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.

翻译：纳米电子显微镜(EM)图像的分解至关重要,但在连接缩微镜(EM)中具有挑战性。 最近的深层次学习进展表明,对于梯度的EM图像,自动分解具有巨大的潜力。 但是,现有的分解方法没有一个是无误的,它们需要校对,通常通过人工干预作为互动的半自动程序加以实施。在这里,我们建议了一种基于强化学习的完全自动校对方法。主要想法是模拟人类在校对过程中的决策过程,使用增强剂实现完全的自动校对。我们系统地设计了拟议的系统,将多个强化学习剂以等级方式结合起来,其中每个代理只注重特定任务,同时保持代理之间的依赖性。此外,我们还表明,强化学习的成形任务设置能够有效地管理输入中同时出现的合并和分裂错误的组合。我们通过使用各种测试实例将其与最先进的校对方法进行比较,来显示拟议系统的有效性。