# An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes

arXiv: https://arxiv.org/abs/2302.00545 · HTML: https://mlsanity.com/paper/2302.00545/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Synaptic Partner Assignment Using Attentional Voxel Association Networks](https://mlsanity.com/paper/1904.09947/) (2019)
- [Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy](https://mlsanity.com/paper/1904.12966/) (2019)

## Built on by (most cited first, 2 of 2)

- [Domain Adaptive Synapse Detection with Weak Point Annotations](https://mlsanity.com/paper/2308.16461/) (2023)
- [TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images](https://mlsanity.com/paper/2401.13961/) (2024)

## Abstract (arXiv)

The size of image stacks in connectomics studies now reaches the terabyte and often petabyte scales with a great diversity of appearance across brain regions and samples. However, manual annotation of neural structures, e.g., synapses, is time-consuming, which leads to limited training data often smaller than 0.001% of the test data in size. Domain adaptation and generalization approaches were proposed to address similar issues for natural images, which were less evaluated on connectomics data due to a lack of out-of-domain benchmarks.