Machines that learn to segment images: a crucial technology for connectomics.
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.
Jain
Viren
V
Howard Hughes Medical Institute and Brain & Cog. Sci. Dept., Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Seung
H Sebastian
HS
Turaga
Srinivas C
SC
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods / Q000639:trends*; D008854:Microscopy, Electron / Q000295:instrumentation / Q000379:methods / Q000639:trends; D036103:Nanotechnology / Q000295:instrumentation / Q000379:methods / Q000639:trends*; D009446:Neurobiology / Q000295:instrumentation / Q000379:methods / Q000639:trends*
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