# RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation

arXiv: https://arxiv.org/abs/2106.05487 · HTML: https://mlsanity.com/paper/2106.05487/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Learning When and Where to Zoom with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.00425/) (2020)
- [FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics](https://mlsanity.com/paper/1612.05360/) (2016)
- [PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing](https://mlsanity.com/paper/1912.07190/) (2019)
- [Guided Proofreading of Automatic Segmentations for Connectomics](https://mlsanity.com/paper/1704.00848/) (2017)
- [Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics](https://mlsanity.com/paper/1812.01157/) (2018)
- [An Error Detection and Correction Framework for Connectomics](https://mlsanity.com/paper/1708.02599/) (2017)
- [Actor-Critic Instance Segmentation](https://mlsanity.com/paper/1904.05126/) (2019)
- [VICE: Visual Identification and Correction of Neural Circuit Errors](https://mlsanity.com/paper/2105.06861/) (2021)

## Built on by (most cited first, 3 of 3)

- [ConnectomeBench: Can LLMs Proofread the Connectome?](https://mlsanity.com/paper/2511.05542/) (2025)
- [ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading](https://mlsanity.com/paper/2606.21116/) (2026)
- [A Systematic Study of Behavioral Cloning for Scientific Data Annotation](https://mlsanity.com/paper/2606.07568/) (2026)

## Abstract (arXiv)

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.