Detail publikačního výsledku

Crowdsourcing the creation of image segmentation algorithms for connectomics

ARGANDA-CARRERAS, I.; TURAGA, S. C.; BERGER, D. R.; CIRESAN, D.; GIUSTI, A.; GAMBARDELLA, L. M.; SCHMIDHUBER, J.; LAPTEV, D.; DWIVEDI, S.; BUHMANN, J. M.; LIU, T.; SEYEDHOSSEINI, M.; TASDIZEN, T.; KAMENTSKY, L.; BURGET, R.; UHER, V.; TAN, X.; SUN, C.; PHAM, T.; BAS, E.; UZUNBAS, M. G.; CARDONA, A.; SCHINDELIN, J.; SEUNG H. S.

Original Title

Crowdsourcing the creation of image segmentation algorithms for connectomics

English Title

Crowdsourcing the creation of image segmentation algorithms for connectomics

Type

WoS Article

Original Abstract

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.

English abstract

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.

Keywords

connectomics; electron microscopy; image segmentation; machine learning; reconstruction

Key words in English

connectomics; electron microscopy; image segmentation; machine learning; reconstruction

Authors

ARGANDA-CARRERAS, I.; TURAGA, S. C.; BERGER, D. R.; CIRESAN, D.; GIUSTI, A.; GAMBARDELLA, L. M.; SCHMIDHUBER, J.; LAPTEV, D.; DWIVEDI, S.; BUHMANN, J. M.; LIU, T.; SEYEDHOSSEINI, M.; TASDIZEN, T.; KAMENTSKY, L.; BURGET, R.; UHER, V.; TAN, X.; SUN, C.; PHAM, T.; BAS, E.; UZUNBAS, M. G.; CARDONA, A.; SCHINDELIN, J.; SEUNG H. S.

RIV year

2016

Released

05.11.2015

Publisher

Frontiers Research Foundation

Location

Švýcarsko

ISBN

1662-5129

Periodical

Frontiers in Neuroanatomy

Volume

9

Number

142

State

Swiss Confederation

Pages from

1

Pages to

13

Pages count

13

URL

http://journal.frontiersin.org/Journal/10.3389/fnana.2015.00142/full

Full text in the Digital Library

http://hdl.handle.net/

BibTex

@article{BUT118015,
  author="ARGANDA-CARRERAS, I. and TURAGA, S. C. and BERGER, D. R. and CIRESAN, D. and GIUSTI, A. and GAMBARDELLA, L. M. and SCHMIDHUBER, J. and LAPTEV, D. and DWIVEDI, S. and BUHMANN, J. M. and LIU, T. and SEYEDHOSSEINI, M. and TASDIZEN, T. and KAMENTSKY, L. and BURGET, R. and UHER, V. and TAN, X. and SUN, C. and PHAM, T. and BAS, E. and UZUNBAS, M. G. and CARDONA, A. and SCHINDELIN, J. and SEUNG H. S.",
  title="Crowdsourcing the creation of image segmentation algorithms for connectomics",
  journal="Frontiers in Neuroanatomy",
  year="2015",
  volume="9",
  number="142",
  pages="1--13",
  doi="10.3389/fnana.2015.00142",
  issn="1662-5129",
  url="http://journal.frontiersin.org/Journal/10.3389/fnana.2015.00142/full"
}