# Synaptic partner prediction from point annotations in insect brains

arXiv: https://arxiv.org/abs/1806.08205 · HTML: https://mlsanity.com/paper/1806.08205/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes](https://mlsanity.com/paper/2104.03577/) (2021)
- [Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models](https://mlsanity.com/paper/2404.13239/) (2024)
- [Domain Adaptive Synapse Detection with Weak Point Annotations](https://mlsanity.com/paper/2308.16461/) (2023)
- [Towards Generalized Synapse Detection Across Invertebrate Species](https://mlsanity.com/paper/2509.17041/) (2025)

## Abstract (arXiv)

High-throughput electron microscopy allows recording of lar- ge stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. Current efforts to automate this process focus mainly on the segmentation of neurons. However, in order to recover a wiring diagram, synaptic partners need to be identi- fied as well. This is especially challenging in insect brains like Drosophila melanogaster, where one presynaptic site is associated with multiple post- synaptic elements. Here we propose a 3D U-Net architecture to directly identify pairs of voxels that are pre- and postsynaptic to each other. To that end, we formulate the problem of synaptic partner identification as a classification problem on long-range edges between voxels to encode both the presence of a synaptic pair and its direction. This formulation allows us to directly learn from synaptic point annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and improve over the current state of the art, producing 3% fewer errors than the next best method.