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An Error Detection and Correction Framework for Connectomics

Author(s): Zung, Jonathan; Tartavull, Ignacio; Lee, Kisuk; Seung, H Sebastian

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Abstract: We define and study error detection and correction tasks that are useful for 3D reconstruction of neurons from electron microscopic imagery, and for image segmentation more generally. Both tasks take as input the raw image and a binary mask representing a candidate object. For the error detection task, the desired output is a map of split and merge errors in the object. For the error correction task, the desired output is the true object. We call this object mask pruning, because the candidate object mask is assumed to be a superset of the true object. We train multiscale 3D convolutional networks to perform both tasks. We find that the error-detecting net can achieve high accuracy. The accuracy of the error-correcting net is enhanced if its input object mask is “advice” (union of erroneous objects) from the error-detecting net.
Publication Date: 2017
Citation: Zung, Jonathan, Ignacio Tartavull, Kisuk Lee, and H. Sebastian Seung. "An Error Detection and Correction Framework for Connectomics." Advances in Neural Information Processing Systems 30 (2017), pp. 6819-6830.
ISSN: 1049-5258
Pages: 6819 - 6830
Type of Material: Conference Article
Journal/Proceeding Title: Advances in Neural Information Processing Systems
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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