Abstract: The segmentation of nanoscale electron microscopy (EM) images is crucial but
challenging in connectomics. Recent advances in deep learning have demonstrated
the significant potential of automatic segmentation for tera-scale EM images.
However, none of the existing segmentation methods are error-free, and they
require proofreading, which is typically implemented as an interactive,
semi-automatic process via manual intervention. Herein, we propose a fully
automatic proofreading method based on reinforcement learning. The main idea is
to model the human decision process in proofreading using a reinforcement agent
to achieve fully automatic proofreading. We systematically design the proposed
system by combining multiple reinforcement learning agents in a hierarchical
manner, where each agent focuses only on a specific task while preserving
dependency between agents. Furthermore, we also demonstrate that the episodic
task setting of reinforcement learning can efficiently manage a combination of
merge and split errors concurrently presented in the input. We demonstrate the
efficacy of the proposed system by comparing it with state-of-the-art
proofreading methods using various testing examples.