# An Error Detection and Correction Framework for Connectomics

arXiv: https://arxiv.org/abs/1708.02599 · HTML: https://mlsanity.com/paper/1708.02599/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2017.

## Built on by (most cited first, 5 of 5)

- [Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing](https://mlsanity.com/paper/2401.03043/) (2024)
- [RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation](https://mlsanity.com/paper/2106.05487/) (2021)
- [Bridging the Gap: Point Clouds for Merging Neurons in Connectomics](https://mlsanity.com/paper/2112.02039/) (2021)
- [Autoproof: Automated Segmentation Proofreading for Connectomics](https://mlsanity.com/paper/2509.26585/) (2025)
- [ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading](https://mlsanity.com/paper/2606.21116/) (2026)

## Abstract (arXiv)

We define and study error detection and correction tasks that are useful for 3D reconstruction of neurons from electron microscopic imagery, and for image segmentation more generally. Both tasks take as input the raw image and a binary mask representing a candidate object. For the error detection task, the desired output is a map of split and merge errors in the object. For the error correction task, the desired output is the true object. We call this object mask pruning, because the candidate object mask is assumed to be a superset of the true object. We train multiscale 3D convolutional networks to perform both tasks. We find that the error-detecting net can achieve high accuracy. The accuracy of the error-correcting net is enhanced if its input object mask is "advice" (union of erroneous objects) from the error-detecting net.