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Dense cellular segmentation for EM using 2D-3D neural network ensembles

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.01.05.895003Source: DataRank Database

Dense cellular segmentation for EM using 2D-3D neural network ensembles is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2020). On theSindex it has a DataRank of 0. It has been cited 5 times.

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Open Access5 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (1 grants)OA: greenTopics (22)IDs (PubMed, OpenAlex)SDGs (3)

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Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (7)

Zeyad A.S. Emam,Adam B. Anderson,Maria A. Aronova ORCID,Brian Storrie ORCID,Irina D. Pokrovskaya ORCID

Live enrichment

PubMed:19910364 OpenAlex:https://openalex.org/W2997487383

National Institutes of Health

Grant: 4R01HL119393-04

Structure/Function Determinants of Platelet Granule Secretion

greenLicense: public-domain

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2020/04/16/2020.01.05.895003.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2020.01.05.895003

repositoryhttps://doi.org/10.1101/2020.01.05.895003

https://doi.org/10.1038/s41598-021-81590-0

Fields of Study

Advanced Electron Microscopy Techniques and ApplicationsCell Image Analysis TechniquesElectron and X-Ray Spectroscopy Techniques02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

Keywords

SegmentationPattern recognition (psychology)Artificial neural networkImage segmentationScale-space segmentationConvolutional neural networkScienceQRArticleMachine LearningMicroscopy, ElectronImaging, Three-DimensionalImage Processing, Computer-AssistedMedicineNeural Networks, ComputerAlgorithms

Sustainable Development Goals

SDG 3: 3. Good healthSDG 4: 4. EducationSDG 7: 7. Clean energy