Crowdsourcing the creation of image segmentation algorithms for connectomics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F15%3APU116097" target="_blank" >RIV/00216305:26220/15:PU116097 - isvavai.cz</a>

Výsledek na webu

<a href="http://journal.frontiersin.org/Journal/10.3389/fnana.2015.00142/full" target="_blank" >http://journal.frontiersin.org/Journal/10.3389/fnana.2015.00142/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fnana.2015.00142" target="_blank" >10.3389/fnana.2015.00142</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Crowdsourcing the creation of image segmentation algorithms for connectomics

Popis výsledku v původním jazyce

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 EM 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.

Název v anglickém jazyce

Crowdsourcing the creation of image segmentation algorithms for connectomics

Popis výsledku anglicky

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 EM 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LO1401" target="_blank" >LO1401: Interdisciplinární výzkum bezdrátových technologií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frontiers in Neuroanatomy

ISSN

1662-5129

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

142

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

000365846500001

EID výsledku v databázi Scopus

2-s2.0-84948763339