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Whole-cell segmentation of tissue images with human-level performance using large-scale data annotat...

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotat...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9010346

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

About this item

Full title

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Publisher

New York: Nature Publishing Group US

Journal title

Nature biotechnology, 2022-04, Vol.40 (4), p.555-565

Language

English

Formats

Publication information

Publisher

New York: Nature Publishing Group US

More information

Scope and Contents

Contents

A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previousl...

Alternative Titles

Full title

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9010346

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9010346

Other Identifiers

ISSN

1087-0156

E-ISSN

1546-1696

DOI

10.1038/s41587-021-01094-0

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