  Pixel-accurate tracking of objects is a key element in many computer vision
applications, often solved by iterated individual object tracking or instance
segmentation followed by object matching. Here we introduce
cross-classification clustering (3C), a technique that simultaneously tracks
complex, interrelated objects in an image stack. The key idea in
cross-classification is to efficiently turn a clustering problem into a
classification problem by running a logarithmic number of independent
classifications per image, letting the cross-labeling of these classifications
uniquely classify each pixel to the object labels. We apply the 3C mechanism to
achieve state-of-the-art accuracy in connectomics -- the nanoscale mapping of
neural tissue from electron microscopy volumes. Our reconstruction system
increases scalability by an order of magnitude over existing single-object
tracking methods (such as flood-filling networks). This scalability is
important for the deployment of connectomics pipelines, since currently the
best performing techniques require computing infrastructures that are beyond
the reach of most laboratories. Our algorithm may offer benefits in other
domains that require pixel-accurate tracking of multiple objects, such as
segmentation of videos and medical imagery.
