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Crowdsourcing the creation of image segmentation algorithms for connectomics.

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.

SUBMITTER: Arganda-Carreras I

PROVIDER: S-EPMC4633678 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Crowdsourcing the creation of image segmentation algorithms for connectomics.

Arganda-Carreras Ignacio I Turaga Srinivas C SC Berger Daniel R DR Cireşan Dan D Giusti Alessandro A Gambardella Luca M LM Schmidhuber Jürgen J Laptev Dmitry D Dwivedi Sarvesh S Buhmann Joachim M JM Liu Ting T Seyedhosseini Mojtaba M Tasdizen Tolga T Kamentsky Lee L Burget Radim R Uher Vaclav V Tan Xiao X Sun Changming C Pham Tuan D TD Bas Erhan E Uzunbas Mustafa G MG Cardona Albert A Schindelin Johannes J Seung H Sebastian HS

Frontiers in neuroanatomy 20151105

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 accepte ...[more]

PMID: 26594156

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Dataset Information

Crowdsourcing the creation of image segmentation algorithms for connectomics.

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Crowdsourcing the creation of image segmentation algorithms for connectomics.

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