# Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

arXiv: https://arxiv.org/abs/1904.12966 · HTML: https://mlsanity.com/paper/1904.12966/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

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

- [AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions](https://mlsanity.com/paper/2107.05451/) (2021)
- [CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy](https://mlsanity.com/paper/2101.04266/) (2021)
- [Efficient Connectivity-Preserving Instance Segmentation with Supervoxel-Based Loss Function](https://mlsanity.com/paper/2501.01022/) (2025)
- [An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes](https://mlsanity.com/paper/2302.00545/) (2023)
- [TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images](https://mlsanity.com/paper/2401.13961/) (2024)
- [Large-scale image segmentation based on distributed clustering algorithms](https://mlsanity.com/paper/2106.10795/) (2021)

## Abstract (arXiv)

Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes.