Abstract
Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified
the visual search for these errors as the bottleneck in interactive proofreading. To aid error correction, we develop two
classifiers that automatically recommend candidate merges
and splits to the user. These classifiers use a convolutional
neural network (CNN) that has been trained with errors
in automatic segmentations against expert-labeled ground
truth. Our classifiers detect potentially-erroneous regions
by considering a large context region around a segmentation boundary. Corrections can then be performed by a
user with yes/no decisions, which reduces variation of information 7.5× faster than previous proofreading methods.
We also present a fully-automatic mode that uses a probability threshold to make merge/split decisions. Extensive
experiments using the automatic approach and comparing
performance of novice and expert users demonstrate that our
method performs favorably against state-of-the-art proofreading methods on different connectomics datasets