• Je něco špatně v tomto záznamu ?

Crowdsourcing the creation of image segmentation algorithms for connectomics

I. Arganda-Carreras, SC. Turaga, DR. Berger, D. Cireşan, A. Giusti, LM. Gambardella, J. Schmidhuber, D. Laptev, S. Dwivedi, JM. Buhmann, T. Liu, M. Seyedhosseini, T. Tasdizen, L. Kamentsky, R. Burget, V. Uher, X. Tan, C. Sun, TD. Pham, E. Bas,...

. 2015 ; 9 (-) : 142. [pub] 20151105

Jazyk angličtina Země Švýcarsko

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc16000884

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.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc16000884
003      
CZ-PrNML
005      
20160125114610.0
007      
ta
008      
160108s2015 sz f 000 0|eng||
009      
AR
024    7_
$a 10.3389/fnana.2015.00142 $2 doi
035    __
$a (PubMed)26594156
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a sz
100    1_
$a Arganda-Carreras, Ignacio $u UMR1318 French National Institute for Agricultural Research-AgroParisTech, French National Institute for Agricultural Research Centre de Versailles-Grignon, Institut Jean-Pierre Bourgin Versailles, France. $7 gn_A_00008322
245    10
$a Crowdsourcing the creation of image segmentation algorithms for connectomics / $c I. Arganda-Carreras, SC. Turaga, DR. Berger, D. Cireşan, A. Giusti, LM. Gambardella, J. Schmidhuber, D. Laptev, S. Dwivedi, JM. Buhmann, T. Liu, M. Seyedhosseini, T. Tasdizen, L. Kamentsky, R. Burget, V. Uher, X. Tan, C. Sun, TD. Pham, E. Bas, MG. Uzunbas, A. Cardona, J. Schindelin, HS. Seung,
520    9_
$a 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.
655    _2
$a časopisecké články $7 D016428
700    1_
$a Turaga, Srinivas C $u Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA.
700    1_
$a Berger, Daniel R $u Center for Brain Science, Harvard University Cambridge, MA, USA.
700    1_
$a Cireşan, Dan $u Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland.
700    1_
$a Giusti, Alessandro $u Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland.
700    1_
$a Gambardella, Luca M $u Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland.
700    1_
$a Schmidhuber, Jürgen $u Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland.
700    1_
$a Laptev, Dmitry $u Department of Computer Science, ETH Zurich Zurich, Switzerland.
700    1_
$a Dwivedi, Sarvesh $u Department of Computer Science, ETH Zurich Zurich, Switzerland.
700    1_
$a Buhmann, Joachim M $u Department of Computer Science, ETH Zurich Zurich, Switzerland.
700    1_
$a Liu, Ting $u Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA.
700    1_
$a Seyedhosseini, Mojtaba $u Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA.
700    1_
$a Tasdizen, Tolga $u Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA.
700    1_
$a Kamentsky, Lee $u Imaging Platform, Broad Institute Cambridge, MA, USA.
700    1_
$a Burget, Radim $u Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology Brno, Czech Republic.
700    1_
$a Uher, Vaclav $u Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology Brno, Czech Republic.
700    1_
$a Tan, Xiao $u School of Engineering and Information Technology, University of New South Wales Canberra, ACT, Australia.
700    1_
$a Sun, Changming $u Digital Productivity Flagship, Commonwealth Scientific and Industrial Research Organisation North Ryde, NSW, Australia.
700    1_
$a Pham, Tuan D $u Department of Biomedical Engineering, The Institute of Technology, Linkoping University Linkoping, Sweden.
700    1_
$a Bas, Erhan $u Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA.
700    1_
$a Uzunbas, Mustafa G $u Computer Science Department, Rutgers University New Brunswick, NJ, USA.
700    1_
$a Cardona, Albert $u Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA.
700    1_
$a Schindelin, Johannes $u Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison Madison, WI, USA.
700    1_
$a Seung, H Sebastian $u Princeton Neuroscience Institute and Computer Science Department, Princeton University Princeton, NJ, USA.
773    0_
$w MED00174548 $t Frontiers in neuroanatomy $x 1662-5129 $g Roč. 9, č. - (2015), s. 142
856    41
$u https://pubmed.ncbi.nlm.nih.gov/26594156 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y a $z 0
990    __
$a 20160108 $b ABA008
991    __
$a 20160125114732 $b ABA008
999    __
$a ind $b bmc $g 1103165 $s 925090
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2015 $b 9 $c - $d 142 $e 20151105 $i 1662-5129 $m Frontiers in neuroanatomy $n Front. neuroanat. $x MED00174548
LZP    __
$a Pubmed-20160108

Najít záznam

Citační ukazatele

Možnosti archivace