Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  High-precision automated reconstruction of neurons with flood-filling networks

Januszewski, M., Kornfeld, J., Li, P. H., Pope, A., Blakely, T., Lindsey, L., et al. (2018). High-precision automated reconstruction of neurons with flood-filling networks. Nature Methods, 15(8), 605-610. doi:10.1038/s41592-018-0049-4.

Item is

Basisdaten

einblenden: ausblenden:
Genre: Zeitschriftenartikel

Externe Referenzen

einblenden:
ausblenden:
externe Referenz:
https://www.nature.com/articles/s41592-018-0049-4 (Verlagsversion)
Beschreibung:
-
OA-Status:

Urheber

einblenden:
ausblenden:
 Urheber:
Januszewski, Michal, Autor
Kornfeld, Jörgen1, Autor           
Li, Peter H., Autor
Pope, Art, Autor
Blakely, Tim, Autor
Lindsey, Larry, Autor
Maitin-Shepard, Jeremy, Autor
Tyka, Mike, Autor
Denk, Winfried1, Autor           
Jain, Viren, Autor
Affiliations:
1Department: Electrons-Photons-Neurons / Denk, MPI of Neurobiology, Max Planck Society, ou_1128546              

Inhalt

einblenden:
ausblenden:
Schlagwörter: VOLUME ELECTRON-MICROSCOPY; CONVOLUTIONAL NETWORKS; SEGMENTATION; SYNAPSES; RETINA; IMAGES; TISSUE; SCALEBiochemistry & Molecular Biology;
 Zusammenfassung: Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2018
 Publikationsstatus: Erschienen
 Seiten: 11
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: ISI: 000440334000021
DOI: 10.1038/s41592-018-0049-4
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Nature Methods
  Andere : Nature Methods
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: New York, NY : Nature Publishing Group
Seiten: - Band / Heft: 15 (8) Artikelnummer: - Start- / Endseite: 605 - 610 Identifikator: ISSN: 1548-7091
CoNE: https://pure.mpg.de/cone/journals/resource/111088195279556