liu.seSök publikationer i DiVA
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Crowdsourcing the creation of image segmentation algorithms for connectomics
Institute Jean Pierre Bourgin, France.
Howard Hughes Medical Institute, VA USA.
Harvard University, MA 02138 USA.
Scuola University of Profess Svizzera Italiana, Switzerland.
Visa övriga samt affilieringar
2015 (Engelska)Ingår i: Frontiers in Neuroanatomy, ISSN 1662-5129, E-ISSN 1662-5129, Vol. 9, nr 142Artikel i tidskrift (Refereegranskat) Published
Resurstyp
Text
Abstract [en]

To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of FM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.

Ort, förlag, år, upplaga, sidor
Frontiers Media S.A., 2015. Vol. 9, nr 142
Nyckelord [en]
connectomics; electron microscopy; image segmentation; machine learning; reconstruction
Nationell ämneskategori
Medicinteknik
Identifikatorer
URN: urn:nbn:se:liu:diva-123808DOI: 10.3389/fnana.2015.00142ISI: 000365846500001PubMedID: 26594156Scopus ID: 2-s2.0-84948763339OAI: oai:DiVA.org:liu-123808DiVA, id: diva2:892912
Anmärkning

Funding Agencies|NIH [1R01NS075314-01]; ARO [W911NF-12-1-0594]; DARPA [HR0011-14-2-0004]; Human Frontier Science Program; Mathers Foundation; Gatsby Charitable Foundation; Howard Hughes Medical Institute; [CZ.1.07/2.3.00/20.0094]

Tillgänglig från: 2016-01-11 Skapad: 2016-01-11 Senast uppdaterad: 2017-11-30Bibliografiskt granskad

Open Access i DiVA

fulltext(3602 kB)417 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 3602 kBChecksumma SHA-512
a4c00044dfce682545e2b1a93bafbb959cab72d11367b3bec1fcce5875010c11f013374f232d5612984c215d1196bc35b8d28086e90d802aaa665d113036559f
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltextPubMedScopus

Personposter BETA

Pham, Tuan

Sök vidare i DiVA

Av författaren/redaktören
Pham, Tuan
Av organisationen
Institutionen för medicinsk teknikTekniska fakulteten
I samma tidskrift
Frontiers in Neuroanatomy
Medicinteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 417 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

doi
pubmed
urn-nbn

Altmetricpoäng

doi
pubmed
urn-nbn
Totalt: 462 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf