  Segmentation of nanoscale electron microscopy (EM) images is crucial but
still challenging in connectomics research. One reason for this is that none of
the existing segmentation methods are error-free, so 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 that mimics the human decision process of
detection, classification, and correction of segmentation errors. 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
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 conventional proofreading methods over various testing cases.
