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ShareScore release 0.9.0
Dataset results
18 results for “Microscopy image segmentation”
Segmentations of 3D electron microscopy image volume from an albino mouse dorsal lateral geniculate nucleus
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Segmented high-resolution transmission electron microscopy images of nanoparticles
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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>
◂Fig. 10 Scanning electron microscopy images of Ramisyllis kingghidorahi n. sp., posterior-most regions and epithelium details. A–D Posterior ends. Arrow in C and D points to heavily ciliated anus. E– G Minute crests on the dorsal surface of midbody segments. Arrows point to crests laterally located on the dorsal surface. H Dorsal surface of posterior segments. I Clumps of cilia on dorsal surface of proventricular segments. Arrows pointing to pores in H. Scale bars: 100 µm A, B, I, 50 um C, G, 5 µm D, E,4 µm F, and 3 µm H in Ramisyllis kingghidorahi n. sp., a new branching annelid from Japan
◂Fig. 10 Scanning electron microscopy images of Ramisyllis kingghidorahi n. sp., posterior-most regions and epithelium details. A–D Posterior ends. Arrow in C and D points to heavily ciliated anus. E– G Minute crests on the dorsal surface of midbody segments. Arrows point to crests laterally located on the dorsal surface. H Dorsal surface of posterior segments. I Clumps of cilia on dorsal surface of proventricular segments. Arrows pointing to pores in H. Scale bars: 100 µm A, B, I, 50 um C, G, 5 µm D, E,4 µm F, and 3 µm H
◂Fig. 9 Scanning electron microscopy images of branches of Ramisyllis kingghidorahi n. sp. A–F Midbody branching regions with segments of different morphologies, as long as wide with long dorsal cirri in A–C, much longer with short dorsal cirri in D, E and F Details of cirri alternation in length. A, C, E–F In dorsal view; B and D in ventral view. Scale bars: 200 µm A, C, 100 µm B, F, 400 µm D, and 500 µm E in Ramisyllis kingghidorahi n. sp., a new branching annelid from Japan
◂Fig. 9 Scanning electron microscopy images of branches of Ramisyllis kingghidorahi n. sp. A–F Midbody branching regions with segments of different morphologies, as long as wide with long dorsal cirri in A–C, much longer with short dorsal cirri in D, E and F Details of cirri alternation in length. A, C, E–F In dorsal view; B and D in ventral view. Scale bars: 200 µm A, C, 100 µm B, F, 400 µm D, and 500 µm E
Flywing (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Flywing (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Flywing (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
On the risk of manual annotations in 3D confocal microscopy image segmentation
<p>This dataset contains different annotated masks of human induced pluripotent stem cell nuclei from the dataset published with https://doi.org/10.1038/s41586-022-05563-7 and DL models trained using these masks. Napari-GT and Slicer-GT were manually annotated using the Napari and 3D Slicer software considering only the DNA channel, while for bioGT the Lamin B1 channel was annotated using the seeded watershed algorithm to obtain a reproducible and biologically plausible nucleus annotation. This dataset is provided to reproduce the results in the manuscript "On the risk of manual annotations in 3D confocal microscopy image segmentation", more details can be found there.</p>
Images and Labels for Segmentation Studies in Microscopy
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Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images
<p>Data for the Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images paper</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.