Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  Crowd Sourcing Image Segmentation with iaSTAPLE

Schlesinger, D., Jug, F., Myers, G., Rother, C., & Kainmueller, D. (2017). Crowd Sourcing Image Segmentation with iaSTAPLE. In G. Egan (Ed.), 14th. IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI 2017; Proceedings (pp. 401-405). Piscataway, N.J.: IEEE.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0002-8BC0-8 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-8BC1-7
Genre: Konferenzbeitrag

Externe Referenzen

einblenden:
ausblenden:
externe Referenz:
https://publications.mpi-cbg.de/Schlesinger_2017_6913.pdf (beliebiger Volltext)
Beschreibung:
-
OA-Status:

Urheber

einblenden:
ausblenden:
 Urheber:
Schlesinger, Dmitrij, Autor
Jug, Florian1, Autor           
Myers, Gene1, Autor           
Rother, Carsten, Autor
Kainmueller, Dagmar1, Autor           
Egan, Garry, Herausgeber
Affiliations:
1Max Planck Institute for Molecular Cell Biology and Genetics, ou_2340692              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware STAPLE (iaSTAPLE) since our image segmentation model seamlessly integrates into the well-known and widely used STAPLE approach. In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99% of all cells when compared to expert segmentations. These results show that iaSTAPLE is a highly useful tool for crowd sourcing image segmentation. © 2017 IEEE.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2017-06-15
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: Anderer: cbg-6913
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: 14th. IEEE International Symposium on Biomedical Imaging, ISBI 2017
Veranstaltungsort: Melbourne
Start-/Enddatum: 2017-04-18 - 2017-04-21

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: 14th. IEEE International Symposium on Biomedical Imaging: From Nano to Macro ; ISBI 2017 ; Proceedings
Genre der Quelle: Konferenzband
 Urheber:
Egan, Garry, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Piscataway, N.J. : IEEE
Seiten: - Band / Heft: 14th. IEEE International Symposium on Biomedical Imaging: From Nano to Macro ; ISBI 2017 ; Proceedings Artikelnummer: - Start- / Endseite: 401 - 405 Identifikator: ISBN: 978-1-5090-1172-8