# Guided Proofreading of Automatic Segmentations for Connectomics

arXiv: https://arxiv.org/abs/1704.00848 · HTML: https://mlsanity.com/paper/1704.00848/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2017.

## Built on by (most cited first, 6 of 6)

- [Topology-Aware Uncertainty for Image Segmentation](https://mlsanity.com/paper/2306.05671/) (2023)
- [RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation](https://mlsanity.com/paper/2106.05487/) (2021)
- [ConnectomeBench: Can LLMs Proofread the Connectome?](https://mlsanity.com/paper/2511.05542/) (2025)
- [VICE: Visual Identification and Correction of Neural Circuit Errors](https://mlsanity.com/paper/2105.06861/) (2021)
- [ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading](https://mlsanity.com/paper/2606.21116/) (2026)
- [Benchmarking Vision-Language Models on Synapse Detection and Proofreading in Connectomics](https://mlsanity.com/paper/2609.36492/) (2026)

## Abstract (arXiv)

Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual search for these errors as the bottleneck in interactive proofreading. To aid error correction, we develop two classifiers that automatically recommend candidate merges and splits to the user. These classifiers use a convolutional neural network (CNN) that has been trained with errors in automatic segmentations against expert-labeled ground truth. Our classifiers detect potentially-erroneous regions by considering a large context region around a segmentation boundary. Corrections can then be performed by a user with yes/no decisions, which reduces variation of information 7.5x faster than previous proofreading methods. We also present a fully-automatic mode that uses a probability threshold to make merge/split decisions. Extensive experiments using the automatic approach and comparing performance of novice and expert users demonstrate that our method performs favorably against state-of-the-art proofreading methods on different connectomics datasets.