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111 results for “Cell Segmentation”
Fully annotated Human Breast carcinoma cells for 3D segmentation training
<p>Fully annotated dataset for training of 3D segmentation models. We provide the Raw image patches in the Raw directory and instance segmentation labels in the RealMask directory, the semantic segmentation masks are provided in the BinaryMask directory. Manually curated from originally published dataset over here: http://celltrackingchallenge.net/3d-datasets/ by team at Kapoorlabs.</p>
HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking
<p>Human fibrosarcoma HT1080WT (ATCC) cells at low cell densities embedded in 3D collagen type I matrices [1]. The time-lapse videos were recorded every 2 minutes for 16.7 hours and covered a field of view of 1002 pixels × 1004 pixels with a pixel size of 0.802 μm/pixel The videos were pre-processed to correct frame-to-frame drift artifacts, resulting in a final size of 983 pixels × 985 pixels pixels.</p> <p><em>Hasini Jayatilaka, Anjil Giri, Michelle Karl, Ivie Aifuwa, Nicholaus J Trenton, Jude M Phillip, Shyam Khatau, and Denis Wirtz. EB1 and cytoplasmic dynein mediate protrusion dynamics for efficient 3-dimensional cell migration. FASEB J., 32(3):1207–1221, 2018. ISSN 0892-6638. doi: 10.1096/fj.201700444RR.</em></p> <p>Further information about how to use this data is given in <a href="http://github.com/esgomezm/microscopy-dl-suite-tf">https://github.com/esgomezm/microscopy-dl-suite-tf</a></p> <p><strong>This dataset is provided together with the following preprint and if you use it, we would like to kindly ask you to cite it properly:</strong></p> <p><a href="https://arxiv.org/abs/2112.08817">Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia, *Search for temporal cell segmentation robustness in phase-contrast microscopy videos*, arXiv 2021 (arXiv:2112.08817)</a></p>
Cell-ACDC: segmentation, tracking, annotation and quantification of microscopy imaging data (dataset)
<p>This repository includes all the data generated or analysed during the preparation of Cell-ACDC publication, including test datasets for testing the software.</p> <p>Cell-ACDC is open-source software available on GitHub <a href="https://github.com/SchmollerLab/Cell_ACDC">here</a>.</p>
2D Cell Instance Segmentation Dataset of Master Thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs"
<p>This is the 2D cell instance segmentation dataset of master thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs". The data is originally from the BBBC038 dataset for the Kaggle 2018 Data Science Bowl. We keep the images with annotations and the final dataset comprises 670 images in the training set and 171 images in the test set. We also converted the dataset to MSCOCO format for convenience.</p>
3D Cell Instance Segmentation Dataset of Master Thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs"
<p>This is the 3D cell instance segmentation dataset of master thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs". The data is originally from the BBBC027 dataset comprising 30 sets of 3D image sets with high SNR level of quality. Because only the 4 to 100 slides of every 3D image set are annotated and the main difference between annotated and unannoated slides are the brightness, to avoiding misunderstanding by the algorithms, we removed the unannotated slides and separate every slide as individual images. We also converted the dataset to MSCOCO format for convenience.</p>
Supplemental Data for: Segmentation-free inference of cell types from in situ transcriptomics data
<p>Supplemental Data for: Segmentation-free inference of cell types from <em>in situ</em> transcriptomics data</p>
Text-fig. 2. Chara socotrensis. Branchlet segments with mucronate end cells. in Some Finds Of Charophytes From East-Africa (Zambia, Tanzania, Kenya And Somalia)
Text-fig. 2. Chara socotrensis. Branchlet segments with mucronate end cells.
Membrane staining and segmentation of a developing mouse embryo from 4 to 26 cells
<h1>Intent</h1> <p>The role of this dataset is to provide an example of segmentable and trackable data using signal from cell membrane. This dataset contains one file with the pre-processed imaged embryo (imaging.zip -> imaging.tif) and one file with the outcome of a segmentation using Cellpose (segmentation.zip -> segmentation.tif).</p> <blockquote> <p>This embryo corresponds to the embryo "C1" in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a></p> </blockquote> <h1>Animal</h1> <p>This set of data represents a mouse embryo developing from the 4-cell stage. The embryo at the last processed timepoint has 26 cells. The mother and the father were both mTmG animals (tdTomato anchored at the membrane of the cells).</p> <blockquote> <p>Details can be found in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a></p> </blockquote> <h1>Culture conditions</h1> <p>The embryo was imaged in an inverted SPIM from Luxendo (now Bruker), laying at the bottom of a PFE imaging dish. The embryo developed in a small pocket made by deforming the PFE with a glass tip. We used approx. 150μL of KSOM-AA to cutlure the embryos at 37ºC ± 0.2ºC in 5% CO2 and 5% O2. The medium was covered with approx. 100μL of mineral oil.</p> <blockquote> <p>Details can be found in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a></p> </blockquote> <h1>Imaging conditions</h1> <p>The tdTomato was excited with a 561nm laser using as little intensity as possible (0% in the settings + a small fraction leaking through the shutter). We used a 561 LP filter to acquire the signal. Imaging was set with a lateral resolution of 0.208μm and a axial resolution of 1μm. Because of technical issues with the stage of the microscope at the time, the resulting axial resolution was ultimately 1.338μm. In total, 181 slices were acquired per time point. Two consecutive timepoints start with 15 minutes of interval.</p> <p>Although this embryo was imaged for a longer period of time, this dataset shows only the first 117 timepoints.</p> <blockquote> <p>Details can be found in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a></p> </blockquote> <h1>Processing conditions</h1> <table> <tbody> <tr> <td><strong>Operation</strong></td> <td><strong>Lateral resolution</strong></td> <td><strong>Axial resolution</strong></td> <td><strong>File</strong></td> </tr> <tr> <td><strong>Imaging</strong></td> <td>0.208 μm</td> <td>1.338 μm</td> <td>-</td> </tr> <tr> <td><strong>Cropping<br></strong></td> <td>0.208 μm</td> <td>1.338 μm</td> <td>-</td> </tr> <tr> <td><strong>Lateral binning (average)</strong></td> <td>0.416 μm</td> <td>1.338 μm</td> <td>-</td> </tr> <tr> <td><strong>Isotropic rescaling</strong></td> <td>0.416 μm</td> <td>0.416 μm</td> <td>-</td> </tr> <tr> <td><strong>Lateral binning (average)</strong></td> <td>0.832 μm</td> <td>0.832 μm</td> <td>imaging.tif</td> </tr> <tr> <td><strong>Segmentation (cellpose)</strong></td> <td>0.832 μm</td> <td>0.832 μm</td> <td>segmentation.tif</td> </tr> </tbody> </table>
Annotated quantitative phase microscopy cell dataset of various adherent cell lines for segmentation purposes
<p>This dataset contains microscopic images of multiple cell lines captured by quantitative phase microscopy (QPI) without use of any fluorescent labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation. </p> <p>Our data consist of quantitative phase microscopy images:</p> <ul> <li>244 labelled images of PC-3 (7,907 cells), 205 labelled PNT1A (9,288 cells), 25 labelled images of G361, 25 labelled images of A2050 and 25 labelled images of HOB cells , in the paper designated as "<em>labelled"</em>, and</li> <li>1,819 unlabelled images with a mixture of 22Rv1, A2058, A2780, DU145, Fadu, G361, HOB and LNCaP used for pretraining, in the paper designated as "<em>unlabelled"</em>.</li> </ul> <ul> <li>See Vicar et al. XXXX 2021 DOI XXX (TBA after publishing)</li> <li>Code using this dataset is available at <a href="https://github.com/tomasvicar/Deep-QPI-Cell-Segmentation">github.com/tomasvicar/Deep-QPI-Cell-Segmentation</a></li> </ul> <p><strong>Materials and methods </strong></p> <p>A set of adherent cell lines of various origins, tumorigenic potential, and morphology were used in this paper (PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2058, A2780, Fadu, G361, HOB). PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2780, and G361 cell lines were cultured in RPMI-1640 medium, A2058, FaDu, and HOB cell lines were cultured in DMEM-F12 medium, all supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml), and with 10% fetal bovine serum (FBS). Prior to microscopy acquisition, the cells were maintained at 37 °C in a humidified (60%) incubator with 5% CO\textsubscript{2} (Sanyo, Japan). For acquisition purposes, the cells were cultivated in the Flow chamber µ-Slide I Luer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stage (Okolab, Ottaviano NA, Italy). For the acquisition of QPI, a coherence-controlled holographic microscope (Telight, Q-Phase) was used. Objective Nikon Plan 10×/0.3 was used for hologram acquisition with a CCD camera (XIMEA MR4021MC). Holographic data were numerically reconstructed with the Fourier transform method (described in Slaby, 2013 and phase unwrapping was used on the phase image. QPI datasets used in this paper were acquired during various experimental setups and treatments. In most cases, experiments were conducted with the time-lapse acquisition. The final dataset contains images acquired at least three hours apart.</p> <p><strong>Folder structure and file and filename description</strong></p> <p><strong>labelled </strong><em>(labelled)</em>: cells with segmentation labels, e.g.<br> 00001_PC3_img.tif - 32bit tiff image (in pg/um2 values)<br> 00001_PC3_mask.png - 8bit image with mask with unique grayscale value corresponding to single cell in FOV.</p> <p><strong>unlabelled </strong><em>(unlabelled)</em>: 11 varying cell lines, total 1819 FOVs, 32bit tiff image (in pg/um2 values)</p> <p> </p>
Segmenting cells in a spheroid in 3D using 2D StarDist within TrackMate
<p>3D image of cells in a spheroid, imaged on a confocal microscope, used in a tutorial to demonstrate how to hack TrackMate to segment cells in 3D using the 2D segmentation algorithms it ships.</p> <p>Image by Guillaume Jacquemet.</p> <p>For more details see https://imagej.net/plugins/trackmate/trackmate-stardist#generation-of-3d-labels-by-tracking-2d-labels-using-trackmate</p> <p> </p>
Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><strong>PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm </strong></p> <p><strong>All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions.</strong></p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p> </p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p> <p> </p> <p> </p>
Trained network for segmentation of yeast cell from brightfield images in microfluidic traps - DetecDiv (id02)
<pre>Trained network for segmentation of yeast cell from brightfield images in microfluidic traps. Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5553771">https://doi.org/10.5281/zenodo.5553771</a></pre> <p><strong>------------------------------------------</strong></p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Dataset for: Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks
<p>Datasets for the paper Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks, published in BMC Biology.</p>
Data sets for "Automated cell segmentation for reproducibility in bioimage analysis"
<p>This is the raw data sets used in "Automated cell segmentation for reproducibility in bioimage analysis", published in Synthetic Biology (Oxford Academic)</p>
Pretrained models and test examples of live cell anatomy segmentation
<p>Pretrained models of live cell anatomy segmentation and test examples of the segmentation of 15 structures</p>
Cell tracking data from: Automated timelapse data segmentation reveals in vivo cell state dynamics
<p>Embryonic development proceeds as a series of orderly cell state transitions built upon noisy molecular processes. Here, we defined gene expression and cell motion states using single cell RNA sequencing data and in vivo timelapse cell tracking data of the zebrafish tailbud. We performed a parallel identification of these states using dimensional reduction methods and a change point detection algorithm. Both types of cell states were quantitatively mapped onto embryos, and we utilized the cell motion states to study the dynamics of biological state transitions over time. The time average pattern of cell motion states is reproducible among embryos. However, individual embryos exhibit transient deviations from the time average forming left-right asymmetries in collective cell motion. Thus, the reproducible pattern of cell states and bilateral symmetry arises from temporal averaging. In addition, collective cell behavior can be a source of asymmetry rather than a buffer against noisy individual cell behavior.</p>
Structure preserving adversarial generation of labeled training samples for single cell segmentation
<p>Supplementary package (dataset, generated data and code) for the article: "<strong>Structure preserving adversarial generation of labeled training samples for single cell segmentation</strong>". (Cell Reports Methods, 2023 - under submission)</p> <p>Content of the package:</p> <ul> <li>datasets.zip: the images and annotations for the fallopian tube and salivary gland dataset.</li> <li>mask_quality_experiment.zip: the generated masks using SIMCEP and the generated microscopy images using pix2pix using the SIMCEP masks.</li> <li>pix2pix_models.zip: the models for fold 0 for both datasets. It can be used to synthesize new images.</li> <li>StyleGAN2-ada_models.zip: the models for synthesizing new masks.</li> <li>code.zip: a github repository (named .git) for executing the pipeline.</li> </ul>
KerNet, 3D fluorescence and segmentation datasets of keratin networks from MDCK, HaCaT, and RPE cells
Open the record for dataset details and reuse information.
Cell tracking data from: Automated timelapse data segmentation reveals in vivo cell state dynamics
Open the record for dataset details and reuse information.
3D Cryo Soft X-ray Transmission Microscopy data of Intact Thick Cells for Membrane Segmentation and Quantification
<p>The datasets used for evaluation of the proposed method in R. Cárdenes and C. Zhang et al. "3D Membrane Segmentation and Quantification of Intact Thick Cells using Cryo Soft X-ray Transmission Microscopy: A Pilot Study", PloS One, 2017. (DOI: 10.1371/journal.pone.0174324)</p>
ScienceDex guides
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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.