Superhuman Accuracy on the SNEMI3D Connectomics Challenge

For the past decade, convolutional networks have been used for 3D reconstruction
of neurons from electron microscopic (EM) brain images. Recent years have seen
great improvements in accuracy, as evidenced by submissions to the SNEMI3D
benchmark challenge. Here we report the first submission to surpass the estimate
of human accuracy provided by the SNEMI3D leaderboard. A variant of 3D UNet
is trained on a primary task of predicting affinities between nearest neighbor
voxels, and an auxiliary task of predicting long-range affinities. The training data is
augmented by simulated image defects. The nearest neighbor affinities are used to
create an oversegmentation, and then supervoxels are greedily agglomerated based
on mean affinity. The resulting SNEMI3D score exceeds the estimate of human
accuracy by a large margin. While one should be cautious about extrapolating
from the SNEMI3D benchmark to real-world accuracy of large-scale neural circuit
reconstruction, our submission inspires optimism that the goal of full automation
may be realizable in the future.

• Machine Perception

• Machine Intelligence