Machines that learn to segment images: A crucial technology for connectomics

Viren Jain, Hyunjune Sebastian Seung, Srinivas C. Turaga

Research output: Contribution to journalReview articlepeer-review

94 Scopus citations

Abstract

Connections between neurons can be found by checking whether synapses exist at points of contact, which in turn are determined by neural shapes. Finding these shapes is a special case of image segmentation, which is laborious for humans and would ideally be performed by computers. New metrics properly quantify the performance of a computer algorithm using its disagreement with 'true' segmentations of example images. New machine learning methods search for segmentation algorithms that minimize such metrics. These advances have reduced computer errors dramatically. It should now be faster for a human to correct the remaining errors than to segment an image manually. Further reductions in human effort are expected, and crucial for finding connectomes more complex than that of Caenorhabditis elegans.

Original languageEnglish (US)
Pages (from-to)653-666
Number of pages14
JournalCurrent Opinion in Neurobiology
Volume20
Issue number5
DOIs
StatePublished - Oct 2010

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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