproducibility." <em>Human brain mapping</em> 41.7 (2020): 1859-1874.</p> <p>Data Organization:<br> The 5 HCP subjects were duplicated 4 times each.<br> 193441 -> A111, B218, C317, D418<br> 219231 -> A127, B228, C320, D426<br> 286650 -> A136, B237, C338, D436<br> 486759 -> A149, B246, C344, D443<br> 615441 -> A156, B252, C359, D450<br> <br> Bundles can be segmented automatically using the <a href="https://github.com/scilus/scilpy">scilpy</a> toolbox.<br> scil_filter_tractogram.py ${INPUT} ${OUTPUT} ${OPTIONS}</p> <ul> <li>${INPUT} would be the whole brain tractogram of an HCP subject in data_to_segment.zip</li> <li>${OUTPUT} would be the bundle filename (preferably .trk format)</li> <li>${OPTIONS} would be the sequence of ROIs to apply, one for each bundle. <ul> <li><strong>CC</strong>: '--drawn_roi CENTRAL_CC.nii.gz any include --drawn_roi LOWER_AXIAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude --drawn_roi POST_C_R.nii.gz any exclude --drawn_roi PRE_C_R.nii.gz any exclude'</li> <li><strong>AF_L</strong>: '--drawn_roi CENTRAL_CS_L.nii.gz any include --drawn_roi MEDIAL_SAGITTAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any include --drawn_roi PRE_C_L.nii.gz any include --drawn_roi TEMPORAL_ENTRY.nii.gz any include --drawn_roi TEMPORAL_STEM.nii.gz any exclude'</li> <li><strong>PYT_L</strong>: '--drawn_roi IC_L.nii.gz any include --drawn_roi MO_L.nii.gz any include --drawn_roi MB_L.nii.gz any include --drawn_roi MO_L_NOT.nii.gz any exclude --drawn_roi MID_SAGITTAL_PLANE.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude'</li> </ul> </li> </ul>
Segmentation of membrane of mouse, sea urchin and human oocytes from transmitted light images
<p>This dataset has been presented in our paper "An interpretable and versatile machine learning approach for oocyte phenotyping", in bioRxiv.</p> <p>It contains images acquired in transmitted light with different settings of mouse and human oocytes and sea urchin eggs, with the corresponding ground-truth of the membrane segmentation. Mouse oocyte images were taken before and during oocyte maturation (meiosis I). Some human oocyte images were taken during oocyte maturation (meiosis I), and some are M-II oocytes just after fertilization. Sea urchin images contains both fertilized and unfertilized eggs.</p> <p> </p>
Earthquake Catalogues for DWARFS (Dense Westland Arrays Researching Fault Segmentation)
<p>This dataset contains earthquake hypocentral information catalogued as part of the DWARFS (Dense Westland Arrays Researching Fault Segmentation) broadband seismometer networks along New Zealand's Alpine Fault, between April 2019-April 2020.</p> <p>'Preferred Lat/Lon/Depth' refers to origin determined by method under 'Method'. HypoDD is the preferred method, but some origins could not be relocated and so we present the NonLinLoc derived origin instead. All magnitudes are Local magnitudes calculated using displacements on the vertical channel (MLv). All times are in UTC time. </p> <p>This dataset accompanies a publication recently submitted (July 2022) to the AGU journal 'Journal of Geophysical Research: Solid Earth' entitled 'Heterogeneity in microseismicity and stress near rupture-limiting section boundaries along the late interseismic Alpine Fault'. </p>
SeasoNet: A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery over Germany
<p>This dataset consists of 1,759,830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019. The patches were taken from 519,547 unique locations, covering the whole surface area of Germany, with each patch covering an area of 1.2km x 1.2km. The set is split into two overlapping grids, consisting of roughly 880,000 samples each, which are shifted by half the patch size in both dimensions. The images in each of the both grids themselves do not overlap.</p> <p><strong>Contents</strong></p> <p>Each sample includes:</p> <ul> <li>3 10m resolution bands (RGB), 120px x 120px</li> <li>1 10m resolution band (infrared), 120px x 120px</li> <li>6 20m resolution bands, 60px x 60px</li> <li>2 60m resolution bands, 20xp x 20px</li> <li>1 pixel-level label map</li> <li>2 binary masks for cloud and snow coverage</li> <li>2 binary masks for easy and medium segmentation difficulties, marks areas <300px and <100px respectively</li> <li>1 JSON-file containing additional meta-information</li> </ul> <p>The meta.csv contains the following information about each sample:</p> <ul> <li>Which season it belongs to</li> <li>Which of the two grids it belongs to</li> <li>Coordinates of the patch center</li> <li>Whether it was acquired from Sentinel-2 Satellite A or B</li> <li>Date and time of image acquisition</li> <li>Snow and cloud coverage percentages</li> <li>Image-level multi-class labels</li> <li>Three additional image-level urbanization labels, based on the center pixel (details below)</li> <li>The path to the sample</li> </ul> <p><strong>Classes</strong></p> <table> <thead> <tr> <th scope="col">ID</th> <th scope="col">Class</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Continuous urban fabric</td> </tr> <tr> <td>2</td> <td>Discontinuous urban fabric</td> </tr> <tr> <td>3</td> <td>Industrial or commercial units</td> </tr> <tr> <td>4</td> <td>Road and rail networks and associated land</td> </tr> <tr> <td>5</td> <td>Port areas</td> </tr> <tr> <td>6</td> <td>Airports</td> </tr> <tr> <td>7</td> <td>Mineral extraction sites</td> </tr> <tr> <td>8</td> <td>Dump sites</td> </tr> <tr> <td>9</td> <td>Construction sites</td> </tr> <tr> <td>10</td> <td>Green urban areas</td> </tr> <tr> <td>11</td> <td>Sport and leisure facilities</td> </tr> <tr> <td>12</td> <td>Non-irrigated arable land</td> </tr> <tr> <td>13</td> <td>Vineyards</td> </tr> <tr> <td>14</td> <td>Fruit trees and berry plantations</td> </tr> <tr> <td>15</td> <td>Pastures</td> </tr> <tr> <td>16</td> <td>Broad-leaved forest</td> </tr> <tr> <td>17</td> <td>Coniferous forest</td> </tr> <tr> <td>18</td> <td>Mixed forest</td> </tr> <tr> <td>19</td> <td>Natural grasslands</td> </tr> <tr> <td>20</td> <td>Moors and heathland</td> </tr> <tr> <td>21</td> <td>Transitional woodland/shrub</td> </tr> <tr> <td>22</td> <td>Beaches, dunes, sands</td> </tr> <tr> <td>23</td> <td>Bare rock</td> </tr> <tr> <td>24</td> <td>Sparsely vegetated areas</td> </tr> <tr> <td>25</td> <td>Inland marshes</td> </tr> <tr> <td>26</td> <td>Peat bogs</td> </tr> <tr> <td>27</td> <td>Salt marshes</td> </tr> <tr> <td>28</td> <td>Intertidal flats</td> </tr> <tr> <td>29</td> <td>Water courses</td> </tr> <tr> <td>30</td> <td>Water bodies</td> </tr> <tr> <td>31</td> <td>Coastal lagoons</td> </tr> <tr> <td>32</td> <td>Estuaries</td> </tr> <tr> <td>33</td> <td>Sea and ocean</td> </tr> </tbody> </table> <p><strong>Urbanization classes</strong></p> <ul> <li><strong>SLRAUM</strong> <ul> <li>0: None</li> <li>1: Ländlicher Raum (~ rural area)</li> <li>2: Städtischer Raum (~ urban area)</li> </ul> </li> <li><strong>RTYP3</strong> <ul> <li>0: None</li> <li>1: Ländliche Regionen (~ rural areas)</li> <li>2: Regionen mit Verstädterungsansätzen (~ urbanizing areas)</li> <li>3: Städtische Regionen (~ urban areas)</li> </ul> </li> <li><strong>KTYP4</strong> <ul> <li>0: None</li> <li>1: Dünn besiedelte ländliche Kreise</li> <li>2: Kreisfreie Großstädte</li> <li>3: Ländliche Kreise mit Verdichtungsansätzen</li> <li>4: Städtische Kreise</li> </ul> </li> </ul> <p>Further information on the urbanization classes can be found here:</p> <p><strong>SLRAUM</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html</a></p> <p><strong>RTYP3</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html</a></p> <p><strong>KTYP4</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html</a></p> <p><strong>License of landcover model</strong></p> <p>Bundesamt für Kartographie und Geodäsie</p> <p>dl-de/by-2-0 from <a href="https://www.govdata.de/dl-de/by-2-0">https://www.govdata.de/dl-de/by-2-0</a></p> <p>© GeoBasis-DE / <strong>BKG</strong> 2022</p> <p><strong>Source of landcover model</strong></p> <p><a href="https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/">https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/</a></p>
MELAN-A EXPRESSION RELATED TO APOPTOSIS OF MELANOCYTES IN SEGMENTAL AND NONSEGMENTAL VITILIGO
<p><strong>Abstract</strong></p> <p><strong>Background: </strong>Vitiligo is a progressive depigmentation of the skin with unclear etiology. Cell-mediated immunity has been suggested to play an important role in the pathogenesis of vitiligo’s progression. Melan-A has a high affinity for specific CD8+ T cells and is one of the critical markers for detecting damage to melanocytes. </p> <p><strong>Aim: </strong>Our study aims to demonstrate the differences of Melan-A expression associated with apoptosis of melanocytes in patients with segmental vitiligo (SV) and those with non-segmental vitiligo (NSV). </p> <p><strong>Methods:</strong> The subjects consisted of 64 patients diagnosed with vitiligo, of whom 33 were with NSV and 31 with SV. Skin biopsy and direct immunofluorescence were used to examine Melan-A, and the TUNEL method was performed to examine melanocyte apoptosis in both groups. Group comparisons were conducted using appropriate statistical methods. </p> <p><strong>Results: </strong>Melan-A expression was significantly higher in the NSV group than in the SV group, and there was a significant difference between two groups (p = 0.001). The median of melanocyte apoptosis in the NSV group was relatively higher than in the SV group, and a significant difference was found between the two groups (p = 0.001). The Spearman’s rank correlation test between Melan-A expression and melanocyte apoptosis in the NSV group was 0.767 (76.7%) and showed a significant relationship (p <0.05). The same test in the SV group was 0.583 (58.3%) and showed a significant relationship (p <0.05). In both groups, the higher the Melan-A expression, the higher the melanocyte apoptosis. </p> <p><strong>Conclusion:</strong> Melan-A expression and melanocyte apoptosis are correlated. The higher Melan-A expression and melanocyte apoptosis in NSV indicates more severe vitiligo disease compared to SV. </p>
StarDist Adipocyte Segmentation Training data, Training Notebook and Model
<p>Data from H&E human bone marrow whole slide scanner images used in the paper: "MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in clinical and experimental hematology" (https://doi.org/10.21203/rs.3.rs-1860140/v1)</p> <p> </p> <p>292 image patches</p> <p>Ground truth were manually annotated using QuPath and split into 263 images for training and 29 for validation.</p> <p>Training in StarDist was done on a Windows 10 PC with an RTX 2080 GPU. The requirements file for installing a Python 3.7 environment to run the attached notebooks is provided (<strong>stardist-val.txt</strong>).</p> <p>The StarDist model configuration can be found in the Jupyter Notebook :</p> <pre><code>Adipocyte Training.ipynb</code></pre> <p>Model validation and metrics can be performed by running the notebook after finishing the <strong>Adipocyte Training</strong> notebook.</p> <pre><code>Quality Control.ipynb</code></pre> <p> </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.