RLCorrector: Reinforced Proofreading for Connectomics Image Segmentation

• URL: http://arxiv.org/abs/2106.05487v1
• Date: Thu, 10 Jun 2021 04:02:41 GMT
• Title: RLCorrector: Reinforced Proofreading for Connectomics Image Segmentation
• Authors: Khoa Tuan Nguyen, Ganghee Jang and Won-ki Jeong
• Abstract summary: 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. We demonstrate the efficacy of the proposed system by comparing it with state-of-the-art proofreading methods.
• Score: 3.21359455541169
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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