Cross-Classification Clustering: An Efficient Multi-Object Tracking Techniquefor 3-D Instance Segmentation in Connectomics
Abstract
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 crossclassification 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.