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Dataset results
5 results for “3D instance segmentation”
3D nuclei instance segmentation dataset of fluorescence microscopy volumes of C. elegans
<p>The dataset consists of 28 confocal microscopy volumes of C. elegans worms at the L1 stage and corresponding stacks of densely annotated nuclei instance segmentation masks.</p> <p>* 28 raw images and corresponding masks of average dimension (xyz) 1050 x 140 x 140<br> * Pixelsize (xyz): 0.116 x 0.116 x 0.122μm<br> * Microscope: Leica confocal microscopy, 63x oil objective</p> <p><br> The original raw data and preliminary annotations were part of the following publication (please cite if you use the dataset):<br> <br> <em>Long, F., Peng, H., Liu, X., Kim, S. K., & Myers, E. (2009). A 3D digital atlas of C. elegans and its application to single-cell analyses. Nature methods, 6(9), 667-672.</em></p> <p>The nuclei annotation masks were further manually curated by Dagmar Kainmueller (MDC Berlin) for the following publication:</p> <p><em>Hirsch, P., & Kainmueller, D. (2020). An auxiliary task for learning nuclei segmentation in 3d microscopy images. In Medical Imaging with Deep Learning (pp. 304-321). PMLR.</em></p> <p>We provide the dataset already structured into the train/validation/test split as used by the above as well as the following publications: </p> <p><em>Weigert, M., Schmidt, U., Haase, R., Sugawara, K., & Myers, G. (2020). Star-convex polyhedra for 3d object detection and segmentation in microscopy. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 3666-3673).</em><br> </p> <p> </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>
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>
Self-Supervised Learning Cell Image Dataset of Master Thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs"
<p>This is the self-supervised learning cell image dataset of master thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs". We gather images from datasets such as the LIVECell dataset, the EVICAN dataset, as well as datasets available on Image Data Resource (https://idr.openmicroscopy.org/) and the Broad Bioimage Benchmark Collection (https://bbbc.broadinstitute.org/). Only images with sizes larger than 512x512 are collected. For datasets containing more than 1000 images, we randomly select 1000 images. Otherwise, we retain all images in the dataset. </p> <p> </p> <p>After download, please put all compressed folders of subdatasets in the "image" folder under the root directory.</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.