RLCorrector：细胞级显微图像分割的强化校对
RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation
摘要：
在神经连接组学研究中，纳米级电子显微镜图像的分割是一个关键而又具有挑战性的问题。其中一个原因是，现有的分割方法都不是无错误的，因此它们需要校对，这通常是通过手动干预实现的交互式半自动过程。在此，我们提出了一种基于强化学习的全自动校对方法，该方法模仿人类对分割错误的检测、分类和纠正的决策过程。我们系统地设计了所提出的系统相结合的多个强化学习代理在一个层次的方式，其中每个代理只专注于一个特定的任务，同时保持代理之间的依赖关系。此外，我们证明了强化学习的情景任务设置可以有效地管理输入中同时存在的合并和拆分错误的组合。我们证明了该系统的有效性，通过比较它与传统的校对方法在各种测试情况下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
