# Domain Adaptive Synapse Detection with Weak Point Annotations

arXiv: https://arxiv.org/abs/2308.16461 · HTML: https://mlsanity.com/paper/2308.16461/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Superhuman Accuracy on the SNEMI3D Connectomics Challenge](https://mlsanity.com/paper/1706.00120/) (2017)
- [Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain](https://mlsanity.com/paper/1805.02718/) (2018)
- [Advanced Deep Networks for 3D Mitochondria Instance Segmentation](https://mlsanity.com/paper/2104.07961/) (2021)
- [Synaptic Partner Assignment Using Attentional Voxel Association Networks](https://mlsanity.com/paper/1904.09947/) (2019)
- [Fully-Automatic Synapse Prediction and Validation on a Large Data Set](https://mlsanity.com/paper/1604.03075/) (2016)
- [Synaptic partner prediction from point annotations in insect brains](https://mlsanity.com/paper/1806.08205/) (2018)
- [An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes](https://mlsanity.com/paper/2302.00545/) (2023)
- [Identifying Synapses Using Deep and Wide Multiscale Recursive Networks](https://mlsanity.com/paper/1409.1789/) (2014)
- [Annotating Synapses in Large EM Datasets](https://mlsanity.com/paper/1409.1801/) (2014)
- [Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets](https://mlsanity.com/paper/1807.02739/) (2018)

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

- [Dual form Complementary Masking for Domain-Adaptive Image Segmentation](https://mlsanity.com/paper/2507.12008/) (2025)
- [Towards Generalized Synapse Detection Across Invertebrate Species](https://mlsanity.com/paper/2509.17041/) (2025)

## Abstract (arXiv)

The development of learning-based methods has greatly improved the detection of synapses from electron microscopy (EM) images. However, training a model for each dataset is time-consuming and requires extensive annotations. Additionally, it is difficult to apply a learned model to data from different brain regions due to variations in data distributions. In this paper, we present AdaSyn, a two-stage segmentation-based framework for domain adaptive synapse detection with weak point annotations. In the first stage, we address the detection problem by utilizing a segmentation-based pipeline to obtain synaptic instance masks. In the second stage, we improve model generalizability on target data by regenerating square masks to get high-quality pseudo labels. Benefiting from our high-accuracy detection results, we introduce the distance nearest principle to match paired pre-synapses and post-synapses. In the WASPSYN challenge at ISBI 2023, our method ranks the 1st place.